Operation Tame Finance

Gary gensler’s students at mit Sloan were an appreciative bunch. Their nominations secured him the business school’s “Outstanding Teacher” award for the 2018-19 academic year. Now that he is the chairman of the Securities and Exchange Commission (sec), America’s main markets watchdog, his constituents are rather more unruly. Finance has been upended by an explosion of raucous innovation, and Mr Gensler has to work out how, and to what extent, to police it all. Forget diligent undergraduates; it is rather like trying to run the world’s largest, noisiest kindergarten.

The drive for more adult supervision is already under way in crypto. The sec recently threatened to sue Coinbase, a large cryptocurrency exchange, if it launches a lending product without first registering it as a security. And this week the regulator extracted $539m from three media firms charged with illegal offerings of stocks and digital assets.

Crypto-believers may have expected a friendlier stance from a man whose courses at mit included one on the uses of blockchain technology. But since taking the sec’s reins Mr Gensler has been at pains to point out that, while he is “neutral” on technology, he is anything but when it comes to investor protection and market stability. And that means beefing up regulation of the $2.2trn crypto market, which, he told a Senate committee this week, is a “Wild West…rife with fraud, scams and abuse”.

His agenda stretches beyond the seething cryptoverse. He is also warily eyeing other newfangled corners of finance, from trading apps like Robinhood that use “digital engagement practices” to encourage retail punters to trade more often, to special-purpose acquisition companies (spacs) that push the envelope of what securities laws allow (an early victim was spac-king Bill Ackman’s complex plan to invest in Universal Music Group). Other targets include the kinds of derivatives that blew up Archegos, a family office, and the shell-company structures used by many Chinese firms that list in America.

For all the focus on finance’s cutting edge, Mr Gensler’s sec may end up having just as big an impact on more established markets. He thinks stock trading needs an overhaul; too much flows to “dark”, off-exchange venues, where small investors can more easily be stiffed. They may also, he suspects, be short-changed by potential conflicts of interest such as the “payment for order flow” that brokers get for routing trades to particular marketmakers. He wants to force corporate disclosure of everything from climate risks to how firms treat their workers.

Quite a to-do list, then; policy reviews are under way in at least 50 areas. And quite a change from President Donald Trump’s era, when the commission seemed happy to drag its feet on implementing post-financial-crisis reforms.

The obvious question is whether Mr Gensler is biting off more than he can chew. His background, equal parts poacher and gamekeeper, should help him. After 18 years at Goldman Sachs, the last ten as a partner, he worked in the Treasury and helped write the Sarbanes-Oxley reforms after the implosion of Enron, an energy firm, in 2001. As head of the Commodity Futures Trading Commission (cftc), which regulates derivatives, he saw off an attack from the giant over-the-counter swaps industry, forcing it onto more highly regulated platforms.

Being a good communicator should also help. Mr Gensler understands that winning the argument means boiling the message down to simple analogies that most punters (and senators) can grasp. Under him, the sec is even using social media to good effect. When the boss of Coinbase professed shock that a lending product could be classed as a security, the commission archly tweeted a 30-second guide to how bonds work.

Good one. But Mr Gensler can expect fierce lobbying against more red tape. He may also have to fight turf wars with other regulators; the cftc wants a piece of the action in digital currencies. And then there are the politicians. Regulation-friendly Democrats have the upper hand in Congress but some people are queasy about a big expansion of the sec’s authority, given its patchy record: think of all the scandals, from Enron to Bernie Madoff, unearthed not by the regulator but by outside sleuths. Mr Gensler also needs more money. At $2bn, his budget is smaller than JPMorgan Chase’s annual spending on marketing. But the increase pencilled in for 2022 is just 5%. Mr Gensler has big ambitions. His problem may be finding the big bucks to realise them.

Culled from the economist

Here are 4 technology trends from emerging economies

  • Emerging economies are experimenting with new technologies and shaping the regulatory landscape, but digital access is still highly unequal.
  • Large-scale investment in infrastructure and innovation is crucial for building an inclusive digital future.
  • COVID-19 has highlighted the need for international collaboration and a joint approach to global challenges, tapping the power of the Fourth Industrial Revolution.

Since the start of the COVID-19 pandemic, our lives have shifted further into the digital realm. For many of us, it is hard to imagine what life would have been like this year without the ability to learn, work and socialize online. But as life-saving as new technologies have been during the pandemic, they also pose a risk. Unequal access to them may worsen the gap between rich and poor. Concerns over safety and privacy have grown in parallel with the widespread adoption of digital tools.

In emerging economies, the promise of these technologies shines particularly bright. They offer the hope of skipping entire stages of conventional development, spreading knowledge and prosperity faster than ever before. Emerging economies are also experimenting with new technologies and finding alternative ways of regulating them, offering inspiring perspectives that influence the rest of the world. Here are four major digital trends happening in emerging economies that could shape lives all over the world for generations to come:

Boosting digital infrastructure

Internet usage has soared during the global pandemic. In the months since the start of the outbreak, internet usage has increased by 70%, use of communication apps has risen by 300%, and video streaming services have grown almost 20-fold. However, not everyone has been able to switch to the digital world when physical spaces were shut down. After all, only 53% of the global population has internet access.

Without massive investment in infrastructure, this digital divide will continue to widen. Less developed regions risk being left behind. Of the 25 least connected countries in the world, 21 are in Africa. A lack of digital access in turn hampers growth. Many small and medium-sized enterprises, which make up between 40 – 90% of GDP across emerging economies, are struggling for survival because they have not been able to digitize their services quickly enough or at all.

To help close this gap, the International Telecommunication Union (ITU), the World Bank and other multilateral and regional bodies are encouraging investment in digital capabilities across the developing world. The World Bank’s Digital Moonshot Initiative, a $25 billion USD fund to support the African Union’s Digital Transformation Agenda 2030, is a prime example of such a vital investment. Its aim is to digitally connect every individual, business and government in Africa by 2030. This would allow Africans to not only access the services and opportunities the Internet offers, but also transform the digital space with their own innovations.

Image: World Economic Forum

Digital tools for education and reskilling

This year, schools and universities all over the world have been forced to teach their students online. In China alone, around 200 million school-aged children pursued their studies virtually during the height of the pandemic. This is out of approximately 1.3 billion school-aged children globally. Unsurprisingly, most of these digitally connected children live in advanced economies. Even in those economies, many children from poorer communities found their education severely disrupted due to a lack of computers or Internet access at home.

The challenge of online learning has fostered creative solutions in emerging economies. Nigerian start-up uLesson is helping bridge network connectivity gaps and addressing high data costs by offering a non-streaming option for their pre-recorded secondary school education content. In Latin America, Peruvian start-up Crehana, an on-demand learning platform for creative and digital professionals, has reported 40% growth in user numbers since the start of the pandemic by offering free daily online courses to learn new skills during the lockdown.

Closing the educational gap also means reskilling existing workers. There are some concerns that the growing use of technology and automation could push humans out of the workplace and increase unemployment. By 2025, more than half of all work tasks are expected to be performed by machines. However, this risk has to be balanced against the opportunities created by new technologies, especially in areas where they facilitate enormous developmental leaps, lift the skill level across the entire workforce and boost local innovation.

Leapfrogging and innovating

The Fourth Industrial Revolution, which merges the digital and physical worlds, has empowered emerging economies to experiment with new technologies in creative and unconventional ways. This has allowed them to leapfrog over conventional stages of development and set their own priorities for innovation. Often, these homegrown innovations are then adopted regionally and internationally.

For example, Rwanda has developed the largest civil-operated drones network in the world. Since 2016, it has embraced the use of commercial drones to deliver vital medicines and blood for transfusions all over the country, even to rural communities in areas with little infrastructure. It also uses drones to deliver health supplies to neighbouring countries. The strategy has been so successful that other African countries have started using drones in their fight against COVID-19. Similarly, these successes have also been scaled in advanced economies with Chinese retailer JD.com using drones to support COVID-19 contact tracing and delivery efforts in rural China. In April 2020, the US Postal Service collaborated with drone developer Matternet to become the first approved drone prescription delivery service in the US.

