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Banks and Artificial Intelligence

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“Artificial Intelligence” is a terminology specifically invented in 1956 by John McCarthy and concerns the ability to make appropriate generalizations quickly, but based on an inevitably limited set of data.

 The wider the scope and the faster conclusions are drawn, and with minimal information, the smarter the machine’s behaviour can be defined.

 Intelligence is the creative adaptation to quick changes in the environment. This is the now classic definition, but in this case, with machines, the speed and the increasingly narrow base of the starting data are also evaluated.

 What if the starting data does not contain exactly the necessary information – which is possible? What if, again, the speed of the solution stems from the fact that the data collected is too homogeneous and does not contain the most interesting data?

 Konrad Lorenz, the founder of animal ethology, was always very careful to maintain that between instinctive behaviour and learned behaviour, external environmental and genetic sources can be equally “intelligent”. The fact, however, is that greater flexibility of a behaviour – always within a reasonable time, but not as quickly as possible – generates greater intelligence of the animal.

As said by a great student of Lorenz, Nikko Tinbergen, human beings are “representational magpies”, which means that much of their genetic and informative history has no practical value.

 When the collection of information becomes easy, the “adaptive” magpie has a very adaptive behaviour, but when the data collection is at the maximum, all data counts and we never know which, among this data, will really be put into action.

 In other words, machine data processing is a “competence without understanding”, unless machines are given all senses – which is currently possible.

 Human intelligence is defined when we are at the extreme of physically possible data acquisition, i.e. when individuals learn adaptive-innovative behaviour from direct imitation of abstract rules.

 Abstract rules, not random environmental signals.

 If machines could reach this level, they would need such a degree of freedom of expression that, today, no machine can reach, not least because no one knows how to reach this level; and how this behaviour is subsequently coded.

 What if it cannot be encoded in any way?

 The standardization of “if-then” operations that could mimic instincts, and of finalized operations (which could appear as an acquired Lorenz-style imprinting) is only a quantitative expansion of what we call “intelligence”, but it does not change its nature, which always comes after the particular human link between instinct, intelligence and learning by doing.

 Which always has an accidental, statistical and unpredictable basis. Which duck will be the first to call Konrad Lorenz “dad”, thus creating a conditioning for the others? No one can predict that.

If systematized, bio-imitation could be a way to produce – in the future – sentient machines that can create their own unique and unrepeatable intelligent way to react to the environment, thus creating a one and only intelligent behaviour. Will it be unique?

However, let us go back to Artificial Intelligence machines and how they work.

 In the 1980s there was the first phase of large investment in AI, with the British Alvey Program; the U.S. DARPA Program spending a billion US dollars on its Strategic Computing Initiative alone; finally the Japanese Fifth Generation Computer Project, investing a similar amount of money.

At the time there was the booming of “expert systems”, i.e. symbolic mechanisms that solved problems, but in a previously defined area.

From the beginning, expert systems were used in financial trading.

There was the hand of the expert system in the fall of the Dow Jones Industrial Average by 508 points in 1987. In 1990, however, Artificial Intelligence also began to be used in the analysis of financial frauds, with an ad hoc program used by the Financial Crimes Enforcement Network (FinCEN), especially with the possibility to automatically review 200,000 transactions per week and to identify over 400 illegal transactions.

Machine learning, the model on which the most widely used AI financial technology relies, is based on a work by McCullogh and Pitts in 1943, in which it was discovered that the human brain produces signals that are both digital and binary.

 A machine learning system is composed, in principle, by: 1) a problem; 2) a data source; 3) a model; 4) an optimization algorithm; 5) a validation and testing system.

 In 2011, deep learning (DL) added to the other “expert” systems.

It is a way in which machines use algorithms operating at various separate levels, as happens in the real human brain. Hence deep learning is a statistical method to find acceptably stable paradigms in a very large data set, by imitating our brain and its structure in layers, areas and sectors.

As explained above, it is a mechanism that “mimics” the functioning of the human brain, without processing it.

 DL could analyse for the first time non-linear events, such as market volatility, but its real problem was the verification of models: in 2004 Knight Capital lost 440 million US dollars in 45 minutes, because it put into action a DL and financial trading model that had not been tested before.

 In 2013, during a computer block of only 13 minutes, Goldman Sachs flooded the U.S. financial market with purchase requests for 800,000 equities. The same week., again for a computer error, the Chinese Everbright Securities bought 4 billion of various shares on the Shanghai market, but without a precise reason.

