The Fuzziness of Financialization

In 1982, the Financial Times adopted a new corporate jingle: “No FT, no comment.” The television advertisement related to this slogan showed a predatory fish trying, unsuccessfully, to catch a puffer fish. As the puffer fish gets away, the male voice-over states, “You don’t have to read the FT every day, but you can be sure you’ll have to deal with people who have.” The advertisement was a great success: It went on not only to win 3^rd place in the Advertising Slogan Hall of Fame,¹ but also entered popular culture when it was uttered by Francis Urquhart/Frank Underwood,² the central character in the popular television series House of Cards, as a response to every controversial question that was asked of him (“You might very well think that but I couldn’t possibly comment”).

Part of the slogan’s popularity at that time was due to the fact that it reflected a cultural consensus. As the Reagan-Thatcher epoch unfolded, the privatization of state enterprises, coupled with the deregulation of markets and the dominance of junk bond capital, meant that finance was now entering all aspects of public and private life. As a primary source of financial information, “No FT no comment” embodied a reality where it would be judicious to check what the FT was saying about a subject or an investment, and thus educate oneself to be a player or a spectator in this new era. The popularity of the phrase represented a growing intellectual pattern that was immersing households and non-financial firms in the financial market.

Having become the de factoreference and provider of economic and financial information, the FT repeated this representation of societal mindset change again a quarter of a century later. In 2007, just before the crisis, the FT released its new advertisement campaign with three distinct images to signify globalization, mergers and acquisitions, and entrepreneurship. All three images carried their new slogan, “We live in Financial Times,” almost alluding to the fact that irrespective of the sector or size of the industry, finance was omnipresent. A year later, as the crisis unrolled, their billboard advertisements showed a Saint Bernard carrying a copy of the newspaper in place of the lifesaving flask of brandy on its collar to promote its essential role during the economic downturn (Sweney, 2008).

These FT slogans represent the ascendency of finance into all aspects of our affairs. This phenomenon has been and continues to be referred to as “financialisation” and the Figures 2-1 and 2-2 show its ascent.

Figure 2-1. Relative industry shares of employment in US economy, 1950–2001

Figure 2-2. Relative industry shares of corporate profits in US economy, 1950–2001 Image source: “The financialization of the American economy” (Greta R. Krippner, 2005)

FIRE: Finance, Insurance, and Real Estate. Note the curve similarities of the Services and FIRE industries. Image source : “The financialization of the American economy” (Greta R. Krippner, 2005).

The definition of financialization differs from context to context. The Oxford dictionary defines it as, “The process by which financial institutions, markets, etc., increase in size and influence.” But any process is based on a series of events. Hence, financialization needs to be looked as not as an existential realism, but more as a gradual build-up of events.

As the volume, diversity, and turnover of financial instruments has grown faster than the real economy, the financial sector has grown faster than the real economy (Smaghi, 2010). As a result, markets and institutions have exercised a growing influence on the conduct of managers of non-financial firms (Zorn, 2000), and households and non-financial firms have become progressively tangled in financial products and markets. Furthermore, as financialization continued its widespread infiltration, it was widely cited as a good thing under the guise of being a mechanism that allowed us to tame risk. As stated by Ben Bernanke in a speech to the Eastern Economic Association in 2004,

“One of the most striking features of the economic landscape over the past twenty years or so has been a substantial decline in macroeconomic volatility…”

Along with this cultural transition was the talismanic standing rendered to the importance of credit for economic growth. But, as seen in the previous chapter, the issuance of increased amounts of debt leads to debt overhang and the shifting of the debt burden from private to public sectors. In addition, sustainable, continuous growth in a consumerist society requires the constant purchase and consumption of goods. The availability of cheap credit meant that those with revenue increases that were out of sync with economic growth could be persuaded to borrow instead in order to maintain a certain lifestyle.

Thus, the collective effect of these trends has meant that an increase in economic performance depends on a disproportionate increase in consumer debt. It is for these reasons that household debt has risen faster than that of the general level of economic activity, and why every successive economic cycle requires a bigger dose of household debt to stimulate economic activity. When seen in conjunction with the previous statements and the topics described in Chapter 1, the net result has been the omnipresence of financialization in every fiber of industrialized societies. As per Thomas I. Palley of the Levy Economics Institute, the impacts of this change have been:

1. the elevation of the significance of the financial sector relative to the real sector ³;

2. the transfer of income from the real sector to the financial sector;

3. the increase of income inequality and contribution to wage stagnation.

While it can be extrapolated that these behaviors could increasingly threaten social cohesion, it must be remembered that the cultural infringement of finance is only part of the reason behind the growth of financialization. Finance, after all, is to be looked at as a catalyst that allows for the efficient production and distribution of goods and services. This function is executed by the provision of credit and the allocation of risk between economic agents who are best suited for these roles, thus resulting in the appropriate allocation of resources within the economy.

However, as it has been discussed, since macroeconomic theory only considers the production and exchange of goods and services involving “real” variables, money and credit have been considered to occur in a separate analytical sector. As a result, finance has been treated as a fuzzy veil behind which was the “real” exchange of “tangible” and societally impactful products. It is for this reason that macroeconomists have generally paid little attention to the workings of financial markets. The amalgamation of cultural transitions and the suprising disregard of formal macroeconomic academic investigation have in part led to the growth of financialization.

Financialization and the Innovation of Risk

No discussion about financialization can be considered complete without a conversation of its principal component , risk. The aspect of risk is at the heart of financialization. One of the functions of financial institutions is to act as an intermediary between economic agents who have unequal or unaligned objectives or who wish to hedge themselves from risks such as credit or interest rate fluctuations. By acting as a counterparty to these agents, financial institutions take risk on their shoulders and, in doing so, provide a service by which they earn a revenue in response. But the growth of financialization has had a curious effect on the redistribution of risk. Rather than simply being redistributed, risk has undergone a transformative process and become a product in and of itself which can now be traded and sold as an actual good.