This growing trend of African innovation is strengthening the continent’s profile as a hub of local ideas with global potential. It builds on success stories such as M-PESA, a mobile money transfer service in Kenya, which is credited with sparking the boom of mobile money in Africa. Services like this drove the move to a greater use of phone-based services from payments to medical advice all over the world.

Innovation flourishes when we are able to embrace our differences, individuality, and ability to think in unique ways. However, to fully realize the potential for research and development in emerging markets, more investment is needed. This is where an enormous gap still exists between different regions.

In 2019, the total annual investment in technology start-ups came to $258 billion in China, $130 billion in the US and $122 billion in Europe. India lagged far behind with a total tech investment of $14.5 billion that year. Africa was even further behind, with an investment of $1.3 billion across the entire continent. Such vastly different funding pools will widen the digital divide rather than close it.

Culled from World Economic Forum

Going public? Here is a how-to guide

As flotations boom, we look at what is changing at a key moment in capitalism

“A FLOTATION is like your own funeral. You usually do it only once,” deadpans the chief financial officer of a software company that recently staged a blockbuster initial public offering (IPO). Some compare a listing to a wedding, requiring much frantic preparation and ending with a big celebration and bell-ringing. Others liken it to an 18th birthday, marking the moment a young company is launched into the harsh realities of adult life.

Whichever metaphor you choose, going public combines mixed emotions, much complexity and myriad idiosyncracies. Despite that, and undeterred by recent wobbles in equity markets, startups have been listing in droves. So far this year tech firms have raised $60bn, according to Dealogic, a data provider, more than at the height of the dotcom bubble in 2000. Include all types of business and the figure is close to $250bn (see chart 1). One headhunting agency is said to have more than 50 searches under way for finance chiefs at startups hoping to go public soon.

This week alone will see a handful of blockbuster flotations. They include Amplitude, a data-analytics company most recently valued at $4bn, Olaplex, a hair-care-products firm seeking a valuation of $10bn, and Warby Parker, a maker of spectacles popular among hipsters that could be worth nearly $3bn. Investors can’t get enough of the fresh blood. Despite a sharp drop in the first half of the year, recently listed firms are back in favour, and have handily outperformed the stockmarket as a whole since the start of 2020 (see chart 2).

Besides being more numerous than earlier cohorts, the current generation of floaters enjoy greater choice in how to go about it. Holders of stakes in Amplitude and Warby Parker will sell their shares directly to public investors without raising fresh capital, as is the case in an IPO. Last year a record number of companies listed via reverse mergers with special-purpose acquisition companies (SPACs). Even the classic IPO is getting a reboot.

To make sense of it all, we spoke to bosses and chief financial officers of companies that have recently listed or are about to, as well as venture capitalists, bankers and brokers, most of whom spoke on the condition of anonymity. The result is a rough-and-ready guide to everything that is new in what one chief executive dubs the “key moment in capitalism”.

Party poppers

A conventional listing goes something like this. Banks distribute newly created shares, on average 10% of a firm’s total, to public investors, and pocket 7% of the money raised as fees. Though this should incentivise them to price the shares highly, the bankers also work for the buyers, who are often their long-term institutional clients rather than one-off customers like the listing startup. Pleasing those regulars often means setting a lower price. That in turn all but ensures a share-price “pop” on the first day of trading, generating a quick profit for the public investors at the expense of the private ones. In the past decade the pop averaged 21%, according to an analysis by Jay Ritter of the University of Florida. And the first-day surge can be much bigger. Snowflake, a cloud-based data platform which went public last year, popped by 112%, adding nearly $40bn to its market value. As a result, its private investors may have left nearly $4bn on the table.

The good news for startup bosses, their early backers and staff, who are often paid in stock, is that banks’ power over the process is waning. Faced with alternatives such as SPACs and direct listings, the bankers have become more flexible with the terms they are willing to accept, at least for bigger, high-quality deals aiming to raise $500m or more. The 7% is now negotiable. Strict 180-day lock-ups, which bar pre-IPO investors from selling their shares too soon, have given way to staggered ones. Employees of Coursera, an online-education platform which went public in March, were allowed to sell 25% of their holdings 41 days after the IPO. Management could do the same, but only if the share price stayed at least 33% above the IPO price for 10-15 trading days.

That makes the IPO look a bit more like a direct listing, which by definition has no lock-ups. Direct listings, meanwhile, are becoming more like IPOs. Last December the Securities and Exchange Commission (SEC) approved a rule change that allowed companies listing directly on the New York Stock Exchange (NYSE) to raise capital—something that had been prohibited. In May the markets regulator waved through a similar change for the tech-heavy Nasdaq exchange.

For the time being, startups eyeing direct listings simply raise money ahead of the flotations, as Databricks, a data-management firm eyeing a listing, has done in two rounds this year that brought in $2.67bn. But the ability to raise new capital may in time make direct listings appealing to companies with less cash than the tech darlings that have taken the direct route, like Spotify (in music-streaming) or Slack (office-messaging).

Then there are the SPACs. These have been around for decades, as has their reputation for dodginess (born of laxer requirements than the more traditional avenues to public markets). After a frenzy in late 2020 and earlier this year, this reputation may have caught up with them. Having raised around $100bn between January and March, the SPAC fever has broken. According to one reckoning, new SPACs that had merged with their target by mid-February have lost a quarter of their combined market capitalisation since then, wiping out $75bn in shareholder value.

Still, there may be room for SPACs in the pool of flotation options, especially now that regulators and investors alike are waking up to the iffiness. The SEC is taking a closer look at the practice, fearing that SPACs mostly benefit the vehicles’ founders (who customarily get 20% of a SPACs shares as a fee, or “promote”), their bankers and lawyers. This month its advisory panel recommended that SPACs disclose more information about things like promoters’ financial incentives and conflicts of interest, merger due diligence and risks. In August the SEC objected to one novel SPAC format proposed by Bill Ackman, a hedge-fund billionaire, because it looked too much like an investment fund.

Closer scrutiny should help clean up the industry. And even before any new rules are enacted, many SPACs are already offering more generous terms as they hunt for promising startups to merge with, which they must do within two years. Some SPAC sponsors are accepting lower “promotes” than the customary 20%. In one SPAC last year Mr Ackman forwent the promote altogether and settled for warrants that allow him to buy shares in the merged entity.

SPACs’ sponsors are also sticking around for longer rather than flipping shares quickly, which gives them a reason to nurture longer-term success. In the record $40bn SPAC deal involving Grab, South-East Asia’s biggest super-app, due to be completed this year, founders of the shell company, Altimeter Growth, vowed to hold on to their shares for at least three years, rather than the customary 12 months.

Other parts of the listing process look a bit more familiar. A CEO must find a trusted finance chief, and IPO-hardened ones remain a scarce commodity. Startups also continue to rely on investment bankers to take on legal liability, provide underwriting (as “stabilisation agents” vowing to support the share price should it tank) and act as a marketing department for the listing. Chief executives should still try to talk to the more taciturn members of the sales team pitching a bank’s offer (they do more work than the talkative types) and forge close relations with brokers that will follow their firms’ public fate (as the saying goes, “you date the banker but marry the analyst”). And firms in Silicon Valley still have only three real choices for the two “lead” banks: Goldman Sachs, JPMorgan Chase and Morgan Stanley. If a tech startup picks some other bank as the lead, investors will wonder what is wrong with its offering.

Bankers beware

But here, too, change is afoot. Improved access to information and investors lets bosses play the big three banks, and the ten or so others in the prospectus that provide additional distribution of shares and analyst coverage, off against each other. Banks are responding by throwing in ever more extra sweeteners, such as offering to manage a founder’s future wealth, or loans in exchange for collateral in the form of privately held stakes. Some startups that make business technology, like SimilarWeb, which provides tools to analyse website traffic, require that banks which want to vie for the contract purchase their wares.

Once the syndicate is in place, it is time to sell a story. This has grown in importance as technology offered by startups has become more complex and their business models more unusual. Few companies these days leave the prospectus entirely to the bankers. The middlemen can deal with the financial disclosures and other legal boilerplate. But the opening letter to shareholders is virtually always written by the founder CEO. “It helps clarify the essence of what you do as a company,” says Daniel Dines, the boss of UiPath, which sells automation software and raised $1.3bn in an IPO in April that valued it at $29bn.