 Between 2012 and 2016, the United States invested 18.2 billion US dollars in Artificial Intelligence, while only 2.6 were invested by China and 850 million US dollars by the United Kingdom in the same period.

 The Japanese Government Pension Savings Investment Fund, the world’s largest pension fund manager, thinks it can soon replace “human” managers with advanced Artificial Intelligence systems.

 BlackRock has just organized an AILab.

 In 2017, however, China overtook the United States in terms of AI startups, with 15.2 billion funding.

 China now has 68% of AI startups throughout Asia, raising 1.345 billion US dollars on the markets for their take-off.

China has also overtaken the United States in terms of Artificial Intelligence patents over the last five years.

Nevertheless, considered together, the USA and China still account for over 50% of all AI patents worldwide.

 China also dominates the market of patents on AI technology vision systems, while deep learning data processing systems are now prey to the big global companies in the sector, namely Microsoft, Google and IBM. Similar Chinese networks are rapidly processing their new “intelligent” data collection systems, also favoured by the fact that the Chinese population is about twice as much as the US population and hence the mass of starting data is huge.

 The Chinese intelligence industry zone near Tianjin is already active.

In the end, however, how does Artificial Intelligence change the financial sector?

AI operates above all in the trading of securities and currencies in various fields: algorithmic trading; the composition and optimization of portfolios; validation of investment models; verification of key operations; robo-advising, namely robotic consultancy; the analysis of impact on the markets; the effectiveness of regulations and finally the standard banking evaluations and the analysis of competitors’ trading.

 Algorithmic trading is a real automatic transaction system – a Machine Learning program that learns the structure of transaction data and then tries to predict what will happen.

Nowadays computers already generate 70% of transactions in financial markets, 65% of transactions in futures markets and 52% of transactions in the public debt securities market.

The issue lies in making transactions at the best possible price, with a very low probability of making mistakes and with the possibility of checking different market conditions simultaneously, as well as avoiding psychological errors or personal inclinations.

In particular, algorithmic trading concerns hedge funds operations and the operations of the most important clients of a bank or Fund.

 There are other AI mathematical mechanisms that come into play here.

 There is, in fact, signal processing, which operates by filtering data to eliminate disturbing elements and observe the development trends of a market.

 There is also market sentiment.

The computer is left completely unaware of the operations in progress, until the specific algorithm is put to work – hence the machine immediately perceives the behaviour of supply and demand.

 There is also the news reader, a program that learns to interpret the main social and political phenomena, as well as pattern recognition, an algorithm teaching the machine to learn and react when the markets show characteristics allowing immediate gains.

Another algorithm is available, developed by a private computer company in the USA, which processes millions of “data points” to discover investment models or spontaneous market trends and operates on trillions of financial scenarios, from which it processes the scenarios deemed real.

 Here, in fact, 1,800 days of physical trading are reduced to seven minutes.

However, the algorithms developed from evidence work much better than human operators in predicting the future.

 Artificial Intelligence works as a prediction generator even in the oldest financial market, namely real estate.

 Today, for example, there is an algorithm, developed by a German company, that automatically “extracts” the most important data from the documents usually used to evaluate real estate transactions.

 In Singapore, Artificial Intelligence is used to calculate the value of real estate property, with a mix of algorithms and comparative market analysis. Man is not involved at all.

As to corporate governance, there are AI programs that select executives based on their performance, which is analysed very carefully.

What is certainly at work here is the scientist and naive myth of excluding subjectivity, always seen as negative. The program, however, is extremely analytical and full of variables.

 Artificial Intelligence is also used in the market of loans and mortgages, where algorithms can be processed from an infinity of data concerning clients (age, work, gender, recurrent diseases, lifestyles, etc.) and are linked to operations – always through an algorithm – which are ordered, without knowing it, from one’s own mobile phone or computer.

 So far we have focused on Artificial Intelligence algorithms.

 But there is also quantum computing (QC), which is currently very active already. Its speed cannot be reached by today’s “traditional” computers.

 It is a more suitable technology than the others to solve problems and make financial forecasts, because QC operates with really random variables, while the old algorithms simply simulate random variables.

 Quantum computing can process several procedures simultaneously, and these “coexistence states” are defined as qubits.

In a scenario analysis, QC can evaluate a potentially infinite set of solutions and results that have been randomly generated.

 An extremely powerful machine which, however, cannot determine exactly – as it also happens to slower machines – whether the scenario processed corresponds to human interests (but only to the initial ones known by the machine) or whether the procedure does not change during operations.

