Every time I pass by the busy streets of developing Asian cities, Thimphu’s included, I couldn’t help but notice the bustling small businesses lining up the side streets. I could feel the raw energy among the entrepreneurs eager to expand their business, against all odds.
Aside from generating income for their family by selling their products and services, these entrepreneurs are playing important roles in their country’s economic development. The dynamism of small and medium sized enterprises (SMEs) and entrepreneurship is key for employment, economic diversification, and inclusive growth across the globe, not just in developing countries. SMEs that grow—in terms of employees, profitability or market share—can have a considerable impact on productivity and the competitiveness of national and sub-national economies. SMEs that grow also contribute to raising wage and income levels. As a result, establishing a strong SME sector sits prominently on the economic development agendas, especially in Asia where sustainable and inclusive growth is being actively sought.
In Bhutan, vibrant SMEs are expected to play a vital role in providing employment opportunities to the growing number of young and educated Bhutanese. They are also expected to contribute towards laying a strong foundation for reducing economic vulnerability, increasing diversity, and paving the way for Bhutan’s successful transition to middle-income country status.
Strengthening Bhutan’s SMEs is critical in helping women empowerment in the country. In 2016, 36% of the registered micro, small and medium enterprises were owned and managed by women. With a female-to-male labor force ratio of 59%, Bhutan’s economic future is tied to the ability of women to seize and expand opportunities in the marketplace.
The Royal Government of Bhutan have sought to remove the structural barriers for the creation of new businesses and growth of existing ones, such as developing an institutional framework for SME program formulation and implementation, streamlining registration and licensing procedures, and building SME infrastructure and business development capacity.
Despite these efforts, further expansion in the SME sector is often curtailed by limited access to financing. Evidence indicates that SMEs continue to be undersupplied with financial products and services that are critical to their growth.
While SMEs make up 95% of registered businesses in Bhutan, their contribution to GDP remains miniscule at about 4% and they provide only about 11% of total employment. Access to finance is a key constraint for further growth. Only 25% of small businesses and 9% of micro enterprises access bank financing, while most rely on internal funding or retained earnings.
Expanding access to finance for SMEs is key for these enterprises to thrive and grow. The impact of the Priority Sector Lending policy launched by the Royal Monetary Authority of Bhutan mandating soft loans by commercial banks to SMEs, has yet to be materialized.
Credit underwriting often does not focus enough on the small businesses’ cash flows. Only 35% of small and micro enterprises in Bhutan prepare externally audited financial statements. In 90% of cases in Bhutan, banks require collateral to guarantee a loan, and only fixed assets are allowed as collateral. This is far higher than the median of OECD countries where only 40% of SME bank loans require collateral. Collateral requirements for SMEs in Bhutan average at 178%. Moreover, prevailing practices only allow for the collateralization of fixed assets. Most small enterprises simply do not have enough property titles to satisfy the collateral requirements to get financing from banks.
I am pleased that ADB is assisting the government and is working with key stakeholders, including regulators, banks and private sector entrepreneurs to tackle these issues, developing an understanding of the particular needs and preferences of SMEs, and inventing tailored approaches to overcome high credit risk and cost to serve SMEs, in order to unleash the potential of the private sector for the development in Bhutan.
In 2012, ADB completed a $15-million grant for Micro, Small, and Medium Enterprise (MSME) Sector Development Program to support growth and development of Bhutan’s MSMEs through policy reforms, credit extensions, and business development support. Through this grant, MSME loan portfolios increased to 35% and 1,900 SME loan accounts opened.
In 2018, ADB completed the second phase of Strengthening Economic Management Program that covers extensive policy actions to improve revenue collection and tax service delivery, develop capital markets and enhance financial stability through macro-prudential management, improve regulatory and supervisory framework of capital market and financial sector, strengthen access to finance, financial inclusion, and financial literacy, and improve the service and coverage of the Credit Information Bureau.
This week, ADB’s experts are in Paro to deliver a training program for credit officers and managers of local banks on risk management for SME lending. As part of the workshop, our experts will introduce best practices in movable asset-based lending. Widening product offerings and lending to SMEs against moveable collateral—such as equipment, stored crops, crop transactions and other inventory or livestock—could help micro enterprises and, in particular, agribusinesses, who employ 70% of the private-sector work force, but represented only 5% of aggregate credit, grow their business and give an important impetus to improved livelihoods of local farmers.