Nowadays many companies file their prospectus, or s-1 in SEC-speak, confidentially, allowing them to modify the document in response to queries by the regulator without the embarrassment of having to refile it publicly. The “road shows” that make up the other part of the sales pitch have also become more of a back-and-forth process. Some firms start meeting investors before they file their s-1. After the filing, they do another round of meetings to hone the presentation and the accompanying pitch deck. Only then comes the road show proper, which gets cracking after the s-1 is made public.

As a result of the pandemic this arduous process involves fewer actual roads. Investor presentations have mostly gone virtual, sparing bosses visits to a dozen cities in ten days, including a handful overseas. And the tedium of talking on Zoom for hours on end is at least now punctuated by instant gratification. After each presentation investors put in their bids, which pop up instantly in an app provided by the banks. These enable all manner of fancy analytics, including drawing demand curves for an offering.

Nevertheless, actual share allocation and pricing still requires “man-to-man combat”, in the words of a (female) banker. If a bank senses no pushback, the client startup will find many hedge funds on the investor list. Most startups do try to push back, however, demanding that all their future shareholders are long-term and blue chip. CrowdStrike, a cyber-security firm which went public in 2019, had confronted its bankers with a spreadsheet of some 400 investors that management had already vetted. Some firms are offering shares to their users. In its IPO Uber set aside 3% of its stock for drivers. Its ride-hailing rival, Lyft, did something similar. In July Robinhood, a day-trading app, reserved up to a third of shares in its IPO for its users.

Once the price is set and the allocations decided, the last task for the exhausted boss is to ring the bell on the opening day of trading. Besides being the culmination of a protracted process it also remains a marvellous marketing opportunity. So when the bell chimes on the NYSE or the Nasdaq, bosses should smile, wave and watch traders spring into action to start delivering their wildest capitalist dream.

Culled From The Economist

5 Ways AI is Transforming the Finance Industry

As global technology has evolved over the years, we have moved from television to the internet, and today we are smoothly and gradually adapting Artificial Intelligence. The term AI was first coined by John McCarthy in 1956. It involves a lot of the main things ranging from process automation of robotics to the actual process of robotics. It has become highly popular among large enterprises today owing to the amount of data these companies are dealing in. Increase in the demand for understanding the data patterns has led to the growth in demand of AI. AI processes are much more efficient in identifying data patterns than humans which is beneficial for companies to understand their target audience and gain insight. Thousands of companies all around the world are looking at AI as the next big thing for the finance industry.

AI basically is of two types –

  1.    Weak AI
  2.    Strong AI

Weak AI

Weak AI, which can also be described as Narrow AI is the system which is set up only to fulfill or accomplish a particular task. It is designed in a way which can help solve specific problems. Weak AI works according to the rules that are set and is bound by it. It does not go beyond the rules set. It focuses on only the narrow tasks and does the best job at it. Weak AI just like humans has the capability of all cognitive functions and is not distinct from the human mind. Though it cannot be defined as general intelligence, rather it is designed to act intelligently towards completing the narrow tasks that are assigned to it.

The best example of weak AI is Apple’s Siri which is governed by the substantial database of the internet. It appears very intelligent, primarily because of the conversations that it is able to hold with humans. Siri also is known for its witty remarks, but in actuality, it operates in a predefined manner. The “narrowness” is witnessed when the system engages in conversations that it is not designed to respond correctly to.

Same is the case of robots which are used in manufacturing companies. They respond very aptly when asked questions by the customers and clients. The responses are accurate and sometimes very witty too. The AI is capable of managing situations which are extremely complex in nature. But their intelligence level is restricted to providing solutions to problems that the system is programmed for, anything beyond that cannot be accomplished by it.

Strong AI

Strong AI, also known as full AI has much bigger prospects than the Weak AI. It is the artificial intelligence that has huge capabilities and functionality. It can broadly mimic the human brain. It is so powerful that the actions performed by the system are exactly similar to the actions and decisions of a human being. It also has the understanding power and consciousness.

Strong AI is actually a process which can be entirely equated to the human mind. Artificial Intelligence in every sense functions like the human mind with the extraordinary capability to understand everything that can be understood by it. The beliefs, cognitive states and perception which can be only found in humans are programmed in strong AI.

However, the difficulty lies in defining intelligence accurately. It is almost impossible or highly difficult to determine success or set boundaries to intelligence as far as strong AI is concerned. Hence weak AI is more preferable, primarily because of its ability to accomplish mainly assigned tasks with optimum efficiency. Weak AI does not fully encompass intelligence rather it focuses on completing a particular task it is assigned to complete. Hence it can be broken down into smaller processes.

Artificial Intelligence is the intelligence which is shown by machines and not humans. Devices using their cognitive functions identify and solve problems just like humans or in most cases solve better. It has successfully managed to create a significant impact by doing what is thought as impossible.

However, it is the finance industry which is claimed to have benefitted the most with the help of Artificial Intelligence. Cognitive computingChatbots, Personal Assistant, Machine Learning are all peripherals of AI used in the finance industry extensively nowadays. Some financial organizations have been investing significantly in AI for years now, and much many are now willing to invest in AI.

AI is predicted to replace humans in the near future as companies start looking for features such as machine learning, personal assistants/advisors or digital labor. Owing to the big data, cloud services, and hyper processing systems, AI has gained popularity. But the greatest challenges faced are lack of trust, biases and majorly regulatory concern. Hence, companies today prefer a reliable option in the form of Augmented Intelligence which is designed to assist humans.

Artificial Intelligence can be used abundantly in processes which involve auditing of financial transactions. Also when it comes to analyzing an enormous number of pages of the tax changes, AI can be of great help. It can be expected in the near future to see companies relying on AI to make significant firm related decisions. AI also has the capability to identify how customers are going to react to various situations and problems. Artificial Intelligence is going to help people and firms make smarter decisions at a very quick pace. But the key here is to find the right balance between humans and machines.


1. Risk Assessment:

Since the very basis of AI is learning from past data; it is natural that AI should succeed in the Financial Services domain, where bookkeeping and records are second nature to the business. Let’s take the example of credit cards. Today, we use credit score as a means of deciding who is eligible for a credit card and who isn’t. However, grouping people into ‘haves’ and ‘have-nots’ is not always efficient for business. Instead, data about each individual’s loan repayment habits, the number of loans currently active, the number of existing credit cards, etc. can be used to customize the interest rate on a card such that it makes more sense to the financial institution that is offering the card. Now, take a minute to think about which system has the capability to go through thousands of personal financial records to come up with a solution- a learned machine of course! This is where AI comes in. Since it is data driven and data dependent, scanning through these records also gives AI the ability to make a recommendation of loan and credit offerings which make historical sense.

AI and ML are taking the place of a human analyst very fast as inaccuracies which are involved in human selection may cost millions. AI is built upon machine learning which learns over time, less possibility of mistake and analyzing vast volumes of data; AI has established automation to the areas which require, intelligent analytical and clear-thinking. Financial Services Chatbots have indeed proven themselves as a powerful tool to customer satisfaction and an unmatched resource for the enterprises helping them save a lot of time and money. Now, getting back to Facebook’s endeavors in designing and developing Bots to make negotiations the way humans do, let us analyze the chances of the success of this research. This new technology will not only change the way we do business but also non-commercial activities. The example of non-commercial activities can include fixing meeting time. The Bots can fix up the meetings keeping in mind the availability of everyone involved in the meeting.

2. Fraud Detection And Management:

Every business aims to reduce the risk conditions that surround it. This is even true for a financial institution. The loan a bank gives you is basically someone else’s money, which is why you also get paid an interest on deposits and dividends on investments. This is also why banks and financial institutions take fraud very, very seriously. AI is on top when it comes to security and fraud identification. It can use past spending behaviors on different transaction instruments to point out odd behavior, such as using a card from another country just a few hours after it has been used elsewhere, or an attempt to withdraw a sum of money that is unusual for the account in question.Another excellent feature of fraud detection using AI is that the system has no qualms about learning. If it raises a red flag for a regular transaction and a human being corrects that, the system can learn from the experience and make even more sophisticated decisions about what can be considered fraud and what cannot.