Advisory Board Co-chair Honoris Causa Professor Giancarlo Elia Valori is an eminent Italian economist and businessman. He holds prestigious academic distinctions and national orders. Mr. Valori has lectured on international affairs and economics at the world’s leading universities such as Peking University, the Hebrew University of Jerusalem and the Yeshiva University in New York. He currently chairs “International World Group”, he is also the honorary president of Huawei Italy, economic adviser to the Chinese giant HNA Group. In 1992 he was appointed Officier de la Légion d’Honneur de la République Francaise, with this motivation: “A man who can see across borders to understand the world” and in 2002 he received the title “Honorable” of the Académie des Sciences de l’Institut de France. “

Economy

The Blazing Revival of Bitcoin: BITO ETF Debuts as the Second-Highest Traded Fund

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It seems like bitcoin is as resilient as a relentless pandemic: persistent and refusing to stay down. Not long ago, the crypto-giant lost more than half of its valuation in the aftermath of a brutal crackdown by China. Coupled with pessimism reflected by influencers like Elon Musk, the bitcoin plummeted from the all-time high valuation of $64,888.99 to flirt around the $30,000 mark in mere weeks. However, over the course of the last four months, the behemoth of the crypto-market gradually climbed to reclaim its supremacy. Today, weaving through national acceptance to market recognition, bitcoin could be the gateway to normalizing the elusive crypto-world in the traditional global markets: particularly the United States.

The recent bullish development is the launch of the ProShares Bitcoin Strategy ETF – the first Bitcoin-linked exchange-traded fund – on the New York Stock Exchange. Trading under the ticker BITO, the Bitcoin ETF welcomed a robust trading day: rising 4.9% to $41.94. According to the data compiled by Bloomberg, BITO’s debut marked it as the second-highest traded fund, behind BlackRock’s Carbon fund, for the first day of trading. With a turnover of almost $1 billion, the listing of BITO highlighted the demand for reliable investment in bitcoin in the US market. According to estimates on Tuesday, More than 24 million shares changed hands while BITO was one of the most-bought assets on Fidelity’s platform with more than 8,800 buy orders.

The bitcoin continued to rally, cruising over the lucrative launch of BITO. The digital currency rose to $64,309.33 on Tuesday: less than 1% below the all-time high valuation. In hindsight, the recovery seems commendable. The growing acceptance, albeit, has far more consequential attributes. The cardinal benefit is apparent: evidence of gradual acceptance by regulators. “The launch of ProShares’ bitcoin ETF on the NYSE provides the validation that some investors need to consider adding BTC to their portfolio,” stated Hong Fang, CEO of Okcoin. In simpler terms, not only would the listing allow relief to the crypto loyalists (solidifying their belief in the currency), but it would also embolden investors on the sidelines who have long been deterred by regulatory uncertainty. Thus, bringing larger, more rooted institutional investors into the crypto market: along with a surge of capital.

However, the surging acceptance may be diluting the rudimentary phenomenon of bitcoin. While retail investors would continue to participate in the notorious game of speculation via trading bitcoin, the opportunity to gain indirect exposure to bitcoin could divert the risk-averse investors. It means many loyalists could retract and direct towards BITO and other imminent bitcoin-linked ETFs instead of setting up a digital custodianship. Ultimately, it boils down to Bitcoin ETFs being managed by third parties instead of the investor: relenting control to a centralized figure. Moreover, with growing scrutiny under the eye of SECP, the steps vaguely intimate a transition to harness the market instead of liberalizing it: quiet oxymoronic to the entire decentralized model of cryptocurrencies.

Nonetheless, the listing of BITO is an optimistic development that would draw skeptics to at least observe the rampant popularity of the asset class. While the options on BITO are expected to begin trading on the NYSE Arca Options and NYSE American Options exchanges on Wednesday, other futures-based Bitcoin ETFs are on the cards. The surging popularity (and reluctant acceptance) amid tightening regulation could prove a turn of an era for the US capital markets. However, as some critics have cited, BITO is not a spot-based ETF and is instead linked to futures contracts. Thus, the restrain is still present as the regulators do not want a repeat of the financial crisis. Nevertheless, bitcoin has proved its deterrence in the face of skepticism. And if the BITO launch is to be marveled at, then the regulations are bound to adapt to the revolution that is unraveling in the modern financial reality.

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Economy

Is Myanmar an ethical minefield for multinational corporations?