In addition, ADB is looking to continue its support to the government’s financial market development program. ADB’s financial assistance will seek to build upon the momentum of past ADB support and aims to assist the country in removing policy distortions related to financial intermediation and access to finance for sustainable economic growth by developing nonbank financial institutions, strengthening the stability and integrity of the financial system, and promoting financial inclusion. The program will support the government with institutional reforms to encourage private sector investment.
Bhutan’s Cottage, Small and Medium Industry Strategy 2012-2020 states that more emphasis would be given to women-owned SMEs, and this is reflected in ADB’s Country Partnership Strategy 2019-2023 with gender-related focus on market access and entrepreneurship.
Banks and Artificial Intelligence
“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.
The Reckoning: Debt, Democracy and the Future of American Power- Book Review
Authors: Junaid R.Soomro and Nadia Shaheen
The chapter is written by Michael Moran in which he discussed about the relations between the economic institutions with the other institutions of the state. A state is a combination of many institutions that work together as a single body to make the state run accordingly. Political and economic institutions are two major components of the state. Politics and economy somehow depend on each other from a very long time. The both concepts are old and influenced by each other. The major changes occurred after the industrial revolution that gave birth to new tactics and opportunities to the economy. Earliest, before the French Revolution the economy was controlled by the elites that were the political identities. This is the example that how those bourgeois controlled the economic structure of the state and how they shape or influence the economical aspect of the society. These involvements of both disciplines gave birth to a new subject that is known as the political economy of the states, that how political and economic policies influence each other because it is not possible for any institution to work separately. The economic institutions shape the economic structure of the state and it is controlled by many aspects, including the political institutions, the economic regulations, the political structure of the state that somehow effects the economic institution of the state.
The chapter tells us that how economic institution and other institutions are interconnected.
Firstly, the focus is on the political institutions. The recognitions of an economic institution as a political act. The “politics” and “market” are somehow interconnected. It’s not because the political institutions shape the fate of economy, but the economy shapes it as well. From the start of the history these two aspects are there and depend on each other. We can see it through the examination of the history that how the political elites dominated the society because they were also superior financially. The political institutions somehow legitimize the economic institutions. According to “Godin” different preoccupations drive inquiry in different disciplines: for instance, choice in economy and the power in the politics.
Secondly the focus is the connection between institutionalism and the economic institutions.
The institutions are constructs of human mind, we cannot see or feel them. The regulations and the market grew up together. The current world politics is an example that how the regulations affect the economy and shape it as different stats can be taken as a model who are following the regulations. The institutions determine the opportunities of the society and in result the organizations are made in order or take benefit of those opportunities. There are several parallels that shape the behavior of the institutions that later affects the other institutions including the economic institution.
Thirdly, the connection between the economic institutions and the regulations.
The regulations are made to control the behavior of the institutions. This faced major change after the industrial revolution when many regulations were made that were supposed to control the outcomes of the institutions. We cannot run from globalization, this is the reason that the concept is not the same as it was in the past, but it came up with the new characteristics. Mainly the evolution in the middle of the twentieth century created a paradigmatic shift in the relationship of economic and political institutions. There are agencies with in the states that regulates the working on an institution and on the international level there are multinational corporations. This gives us two basic concepts. The first is uncertainty about the boundaries between the politics and economy, and the second is the importance of the agencies that fills the space and regulates the institutions.
Fourthly, the connection between the economic institutions and the capitalism.
Capitalism and the economy are directly connected with each other because the industrial revolution triggered the economy. Industries were made after the revolution and the world faced a new era of progress and economic change. The modern organizations are the basics that can be taken as the source of understanding the modern political economy. Industries were made after the industrial revolution that mainly works on the productivity, the more the productions are the more it will benefit. This era was a game changer for the economic aspect of the society and later it the economic institutions modified themselves.
Fifthly, the economic institutions and the democratic government.
The connection between democratic political institutions and the economic institution is complex. It depends that how far democratic government can try to constrain the operations of the economic institutions or how far the economic institutions can try the constrain the operation of the democratic government. the basic aspect of the relation is the relationship between the democracy and the market order. The control of the trade union and the control of the business. There are several problems such as the tussle between the capitalist institution and the democratic institution. There are several measures that can make both sides work together. The democratic governments usually believe on large economic interests and they also shape it according to their interests. There come the institutional regulations that regulates the behavior of these institutions in the particular manner.