3. Financial Advisory Services:

According to the Pwc Report, we can look forward to more robo-advisors. As the pressure increases on financial institutions to reduce their rates of commission on individual investments, machines may do what humans don’t- work for a single down payment. Another evolving field is bionic advisory, which combines machine calculations and human insight to provide options that are much more efficient than what their individual components provide.Collaboration is key. It is not enough to look at a machine as an accessory, or on the other end, as an insufferable know-it-all. An excellent balance and the ability to look at AI as a component in decision-making that is as important as the human viewpoint is the future of financial decision-making.

4. Trading:

Investment companies have been relying on computers and data scientists to determine future patterns in the market. As a domain, trading and investments depend on the ability to predict the future accurately. Machines are great at this because they can crunch a huge amount of data in a short while. Machines can also be taught to observe patterns in past data and predict how these patterns might repeat in the future. While anomalies such as the 2008 financial crisis do exist in data, a machine can be taught to study the data to find ‘triggers’ for these anomalies, and plan for them in future forecasting as well.What’s more, depending on individual risk appetite, AI can suggest portfolio solutions to meet each person’s demand. So a person with a high-risk appetite can count on AI for decisions on when to buy, hold and sell stock. One with a lower risk appetite can receive alerts for when the market is expected to fall, and can thus make a decision about whether to stay invested in the market or to move out.

5. Managing Finance:

Managing finances in this well-connected and the materialistic world can be a challenging task for so many of us, as we look further into the future we can see AI helping us to manage our finances. PFM (personal financial management) is one of the recent developments on the AI-based wallet. Walletstarted by a San Francisco based startup, uses AI to builds algorithms to help the consumers make smart decisions about their money when they are spending it. The idea behind the wallet is very simple it just accumulates all the data from your web footprint and creates your spending graph. Advocates of privacy breaching on the internet may find it offensive but, maybe be this is what lies in future. Thus it has to be the preferred personal financial management in order to save time from making lengthy spreadsheets or writing on a piece of paper. From a small-scale investment to a large scale investment AI commits to be a watchdog of future for managing finances.

Without a speck of doubt, AI is the future for the finance industry. Since the speed at which it is making progressive steps towards making the financial processes easier for the customers, it is very soon going to replace humans and provide faster and much more efficient solutions. Bots are gradually evolving as innovations are being in the AI sector. Massive investments are being made by the firms who are seeing this as a long-term cost-cutting investment. It helps the companies in saving money of hiring humans and also avoiding human errors in this process.

Though it is still in its nascent stage the speed at which it is progressing to evolve the finance sector, it can be well expected that the prospects are going to lead to minor losses, smarter trading and of course top-notch customer experience.


Culled from Mauriti Techlab

Trade finance: Can African banks go digital to fill the $80bn gap?

Digitisation can support much-needed SME trade finance in Africa in the AfCFTA era, although regulatory bottlenecks remain, say participants of the second webinar organised by the Africa Financial Industry Summit.

According to Afreximbank, $5 billion flowed out of Africa in Q1 2020 alone, adding to a trade finance gap that already stood at $82bn.

COVID-19 has further exacerbated African trade flows with global banks calling in credit en masse amid severe risk exposure rationalisation that has hit emerging economies and SMEs hardest.

A seemingly dire situation, but many see opportunities for African banks and the nascent African Continental Free Trade Area (AfCFTA) to step into the breach.

“The gap is there, it is wide, it is concerning,” said webinar moderator Alain Nounke, portfolio manager of financial institutions in Africa at the International Finance Corporation (IFC).


The problem of ‘non-digitised environments’

Souleïma Baddi, the CEO of Swiss-based digital trade operator, Komgo, said firms seeking intra-African trade faced “infrastructure challenges, inefficient port operations, excessive document requirements, customs procedures and complicated border processes”.

But perhaps the biggest challenge, Baddi reckoned, was a prevalence of “non-digitalized environments”. She said only a quarter of local banks believe digitisation could benefit their operations while half think it could deliver cost savings.

“Technology is often seen as too costly, too futuristic, too risky, with insufficient real use cases and real added value,” Baddi said. “But one could argue that fragmented markets have even more to gain by going digital; that such digitalisation should bring homogeneity and consistency.”

“We saw it for example in the consumer banking space with the advent of mobile banking. It played a major role in accelerating service rollout for the previously unbanked community.”


Bringing SMEs in from the trade financing cold

Chinwe Egwim, chief economist at Nigerian-based Coronation Merchant Bank, said Africa’s trade finance gap was felt hardest by small and medium-sized enterprises (SMEs) which account for about 80% of all African private businesses and 50% of African GDP.

“Typically banks in Africa prefer to lend to large corporations,” Egwim said. “SMEs often struggle to gain access to trade finance due to a limited track record and lack of experience in foreign markets.”

AfCFTA’s inception was expected to increase intra-Africa trade from 18% currently to 25% within a decade, via measures including reduced tariffs, trade dispute resolution mechanisms and fostering of local financing avenues offering greater opportunities to SMEs, she said.

Nathalie Louat, IFC’s director of trade and supply chain finance, said AfCFTA was a “game changer for African trade” as it had prioritised African trade finance growth since day one of its 1-year existence.

AfCFTA has the potential “to lift 30m people out of extreme poverty”, Louat said.

She echoed the view that digitisation was a vital agent in trade finance democratisation. “Regulations must eliminate requirements for trade documents to be in paper hardcopy format,” said Louat.

Egwim went further, suggesting: “Artificial Intelligence, blockchain-based distributed ledger systems, cloud-based computing and digital identities can help operational efficiencies and secure reports.”


Trade finance challenges: Too much regulation?

Michelle Knowles, pan-Africa head of trade finance products at South African financial services firm Absa Group, criticised regulations that “have emerged as a significant drag on trade finance in Africa”.

She cited trade-based money laundering and know your customer (KYC) compliance requirements across African nations that are expensive to implement and put some banks off engaging in trade finance activities.

“To solve this requires close collaboration amongst policymakers, banks, DFIs (development finance institutions) and fintechs so that the necessary synergies can be created,” Knowles suggested.


Beefing up SME support

Webinar participants also noted expansion areas for banks.

The head of African trade finance at Société Générale, Blandine Gamblin, highlighted sectors like agribusiness and renewable energy along with infrastructure projects as growth areas.

Gamblin pointed out the French bank had a “close partnership with AfCFTA which is important because we can cover up to 30 countries all together”.

The bank has established SME-focused centres that provide business plans and insurance and other services in countries like Benin, Burkina Faso, Cameroon, Senegal and Madagascar.

Souleymane Diagne, group head of trade finance and services at Ecobank, agreed pan-African banks had an important role to play in fostering efficient, cross-border payments that were customer-focused, affordable and scalable.

They could assist SMEs by “leveraging on their knowledge base in terms of client relationships and putting in place the right products…on both ends of the transaction.”

He added pan-African banks had a big part to play in financing public infrastructure projects like roads and airports.

For further insights, find the on-demand webinar ‘Trade finance: Bridging the gap in times of AfCFTA’ below.


  • Trade finance gap as of Q1 2020: $87bn.
  • 80% of African private businesses are SMEs.
  • Intra-African trade comprises only 18% of total African trade.
  • AfCFTA initiatives expected to boost intra-African trade 25% by 2035 and lift 30m Africans out of extreme poverty.
Culled from the African CEO Forum

Why it is wise to add bitcoin to an investment portfolio

Why it is wise to add bitcoin to an investment portfolio

It is a Nobel prize-winning diversification strategy

“Diversification is both observed and sensible; a rule of behaviour which does not imply the superiority of diversification must be rejected both as a hypothesis and as a maxim,” wrote Harry Markowitz, a prodigiously talented young economist, in the Journal of Finance in 1952. The paper, which helped him win the Nobel prize in 1990, laid the foundations for “modern portfolio theory”, a mathematical framework for choosing an optimal spread of assets.

The theory posits that a rational investor should maximize his or her returns relative to the risk (the volatility in returns) they are taking. It follows, naturally, that assets with high and dependable returns should feature heavily in a sensible portfolio. But Mr Markowitz’s genius was in showing that diversification can reduce volatility without sacrificing returns. Diversification is the financial version of the idiom “the whole is greater than the sum of its parts.”