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Business at a crossroads

Political reforms in Myanmar started in November 2010 followed by the release of the opposition leader, Aung San Suu Kyi, and ended by the coup d’état in February 2021. Business empire run by the military generals thanks to the fruitful benefits of democratic transition during the last decade will come to an end with the return of trade and diplomatic sanctions from the western countries – United States (US) and members of European Union (EU).  US and EU align with other major international partners quickly responded and imposed sanctions over the military’s takeover and subsequent repression in Myanmar. These measures targeted not only the conglomerates of the military generals  but also the individuals who have been appointed in the authority positions and supporting the military regime.

However, the generals and their cronies own the majority of economic power both in strategic sectors ranging from telecommunication to oil & gas and in non-strategic commodity sectors such as food and beverages, construction materials, and the list goes on. It is a tall order for the investors to do business by avoiding this lucrative network of the military across the country. After the coup, it raises the most puzzling issue to investors and corporate giants in this natural resource-rich country, “Should I stay or Should I go?”

Crimes against humanity

For most of the people in the country, war crimes and atrocities committed by the military are nothing new. For instances, in 1988, student activists led a political movement and tried to bring an end to the military regime of the general Ne Win. This movement sparked a fire and grew into a nationwide uprising in a very short period but the military used lethal force and slaughtered thousands of civilian protestors including medical doctors, religious figures, student leaders, etc. A few months later, the public had no better options than being silenced under barbaric torture and lawless killings of the regime.

In 2007, there was another major protest called ‘Saffron Uprising’ against the military regime led by the Buddhist monks. It was actually the biggest pro-democracy movement since 1988 and the atmosphere of the demonstration was rather peaceful and non-violent before the military opened live ammunitions towards the crowd full of monks. Everything was in chaos for a couple of months but it ended as usual.

In 2017, the entire world witnessed one of the most tragic events in Myanmar – Again!. The reports published by the UN stated that hundreds of civilians were killed, dozens of villages were burnt down, and over 700,000 people including the majority of Rohingya were displaced to neighboring countries because of the atrocities committed by the military in the western border of the country. After four years passed, the repatriation process and the safety return of these refugees to their places of origin are yet unknown. Most importantly, there is no legal punishment for those who committed and there is no transitional justice for those who suffered in the aforementioned examples of brutalities.

The vicious circle repeated in 2021. With the economy in free fall and the deadliest virus at doorsteps, the people are still unbowed by the oppression of the junta and continue demanding the restoration of democracy and justice. To date, Assistant Association for Political Prisoner (AAPP) reported that due to practicing the rights to expression, 1178 civilians were killed and 7355 were arrested, charged or sentenced by the military junta. Unfortunately, the numbers are still increasing.

Call for economic disengagement

In 2019, the economic interests of the military were disclosed by the report of UN Fact-Finding Mission in which Myanmar Economic Corporation (MEC) and Myanmar Economic Holding Limited (MEHL) were described as the prominent entities controlled by the military profitable through the almost-monopoly market in real estate, insurance, health care, manufacturing, extractive industry and telecommunication. It also mentioned the list of foreign businesses in partnership with the military-linked activities which includes Adani (India), Kirin Holdings (Japan), Posco Steel (South Korea), Infosys (India) and Universal Apparel (Hong Kong).

Moreover, Justice for Myanmar, a non-profit watchdog organization, revealed the specific facts and figures on how the billions of revenues has been pouring into the pockets of the high-ranked officers in the military in 2021. Myanmar Oil & Gas Enterprise (MOGE), an another military-controlled authority body, is the key player handling the financial transactions, profit sharing, and contractual agreements with the international counterparts including Total (France), Chevron (US), PTTEP (Thailand), Petronas (Malaysia), and Posco (South Korea) in natural gas projects. It is also estimated that the military will enjoy 1.5 billion USD from these energy giants in 2022.