State is made of many institutions. All the institutions work together this is the reason they depend on each other to work properly. The economic institution is the important institution of the state that makes it stand on its own. Today the examples are in front of us, those states th at has the best economic structures are now ruling the world. USA is the major power but with the passage of time new economic powers are competing with each other. The institutions regulate the behaviors but there are negative aspects when people use the institutions for their benefits. After the industrial revolutions there were merits and demerits. It depends on how one regulates the authority. If the institutions work properly the whole structure can be run perfectly but the interference that affects the institutions negatively can damage the structure. Today in the world where the concept of politics and economy is so dominant it is very important to regulate the bodies properly.
About the Author
Michael E. Moran (born May 1962 in Kearny, New Jersey) is an American author and analyst of international affairs he is also a digital documentarian who has held senior positions at a host of media, financial services, and consulting organizations. A foreign policy journalist and former partner at the global consultancy Control Risks, he is author of The Reckoning: Debt, Democracy and the Future of American Power, published in 2012 by Palgrave Macmillan. He is co-author of ‘The Fastest Billion: The Story Behind Africa’s Economic Revolution’. Moran served as Editor – in-Chief at the investment bank Renaissance Capital and has been a collaborator of renowned economist Nouriel Roubini as well commentator for Slate, the BBC and NBC News. He is also an adjunct professor of journalism at Bard College, a Visiting Fellow in Peace and Security at the Carnegie Corporation of New York, and conceived of and served as executive producer of the award-winning Crisis Guides documentary series for the Council on Foreign Relations.
China Development Bank could be a climate bank
Development Bank (CDB) has an opportunity to become the world’s most important
climate bank, driving the transition to the low-carbon economy.
CDB supports Chinese investments globally, often in heavily emitting sectors. Some 70% of global CO2 emissions come from the buildings, transport and energy sectors, which are all strongly linked to infrastructure investment. The rules applied by development finance institutions like CBD when making funding decisions on infrastructure projects can therefore set the framework for cutting carbon emissions.
CDB is a major financer of China’s Belt and Road Initiative, the world’s most ambitious infrastructure scheme. It is the biggest policy bank in the world with approximately US$2.3 trillion in assets – more than the $1.5 trillion of all the other development banks combined.
Partly as a consequence of its size, CDB is also the biggest green project financer of the major development banks, deploying US$137.2 billion in climate finance in 2017; almost ten times more than the World Bank.
This huge investment in climate-friendly projects is overshadowed by the bank’s continued investment in coal. In 2016 and 2017, it invested about three times more in coal projects than in clean energy.
scale makes its promotion of green projects particularly significant. Moreover,
it has committed to align with the Paris Agreement as part of the International Development Finance
Club. It is also
part of the initiative developing Green Investment Principles along the BRI.
This progress is laudable but CDB must act quickly if it is to meet the Chinese government’s official vision of a sustainable BRI and align itself with the Paris target of limiting global average temperature rise to 2C.
What does best practice look like?
In its latest report, the climate change think-tank E3G has identified several areas where CDB could improve, with transparency high on the list.
The report assesses the alignment of six Asian development finance institutions with the Paris Agreement. Some are shifting away from fossil fuels. The ADB (Asian Development Bank) has excluded development finance for oil exploration and has not financed a coal project since 2013, while the AIIB (Asian Infrastructure Investment Bank) has stated it has no coal projects in its direct finance pipeline. The World Bank has excluded all upstream oil and gas financing.
In contrast, CDB’s policies on financing fossil fuel projects remain opaque. A commitment to end all coal finance would signal the bank is taking steps to align its financing activities with President Xi Jinping’s high-profile pledge that the BRI would be “open, green and clean”, made at the second Belt and Road Forum in Beijing in April 2019.
CDB should also detail how its “green growth” vision will translate into operational decisions. Producing a climate-change strategy would set out how the bank’s sectoral strategies will align with its core value of green growth.
CDB already accounts for emissions from projects financed by green bonds. It should extend this practice to all financing activities. The major development banks have already developed a harmonised approach to account for greenhouse gas emissions, which could be a starting point for CDB.
Lastly, CDB should integrate climate risks into lending activities and country risk analysis.
One of the key functions of development finance institutions is to mobilise private finance. CDB has been successful in this respect, for example providing long-term capital to develop the domestic solar industry. This was one of the main drivers lowering solar costs by 80% between 2009-2015.
However, the extent to which CDB has been successful in mobilising capital outside China has been more limited; in 2017, almost 98% of net loans were on the Chinese mainland. If CDB can repeat its success in mobilising capital into green industries in BRI countries, it will play a key role in driving the zero-carbon and resilient transition.
From our partner chinadialogue.net
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