An investor seeking high returns without volatility might not gravitate towards cryptocurrencies, like bitcoin, given that they often plunge and soar in value. (Indeed, while Buttonwood was penning this column, that is exactly what bitcoin did, falling 15% then bouncing back.) But the insight Mr Markowitz revealed was that it was not necessarily an asset’s own riskiness that is important to an investor, so much as the contribution it makes to the volatility of the overall portfolio—and that is primarily a question of the correlation between all of the assets within it. An investor holding two assets that are weakly correlated or uncorrelated can rest easier knowing that if one plunges in value the other might hold its ground.

Consider the mix of assets a sensible investor might hold: geographically diverse stock indexes; bonds; a listed real-estate fund; and perhaps a precious metal, like gold. The assets that yield the juiciest returns—stocks and real estate—also tend to move in the same direction at the same time. The correlation between stocks and bonds is weak (around 0.2-0.3 over the past ten years), yielding the potential to diversify, but bonds have also tended to lag behind when it comes to returns. Investors can reduce volatility by adding bonds but they tend to lead to lower returns as well.

This is where bitcoin has an edge. The cryptocurrency might be highly volatile, but during its short life it also has had high average returns. Importantly, it also tends to move independently of other assets: since 2018 the correlation between bitcoin and stocks of all geographies has been between 0.2-0.3. Over longer time horizons it is even weaker. Its correlation with real estate and bonds is similarly weak. This makes it an excellent potential source of diversification.

This might explain its appeal to some big investors. Paul Tudor Jones, a hedge-fund manager, has said he aims to hold about 5% of his portfolio in bitcoin. This allocation looks sensible as part of a highly diversified portfolio. Across the four time periods during the past decade that Buttonwood randomly selected to test, an optimal portfolio contained a bitcoin allocation of 1-5%. This is not just because cryptocurrencies rocketed: even if one cherry-picks a particularly volatile couple of years for bitcoin, say January 2018 to December 2019 (when it fell steeply), a portfolio with a 1% allocation to bitcoin still displayed better risk-reward characteristics than one without it.

Of course, not all calculations about which assets to choose are straightforward. Many investors seek not only to do well with their investments, but also to do good: bitcoin is not environmentally friendly. Moreover, to select a portfolio, an investor needs to amass relevant information about how the securities might behave. Expected returns and future volatility are usually gauged by observing how an asset has performed in the past. But this method has some obvious flaws. Past performance does not always indicate future returns. And the history of cryptocurrencies is short.

Though Mr Markowitz laid out how investors should optimise asset choices, he wrote that “we have not considered the first stage: the formation of the relevant beliefs.” The return from investing in equities is a share of firms’ profits; from bonds the risk-free rate plus credit risk. It is not clear what drives bitcoin’s returns other than speculation. It would be reasonable to believe it might yield no returns in future. And many investors hold fierce philosophical beliefs about bitcoin—that it is either salvation or damnation. Neither side is likely to hold 1% of their assets in it.

For more expert analysis of the biggest stories in economics, business and markets, sign up to Money Talks, our weekly newsletter.


Culled From The Economist

Neobanks: What next for the new challengers to African legacy banking?

Africa-focused neobanks are riding the wave of online banking’s growing global influence and are attracting big investment, creating another challenger beyond mobile money for legacy banks. But can they be any more successful in serving the unbanked than traditional counterparts?


Two of the biggest African neobanks are taking big strides: Kuda in Nigeria and TymeBank in South Africa.

South Africa founded TymeBank, which recently moved its headquarters to Singapore, in February won $109 million in funding from British and Philippine investors, including Apis Partners and the Gokongwei Group. In August, Kuda received $55m in a third funding round that valued the neobank at $500m – making it the 7th biggest among all Nigerian banks.


Africa’s fintech funding frontrunners

Other payments and loan-focused fintechs attracting investment are:

Nigerian payments operation OPay ($170m);Ugandan firm Chipper Cash ($152m); Cellulant (payments, Kenya, $54m); PalmPay (payments, Nigeria, $40m), Migo (loans, Nigeria, $37m); Paga (payments, Nigeria, $32m); OneFi (loans, Nigeria, $16m) and Paystack (payments, Nigeria, $12m).

Between them, the Nigerian-dominated top-10 have raised almost $750m in 36 unique funding rounds.

Source: Digest Africa


The intersection of high mobile phone penetration and mass numbers of unbanked or underbanked Africans are key drivers to neobank and fintech customer growth.

But Africa still lags other continents in neobanking exposure – less than 20 of the world’s 300+ neobanks are African-focused.

“The top funding guys in Africa are still behind some of the numbers you’re seeing in Europe, in Asia, in South America,” said Barcelona-based Exton Consulting analyst Lance Daniels, who co-authored a recent report on global neobanking movements.

African neobanks, he said, are attracting many first-time banking clients compared to Europe where the focus is converting customers to digital-only. “It’s still ramping up in Africa whereas a lot of other markets are reaching neobank saturation,” he added.


Onboarding and outreaching

In a couple of years Kuda has onboarded 1.5m mostly retail clients, while TymeBank has 3.5m and continues to onboard 100,000 more a month.

Kuda has taken the digital purist route. Almost all its sign-ups occur via heavily customer-supported online onboarding spaces, consequently attracting more male customers, according to the Nigerian firm’s products and partnership lead Nosakhare Oyegun.

TymeBank favours what it calls a hybrid ‘high-tech, high-touch’ approach in South Africa that has seen about 85% of its clients onboarded face-to-face via some 1,000 kiosks it has erected inside supermarket partners Boxer and Pick n Pay.

Not only does this retail presence help build customer profiles and give TymeBank the kind of geographical coverage few banks’ branch plus ATM networks can match, it has also helped attract large numbers of women adverse to ATMs because their use can incur high fees, are located too far from home or present a personal safety threat, according to TymeBank chief growth officer, Rachel Freeman.

“What we found is that low numbers of South African women use ATMs,” Freeman said, noting more than half of TymeBank’s customers are women.


Banking the unbanked masses

About 57% or 700m of Africa’s 1.2bn population are unbanked according to the FinTech Times. Of five African nations tracked by UK fintech researcher Merchant Machine, South Africa had the lowest level of unbanked citizens at 31%, compared to Morocco which had the world’s highest percentage of unbanked citizens at 71%. Other tracked African countries were Egypt with 67% unbanked; Nigeria with 60% and Kenya 56%.

British banking giant Standard Chartered is moving headlong in this ‘agency banking’ direction with 10,000 ‘touch points’ in Uganda up and running. A deal with telco Airtel Africa is set to broaden its exposure in similar ways across 15 African countries.

The bank’s Africa and Middle East CEO, Sunil Kaushal said women in particular would benefit as “bank branches are not always close to women, especially in conservative cultures or rural regions”.


What next for legacy banks?

Legacy banks are starting to borrow from the neobanking digital playbook, even if analysts like Hendrik Malan, the South Africa-based CEO of analyst Frost & Sullivan Africa are sceptical about their ability to succeed.

“They are trying to delay the attrition process with transactional offers as long as they can but they are geared toward a different kind of customer,” he said. “Contorting themselves to compete with a whole different set of competitive actors is not going to work unless they create parallel brands under a broader umbrella servicing the transactional banking client at the bottom and then the more traditional clients at the top.”

Standard Chartered has since 2018 done just that by launching nine digital operations in sub-Saharan African nations: Côte d’Ivoire, Uganda, Tanzania, Ghana, Kenya, Botswana, Zambia, Zimbabwe and Nigeria.

Kaushal said all incumbent banks had to crack their ‘organisational inertia’ if they wanted to succeed in the digital environment. “It requires an inflight change not just of technology, but of entrenched structural costs and legacy mindsets of all banks, clients and regulators,” the CEO said.

Standard Chartered has moved in this direction by going 100% paperless, conducting all onboarding digitally and is in the process of converting all legacy customers onto digital platforms. Two thirds of ‘regular banking services’ are now conducted via its app.

Consequently, the bank grew its customer base by 500,000 since the pandemic began in March 2020 – equivalent to about “half of its legacy base” with many of them under-35 and female.

Frost & Sullivan Africa’s CEO Malan noted the fine line legacy banks are being forced to tread to hold ground and maintain profitability in the neobanking era.

“The transactional banking obviously at the bottom end of the spectrum is becoming less profitable for them; the physical footprints they need to maintain in order to do that is just not profitable – they can’t compete with the digital banks in any shape or form,” Malan said.