Additionally, data shows that the corporate businesses currently operating in Myanmar has been enriching the conglomerates of the generals and their cronies as a proof to the ongoing debate among the public and scholars, “Do sanctions actually work?” Some critics stressed that sanctions alone might be difficult to pressure the junta without any collaborative actions from Moscow and Beijing, the longstanding allies of the military. Recent bilateral visits and arm deals between Nay Pyi Taw and Moscow dimmed the hope of the people in Myanmar. It is now crystal clear that the Burmese military never had an intention to use the money from multinational corporations for benefits of its citizens, but instead for buying weapons, building up military academies, and sending scholars to Russia to learn about military technology. In March 2021, the International Fact Finding Mission to Myanmar reiterated its recommendation for the complete economic disengagement as a response to the coup, “No business enterprise active in Myanmar or trading with or investing in businesses in Myanmar should enter into an economic or financial relationship with the security forces of Myanmar, in particular the Tatmadaw [the military], or any enterprise owned or controlled by them or their individual members…”

Blood money and ethical dilemma

In the previous military regime until 2009, the US, UK and other democratic champion countries imposed strict economic and diplomatic sanctions on Myanmar while maintaining ‘carrot and stick’ approach against the geopolitical dominance of China. Even so, energy giants such as Total (France) and Chevron (US), and other ‘low-profile’ companies from ASEAN succeeded in running their operations in Myanmar, let alone the nakedly abuses of its natural resources by China. Doing business in this country at the time of injustice is an ethical question to corporate businesses but most of them seems to prefer maximizing the wealth of their shareholders to the freedom of its bottom millions in poverty.

But there are also companies not hesitating to do something right by showing their willingness not to be a part of human right violations of the regime. For example, Australian mining company, Woodside, decided not to proceed further operations, and ‘get off the fence’ on Myanmar by mentioning that the possibility of complete economical disengagement has been under review. A breaking news in July, 2021  that surprised everyone was the exit of Telenor Myanmar – one of four current telecom operators in the country. The CEO of the Norwegian company announced that the business had been sold to M1 Group, a Lebanese investment firm, due to the declining sales and ongoing political situations compromising its basic principles of human rights and workplace safety.

In fact, cutting off the economic ties with the junta and introducing a unified, complete economic disengagement become a matter of necessity to end the consistent suffering of the people of Myanmar. Otherwise, no one can blame the people for presuming that international community is just taking a moral high ground without any genuine desire to support the fight for freedom and pro-democracy movement.

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Economy

The Covid After-Effects and the Looming Skills Shortage

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coronavirus people

The shock of the pandemic is changing the ways in which we think about the world and in which we analyze the future trajectories of development. The persistence of the Covid pandemic will likely accentuate this transformation and the prominence of the “green agenda” this year is just one of the facets of these changes. Market research as well as the numerous think-tanks will be accordingly re-calibrating the time horizons and the main themes of analysis. Greater attention to longer risks and fragilities is likely to take on greater prominence, with particular scrutiny being accorded to high-impact risk factors that have a non-negligible probability of materializing in the medium- to long-term. Apart from the risks of global warming other key risk factors involve the rising labour shortages, most notably in areas pertaining to human capital development.

The impact of the Covid pandemic on the labour market will have long-term implications, with “hysteresis effects” observed in both highly skilled and low-income tiers of the labour market. One of the most significant factors affecting the global labour market was the reduction in migration flows, which resulted in the exacerbation of labour shortages across the major migrant recipient countries, such as Russia. There was also a notable blow delivered by the pandemic to the spheres of human capital development such as education and healthcare, which in turn exacerbated the imbalances and shortages in these areas. In particular, according to the estimates of the World Health Organization (WHO) shortages can mount up to 9.9 million physicians, nurses and midwives globally by 2030.

In Europe, although the number of physicians and nurses has increased in general in the region by approximately 10% over the past 10 years, this increase appears to be insufficient to cover the needs of ageing populations. At the same time the WHO points to sizeable inequalities in the availability of physicians and nurses between countries, whereby there are 5 times more doctors in some countries than in others. The situation with regard to nurses is even more acute, as data show that some countries have 9 times fewer nurses than others.

In the US substantial labour shortages in the healthcare sector are also expected, with anti-crisis measures falling short of substantially reversing the ailments in the national healthcare system. In particular, data published by the AAMC (Association of American Medical Colleges), suggests that the United States could see an estimated shortage of between 37,800 and 124,000 physicians by 2034, including shortfalls in both primary and specialty care.

The blows sustained by global education from the pandemic were no less formidable. These affected first and foremost the youngest generation of the globe – according to UNESCO, “more than 1.5 billion students and youth across the planet are or have been affected by school and university closures due to the COVID-19 pandemic”. On top of the adverse effects on the younger generation (see Box 1), there is also the widening “teachers gap”, namely a worldwide shortage of well-trained teachers. According to the UNESCO Institute for Statistics (UIS), “69 million teachers must be recruited to achieve universal primary and secondary education by 2030”.

From our partner RIAC

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