“The African banks of the future will most likely transform into becoming universal banks; the banks that will move towards the upper end of the scale where they provide a comprehensive set of services to their clients whether it be individuals or enterprise with the likes of insurance and vehicle financing, almost all of it done digitally.”


Regulation roulette

Traditional banks are having to act fast as nascent neobanking players like Discovery and Zero enter the fray (in South Africa) and African neobanks expand.

Since TymeBank’s February funding round, the Gokongwei Group. has stepped things up by partnering with TymeBank in a recently announced joint venture – GOtyme – that has quickly secured a Philippine banking licence from the pro-neobank regulator there and made the south-east Asian nation TymeBank’s second market.

TymeBank’s initial expansion plan targeted Egypt (where two thirds of the population are unbanked), but it was canned last year as Egyptian authorities baulked at Tyme’s wholly digital infrastructure.

“Our entire bank is in the cloud on AWS (Amazon Web Services), and the Egyptian regulator would not allow a bank that runs on AWS to actually run that stack in Egypt for reasons relating to cyber security and cyber sovereignty. We decided not to go to Egypt,” Coen Jonker, executive chairman and co-founder of TymeBank said.

South African regulators had no such qualms. By the time it launched there in late 2018 as TymeBank, it had already become the first South African bank of any stripe to secure a commercial banking licence this century.

Kuda is also set for expansion into other African markets in the short term, but Oyegun said much growth potential remains in Nigeria given it had gained only a fraction of the estimated 60m+ unbanked and underbanked Nigerians.

Kuda attributes its growth to offering overdrafts to Nigerian with minimal checks – credit which was denied to them by traditional banks. Defaults percentages rest in the “low single figures” according to Oyegun.

Nigeria-based Zenith Bank is also partnering with Kuda on services the neobank’s Nigerian microfinance banking licence does not authorise it to make such as remittances and some debit actions.

“That’s fine but we are looking into obtaining a full banking licence,” Oyegun said.


Do neobanks need business clients to make money?

While financial inclusion remains core to most African neobanking entities, business customers are not out of the picture. Jonker noted while only about 100,000 of TymeBank’s 3.5m customers hold business accounts, “they are four times as active.”

“We are a fully-fledged commercial bank, and so we have to develop both sides of the balance sheet, the deposit side and the lending side to be successful in the long term.”

Acknowledging that “transactional banking and saving is a thin margin business” Jonker said the bank was refining its product portfolio to boost profitability and meet consumer demands.

Oyegun said Kuda was set to launch a suite of services targeting Nigerian small to medium-sized businesses.


International competition

The African neobanks are set to be joined by the likes of the UK’s biggest digital payments startup, Revolut, valued at £24bn after a recent funding round and quickly moving into neobanking territory by offering bank accounts and overdraft products.

Kiran Wylie, senior communications manager, said Revolut was focused on expanding operations in the US, Australia, Japan and Singapore before slated moves into India and Latin America, but is watching African developments with interest.

“We hope to launch in Africa in the near future,” Wylie said, noting, “Africa’s fintech infrastructure development has improved significantly.”

For Frost’s Malan, the neobanking revolution can’t come fast enough. “One of the biggest challenges in Africa has been the inability to access credit and neobanks are changing that. The African fintech services industry needs a kick in the booty and in this way Africa is ripe for the picking.”


Culled from: Africa CEO Forum

Why businesses fail at machine learning

I’d like to let you in on a secret: when people say ‘machine learning’ it sounds like there’s only one discipline here. There are two, and if businesses don’t understand the difference, they can experience a world of trouble.

A tale of two machine learnings

Imagine hiring a chef to build you an oven or an electrical engineer to bake bread for you. When it comes to machine learning, that’s the kind of mistake I see businesses making over and over.

These are different businesses! Unfortunately, too many machine learning projects fail because the team doesn’t know whether they’re supposed to build the oven, the recipe, or the bread.

If you’re opening a bakery, it’s a great idea to hire an experienced baker well-versed in the nuances of making delicious bread and pastry. You’d also want an oven. While it’s a critical tool, I bet you wouldn’t charge your top pastry chef with the task of knowing how to build that oven; so why is your company focused on the equivalent for machine learning?

Are you in the business of making bread? Or making ovens?

Machine learning research

What they don’t tell you is that all those machine learning courses and textbooks are about how to build ovens (and microwaves, blenders, toasters, kettles… the kitchen sink!) from scratch, not how to cook things and innovate with recipes.

If you build machine learning algorithms, your focus is general purpose tools for others to use. (Kitchen appliances, if you prefer the analogy.) This business is called machine learning research and is typically done by places like academia or Google.

When it comes to machine learning, many organizations are in the wrong business.

You need quite a lot of education to be in this line of work, because there’s a long history here. Some popular algorithms have been around for centuries. For example, the method of least squares for regression, was published in 1805. Trust me, humanity has come a long way in 200 years.

Today, there are some pretty sophisticated appliances out there… how are you going to build a better microwave if you don’t know how this one works? Of course you need all that immersive study! Becoming a researcher takes years and there’s a good reason that the 101 course starts with the basics of calculus.

Applied machine learning

Most businesses just want to get cooking — to solve their business problems. They have no interest in selling microwaves, and yet often make the mistake of trying to build those appliances from scratch. It’s hard to blame them — the current hype and education cycle dominantly focuses on research, instead of application.

If you’re innovating with recipes, don’t reinvent the wheel. Those microwaves exist already. You can get them for free from many places. And if setting up your own machine learning kitchen sounds like a chore, providers like Google Cloud Platform let you use theirs, complete with appliancesingredients, and recipe books.

If you’re innovating in the kitchen, don’t reinvent the wheel.

For most applications, your team doesn’t need to understand the mathematics of backpropagation in neural networks any more than a chef needs to know the wiring diagram for a microwave. But there’s a lot that you do need to know if you’re planning on running an industrial-scale kitchen, everything from curating your ingredients to checking that your dishes are good before you serve them.

Which of these are you selling? The right team to hire depends on your answer.

Crashing and burning with machine learning

Unfortunately, I see a lot of businesses failing to get value from machine learning because they don’t realize that the applied side is a very different discipline from the algorithms research side. Instead, leaders try to start their kitchens by hiring those folks who’ve been building microwave parts their whole lives but have never cooked a thing. What could possibly go wrong? If that works out, it’s because you got lucky and accidentally hired an engineer who is a great chef.

But usually you’re not lucky. There are only so many hours in one lifetime, and if you spend them learning how a microwave is wired, you’ve got fewer to devote to mastering the art of pastry or business. Where — and when! — would your PhD-trained artificial intelligence researcher have gained the skills required for applied machine learning? If you set your heart on the hybrid who’s an expert in both, no wonder you’re complaining about the talent shortage!

If you try to start a restaurant by hiring folks who’ve been building microwave parts their whole lives but have never cooked a thing… what could possibly go wrong?

Whom should you hire instead? Just like in an industrial kitchen, you need an interdisciplinary team with leadership that understands this space. Otherwise, projects fizzle and go nowhere.

Hiring the right team for the job

If you’re selling cutting-edge appliances, hire researchers. If you’re innovating in recipes to sell food at scale, you need people who figure out what’s worth cooking / what the objectives are (decision-makers and product managers), people who understand the suppliers and the customers (domain experts and social scientists), people who can process ingredients at scale (data engineers and analysts), people who can try many different ingredient-appliance combinations quickly to generate potential recipes (applied ML engineers), people who can check that the quality of the recipe is good enough to serve (statisticians), people who turn a potential recipe into millions of dishes served efficiently (software engineers), people who keep the interdisciplinary team on track (project/program managers), and people who ensure that your dishes stay top notch even if the delivery truck brings you a ton of potatoes instead of the rice you ordered (reliability engineers).

While these needn’t be separate individuals, be sure you’ve got each role covered. And before you fling your rotten tomato at me for providing such an incomplete caricature, I’ll freely admit that there’s much more to say about hiring for applied machine learning. I’ve outsourced that to other posts, including this one.

Speaking of outsourcing, if your team has tried all existing tools and can’t make a recipe that meets your business objectives, it makes sense to think about adding skills in building appliances (researcher). Whether or not you hire that person to your permanent staff or outsource the job to an experienced algorithms research firm depends on the scale and maturity of your operation.

Another reason to connect with researchers is that your prototype is so successful that using custom-built appliances makes sense at the massive scale you’re lucky enough to operate at. (What a great problem to have!)

Decision intelligence

Experts should be talking about this, but they aren’t. They’re not owning up to the fact that there’s really two machine learnings here, and so the world is training people in building all these algorithms but not in using them.

My team is working to fix that. We’ve created a new discipline to cover the applied side and we’ve already trained over 15,000 staff members in it. We’re calling it decision intelligence, and it spans all the applied aspects of machine learning and data science.

To put it another way, if research machine learning is building microwaves and applied machine learning is using microwaves, decision intelligence is using microwaves safely to meet your goals and using something else when you don’t need a microwave.

Good luck and have fun!

When it comes to applied machine learning, the hardest part is knowing what you want to cook and how you plan to check it before you serve it to your customers. That part is actually not that hard — just don’t forget to do it.

As for the rest, solving business problems with machine learning is far easier than most people think. Those gleaming kitchens are waiting for you to come play in them. Dive in as you would in a real kitchen. Start tinkering! Every time I meet someone who believes they need to take a traditional machine learning algorithms course — or, goodness! a whole degree — in order to get started, I can’t help but imagine them refusing to use microwaves until they built one themselves. Don’t fall for the lie that says you need a Ph.D to do amazing things with machine learning. Instead, what you really need is a bit of human creativity. Good luck and have fun!

Culled from Hacker Noon

Doughnut Economics: A Review

“Anyone who believes that exponential growth can go on forever in a finite world is either a madman or an economist,” — Kenneth Boulding, 1973

Early on in the pandemic, Marc Andreesen told us it’s time to build. Our embarrassing response rate as a nation was due, in large part, to a shortage of things. We ran out of ventilators, cotton swabs, medical gowns, etc., because we hadn’t bothered to produce more of them. “It’s time for full-throated, unapologetic, uncompromised political support … for aggressive investment in new products, in new industries, in new science, in big leaps forward,” said Andreesen. In other words, more growth.

Others said, f**k that, it’s time to whip out the doughnuts. In April, Amsterdam pledged to turn itself into a giant pastry item by becoming the first city to embrace “Doughnut Economics,” an alternative economic theory that rejects continuous GDP growth as its starry-eyed solution.

Instead, it embraces the doughnut, a circular economy model that seeks to feed and clothe everyone without ransacking the planet. You might be thinking, what are they smoking? Or, it’s socialism, reborn as a doughnut. It’s not: socialism is just as unsustainable. But we’ll let Kate Raworth, the inventor of “Doughnut Economics,” explain.

Too Serious for Its Own Good

Raworth was a renegade British economist who was tired of taking her peers so seriously. As a field, economics has a long history of inflating its own importance. In 1968, the Swiss central bank successfully lobbied and paid for a controversial expansion of the Nobel prize in sciences: overnight, economics was upgraded from an art to a science, and was now awarded the same authority as physics, chemistry, and medicine. The categorization implies that human economic behavior “lends itself to modelling, like chemical reactions or the movement of stars.” No wonder the Council of Economic Advisers (CEA) has every presidents’ ear.

Frustrated with what she considered outdated dogma, Raworth set out to write Doughnut Economics, a growth agnostic model that seeks to update many traditional assumptions of economic theory. “Today we have economies that need to grow, whether or not they make us thrive,” said Raworth. “What we need are economies that make us thrive, whether or not they grow.” Among the doughnut’s other principles? To design a regenerative economy, one that is active within the larger biosphere rather than floating against the white background of a textbook.

Why doughnuts and not something classier, like a beignet? It’s all about the hole. The hole represents misery and deprivation: the space we fall into when our critical needs haven’t been met. The outer ring, on the other hand, represents the ecological ceiling. Cross that line and the planet starts to fall apart. The sweet spot, of course, is the dough. Keep economic activity within the boundaries of the doughnut, and, Raworth tells us, we’ll be safe.

The Charges Against Growth

Raworth is not the first person to bring charges against growth. Thomas Malthus was the original celebrity pessimist, preaching that we would all die of famine thanks to our uncontrollable fondness for marriage. (He didn’t account for the industrial capabilities of Perdue Farms.)

More recently, Donella Meadows, a biophysicist and environmental scientist, provoked outrage with her 1972 report, The Limits to Growth, which used computer simulations to predict the outcome of exponential growth combined with a rising population and unchecked resource use. The conclusion? We’re SOL by 2072. “Growth is one of the stupidest purposes ever invented by any culture,” said Meadows. We need to ask, “Growth of what, and why, and for whom, and who pays the cost, and how long can it last, and what’s the cost to the planet, and how much is enough?”

That’s not to say that Raworth dismisses growth entirely. She acknowledges the deep irony that is central to growth. On the one hand, economic growth is responsible for extraordinary strides in global well-being. Without growth, most of us wouldn’t make it past fifty, our daily budget would be less than $1.90 a day, and on top of that, we’d still have no indoor toilets. (And you thought running out of toilet paper was hard.) On the other hand, Raworth believes that growth, particularly in high-income countries, is dependent on the brainless consumer, widening inequality, and trampling offshore environments.

You Give GDP A Bad Name

Then there’s the issue of GDP itself. What’s so bad about GDP? GDP is simply a measuring tool for the value of goods and services within a given time period. It is widely considered the world’s most powerful indicator of national well-being, and the US Department of Commerce considers it one of the “greatest inventions of the twentieth century.”

Sadly, it’s not very accurate. As Raworth points out, GDP overlooks huge swaths of people that grossly distorts its meaning. Futurist Alvin Toffler, who described conventional economics as a “one-size-misfits-all” fiasco, saw the limitations of GDP and productivity very clearly. “[Economists’] concept of productivity is extremely narrow. They define productivity in terms of your participation in the formal, paid-for work economy. There are millions of people in this society who are productive and do extremely valuable things for the economy, who never get paid.”

A study in 2014 of 15,000 mothers in the United States calculated that if women were paid the going hourly rate for each of their roles, they would earn $120,000 per year. Even mothers who do head out to work each day would earn an extra $70,000 on top of their income for the unpaid care they provide at home. But GDP completely bypasses the cost of social reproduction and fails to value the future labor force. Toffler used to close his presentations by asking business executives, “How productive would your workforce be if it hadn’t been toilet-trained?”

Another group left out by GDP is finance. GDP separates the real economy of goods and services from capital gains. This is significant because capital gains aren’t very equally distributed: in the US, the top 1% received 69% of long-term capital gains in 2018. Is GDP really the best indicator of national health if it doesn’t account for a huge chunk of inequality? Plus, most economic analysts completely ignore the banking sector. In 2008, many major financial institutions were using macroeconomic models in which private banks played no role. Economist Steve Keen, one of the few individuals who predicted the crash, was baffled by this exclusion: “Trying to analyze capitalism without banks, debt, and money is like trying to analyze birds while ignoring they have wings. Good luck.”

The Regenerative Economy

All of these shortcomings lead us to the idea of the regenerative economy, where real-world systems — whether living or non-living — are used as a model for economic-system design. Bill Rees, ecologist and co-creator of the “ecological footprint” concept, explains how it works: “A regenerative system is one that does not deplete or pollute its host and, at best, facilitates its host’s thriving. In other words, consumption by the system must not exceed production by its host; waste production by the system must not exceed the assimilative recycling capacity of its host.”

What would this look like in the economy? For one, replacing linear supply chains with circular ones. Raworth describes the typical supply chain as an “industrial caterpillar, ingesting food at one end, chewing it through, and excreting the waste out the other end.” It’s a nice short-term design for profit-maximization, but, in the long-run, it runs counter to the living world’s natural recycling process and leaves us with a massive pile of garbage.

Raworth suggests transitioning to a circular supply chain where, “instead of heading for landfill, the leftovers from one production process — be they food scraps or scrap metal — become the source materials for the next.” Cellphones, for example, are filled with gold, silver, cobalt and rare earth metals, yet 85% of them wind up in landfills or your sock drawer within two years. In a circular supply chain, they would be designed for easy disassembly and reuse. (Apple definitely hopes this book winds up on the bonfire.)

And as for finance? Regenerative business will get nowhere without funding. But how to redirect resources? Raworth points to John Fullerton, a former managing director at JP Morgan who worked in finance for twenty years before founding the Capital Institute, a non-partisan think tank that seeks to reimagine economics and finance. “[Most people] do not question that the purpose of banking and finance is to make money by using other people’s money, preferably at the fastest rate possible, which usually means fueling those rabid extractive processes that are shattering the long-term holistic health of people and the planet.”

One immediate change Fullerton suggests is developing a systemic rate of return “SRR” rather than an internal rate of return. Currently, finance seeks the highest risk-adjusted rate of return on investment, no matter how it’s generated. A 15 percent IRR is always considered better than a 10 percent IRR to the investor. Any nuance — such as whether an investment is real or speculative — is not reflected in the IRR. Fullerton believes that risk measurements could be tremendously improved if they included broader risks to the economy, society, and planet.

Spare Us The Horoscopes

The persistent branding throughout the book may deter some readers. At times, Raworth’s vision of countries issuing “national doughnut reports” one day or working around “doughnut-shaped conference tables” seemed utterly ridiculous. And sometimes the New Agey, Millennial dialogue was so overwhelming you thought she was going to pull out a horoscope chart next.

But, overall, the idea of modeling our economic systems on ecological cycles seems much more sensible than bits of machinery. There is a growing global movement, led by groups such as the Santa Fe Institute, that rejects the idea that you can understand something simply by looking at its individual parts and ignoring the broader, more complex whole. Current economic models isolate man as a “self-contained globule of desire,” intent on growth for the sake of growth, which is the same ideology as a cancer cell. Can it get any worse?

And, in our biggest quarterly decline in GDP seen since the Great Depression, any news dismissing GDP is welcome. At the very least, we can check in on our friends in Amsterdam in a few years and see how they’re faring. In the meantime, don’t worry about building more things. Let’s relax and have a doughnut.


Culled from Medium

Economics, Like Religion, Assumes It Knows Everything

Both are interested in establishing dogma, not pursuing truth.

It’s amazing just how much imaginary ideas affect the real world. That’s all I could consider as I read the flimsy theological defense of moving the US embassy to Jerusalem. If you aren’t aware, the Christian god apparently cares deeply about the placement of the hegemonic power’s diplomatic HQ.

Curious though that may seem at first, history is full of examples where “His” wants are conspicuously human. It’s almost as if god is a concept by which people (powerful people) rationalize their decisions.

You know, like economics.

Follow me here: economic theory, like the holy word, is not ordained by a truth greater than us. It is a creature of our own making; tamed and held captive by the influential.

Both economics and organized religion disregard human nature, lived experience, and complexity to provide laughably simplistic explanations of the world. Both are inextricably linked with power, so they are uniquely vulnerable to corruption. And both lack any semblance of humility and maintain their dominance by demeaning anyone who dares question the validity of their “insights.” (Please note the Catholic Church’s preferred method of punishment used to be far worse.)

It is for these reasons that I am, shall we say, critical of their self-assuredness.

Yet this blog is first and foremost concerned with economics, so I’ll focus my ire on that particular form of bullshit.

While some economists claim that “the whole intention of empirical economics is to force theory down to Earth,” the opposite seems to be true. Economic debates on the left and right are routinely framed by “the laws of economics”—assumptions that are based upon “synthetic a priori reasoning.”

We’re talking about “eternal” laws like “value is measured by the price people are willing to pay,” “wealth is based on a society’s productivity,” and “something must be produced before it is desired.”

Sure, these laws may appear dubious to the mortal mind, but your critical thought is not welcome in this discipline! Because “one cannot falsify [economic] laws empirically because they are true in themselves.” Or in other words: we said so, therefore it’s true.

Economic theory has utilized this religious strategy so effectively that it has been said that “the laws of man can be bent and broken,” but “the laws of economics can not.”

Zachary Karabell observes:

Referencing “the laws of economics” as a way to refute arguments or criticize ideas has the patina of clarity and certainty. The reality is that referencing such laws is simply another way to justify beliefs and inclinations.

There is no better way to manipulate the masses than to create a belief system which is so foolproof that any doubter can be labelled an ignoramus.

At this point, it’s important to at least address the fact that all human knowledge is based on assumptions. They “permeate our lives precisely because we cannot act without them.” Why, then, focus exclusively on refuting the legitimacy of religious and economic assumptions?

Because these disciplines are social sciences and their methodology “starts with an assumption and is gradually added to with a series of experiments and observation.” Whereas with natural sciences like physics, the methodology relies on “on repeated experiments, laboratory testing, and constant reproductions of results” to arrive at assumptions.

Presupposing the truth and finding the truth are two very different procedures. One seeks knowledge, the other creates doctrine.

Economics is similar to religions like Christianity in this way. The hubris of claiming to know the desires of an omnipresent being is obviously ideological, as is pretending that we know how humans will act in any given situation.

Here, it’s useful to bring up “rational choice theory” which posits that humans, at all times, make prudent and logical decisions. It has been called the “bedrock theory” of economics, with a “God-like ability to make long range, intricate plans exploiting all trade-offs across goods, time, and uncertainty.”

There’s a problem with this neat economic explanation, however. Humans aren’t rational. At all. It is an absurd theory, contrary to human experience (like, say, an immaculate conception), that is nevertheless taken very, very seriously by the economic profession.

(Ironically, our propensity for emotional reasoning has contributed to the rational choice theory’s continued acceptance. In matters that are beyond our comprehension, “we’re happy to unthinkingly agree with others’ seeming expertise.”)

Having blind faith in those who say they know “how things work” has serious consequences, though. It immediately limits our collective imagination of what can be and what is accepted as normal or good. And it gives a huge advantage to the arbiters of knowledge.

Consider, for a second, why people are okay with the richest one percent owning half as much wealth as the rest of the world, but balk at the idea of paying workers $15 per hour. Why is this the case?

Could it be that economists’ adherence to the law of supply and demand has meant they worry more about slight changes in low wages than ghastly inequality?

As my boss Nick Hanauer has remarked, “the claim that wages go up, jobs will go down is not a theory, it’s a scam.” He goes onto quote James Buchanan, a Nobel Prize-winning economist, who compared the law of supply and demand “to a force of nature”:

Just as no physicist would claim that ‘water runs uphill,’ no self-respecting economist would claim that increases in the minimum wage increase employment. Such a claim, if seriously advanced, becomes equivalent to a denial that there is even minimal scientific content in economics.

But as we’ve established above, denying economic assumptions is not the same as denying scientific ones. If we were to find that in every single situation a minimum wage increase lowers employment, then yes, denying that “law” would be silly.

That isn’t the case though—at all. Anyone who cares to look into the minimum wage literature will see that there is, in fact, a lot of evidence to suggest that the minimum wage benefits the broader economy—including employment.

Economic and Christian assumptions have led to positive developments in society, though. For example, the Protestant Reformation’s radical notion that “Christ alone” “is the only mediator between God and man,” is a totally fabricated idea. Yet, its promotion and subsequent acceptance across much of the world has helped establish a philosophical justification for recognizing human rights.

Likewise, economic assumptions have led to great developments in mankind. (The earth’s climate is another story, but I digress.) Capitalism has led to jaw-dropping advances of humankind that would have been unfathomable to the ancient mind. World poverty has plummeted. Humans are living longer than ever. And thanks to market competition I can now do almost anything from the comfort of my couch.

Acknowledging these forms of progress, however, shouldn’t stop us from criticizing the deification of assumptions—either in economics or in Christianity. These disciplines can still attain these aforementioned positive outcomes without having to hold onto some of their more evil concepts.

Christianity can advance compassion and charity without having to warn about a nonexistent hell, just as economics can improve human welfare without having to suppress the wages of the working class.

Thankfully, all it takes for us to change the world around us is to call bullshit on the common acceptance of these more pernicious ideas. Just read about Galileo Galilei. Or Martin Luther. Or Karl Marx. Baseless beliefs only exist for as long as people continue to accept them. That realization is both frightening, yet liberating, and should embolden all of us to constantly question anyone or anything which claims to reveal the truth.

Imagine the possibilities.

Culled from civic SKUNK Works