From the 1980s onwards, as non-financial firms increasingly became involved in financial markets, the incentive attached to those participants willing to take on the responsibility of risk gave rise to a set of esoteric and increasingly complex derivative products (Partnoy, 2009). The cumulative effect of this development meant that non-financial firms now began diverting from their core activities, such as the manufacturing of goods, to financial activities, such as the provision of credit for acquiring those goods, which was the territory of traditional financial institutions.

However, the transference of roles was not all bad news for financial institutions. As an increasing number of consumers took on debt to purchase products, it meant that the growth of the non-financial institutions had to be leveraged by taking on more debt. As the spiral of debt continued, the risk attached to loans grew in volume, to the point that it was now necessary to address the situation. It is here that financial innovation came to the rescue. As the power of banks decreased due to the issuance of debt by non-financial institutions, the banks installed a new risk management practise via their activities in exchanging securities and specifically through CDOs (Collateralised Debt Obligations ), Collateralized Loan Obligations (CLOs) and CDSs (Credit Default Swaps ).

Previously, banks controlled the amount of risk in the economy by providing or withholding credit. This was referred to the as the “originate-to-hold” model (Santos, 2012) of risk management. However, with the birth of CDOs, CLOs and CDSs , new possibilities were discovered. Under the originate-to-hold model, banks limited the distribution of risk to mortgages, credit card credits, and auto and student loans. However, with CLOs,⁴ they were now provided with another venue for distributing the loans that they originated. As a result, the traditional originate-to-hold model grew into an “originate-to-distribute” model based on their corporate lending business. Further financial innovation in the form of CDOs and CDSs caused these models to evolve into a “originate, repackage and sell” model (Ansart & Monvoisin, 2015).

Financialization based on the production and exchange of risk has created transactions involving credit operations which are endogenous to the financial markets. As these transactions involve the transformation of risk into financial products, what has been observed is the slicing and dicing of risk products that multiplies the nominal value of the financial instruments in circulation (Davison, 2015). As a result, they manifest themselves in the form of increased financial turnover, bigger balance sheets, and growing revenues in financial markets. The exponential growth of the transformation of risk via increased financialization has led to a proliferation of financial claims and obligations, as a result of which a growing percentage of total wealth now exists not in the form of real assets but in the form of financial assets or claims by creditors. Its growth has also led to banks getting too big to fail.

TBTF

The term Too Big to Fail (TBTF) has captured the headlines since the crisis. But the growing popularity of this term should not come as a surprise. In the same way that financialization can be considered as a process of events, TBTF was a looming event being built to fruition well before the crisis. Indeed, a number of noted economists, including Raghuram Rajan,⁵ Dean Baker,⁶ Steve Keen,⁷ Ann Pettifor,⁸ and Nouriel Roubini⁹ (Cooper, 2015), had predicted the inefficiencies of financial markets, warned us about increasing private debt levels and wrote about the impending crash that was coming our way. All these economists also warned us about the increasing financialization of the economy, the deregulation of the market, the rising debt levels of households, and the risk of a recession. They were systematically and categorically ignored.

The reason for this dismissive behavior was first based on the exclusion of financial markets from macroeconomic models (See Blanchard et al., 2010). Second, there was also a cognitive dissonance and logical fallacy associated with financialization. As finance was omnipresent in every part of our society, it had achieved a sense of trust. If you could not believe in free markets and the appropriate allocation of risk when everyone else was, then what could you believe in?

This ideological kidnapping of economic theory, market policies, and societal mindsets occurred due to a monetary thought experiment now referred to as the Washington Consensus. Under this ideology, market discipline and self-regulation would be sufficient to ward off any serious problems in financial institutions, and if left to itself, the financial system would not only allocate resources more efficiently, but also redistribute risks better. Not only was this belief widespread, it was also contagious—the IMF, the World Bank, the Federal Reserve, the ECB, and the Bank of England were all infected by the same belief system (Ülgen, 2015). Hence, Lehman Brothers and AIG became TBTF because it was inconceivable that they could fail (Admati and Hellwig, 2013).

As it can be inferred from the term, the Washington Consensus, the growth of TBTF finds its roots in the annals of an American financial history story. Owing to the popularity and acceptance of financialization, over the past 40 years, the notion that oversight of the financial industry was unnecessary was entrenched in society. In 1998, Alan Greenspan stated that, “participants in financial markets are predominantly professionals that simply do not require the customer protections that may be needed by the general public.” A year later, the American Congress almost unanimously voted in favor of the Gramm-Leach-Bliley Financial Modernization Act that removed the specialization of banks by repealing the Glass-Steagall Act and ending the prohibitions against the intermingling of commercial and investment banking activities. As stated by the Republican Senator Phil Gramm (who spearheaded the Gramm- Leach-Bliley Act in 1999), “We have learned that government is not the answer. We have learned that freedom and competition is the answer” (Myers-Lipton, 2009). It was erringly similar to what Reagan had said in his inaugural presidential speech almost 15 years ago (see Sidebar 1-1 in Chapter 1).

As the banks were given freer rein to grow within the economy, this also aided them in expanding across geographies. For most of the 20^th century, they had been constrained in terms of geographies of growth because the McFadden Act of 1927 prohibited nationally chartered lenders from establishing branches outside of their states of incorporation. At the same time, the 1933 Glass-Steagall Act separated commercial from investment banking while the Bank Holding Company Act of 1956 extended the same prohibitions to bank holding companies, which had been developed to circumvent the restrictions against interstate banking (Maxfield, 2013).

Beginning in 1994, the US government began to relax the regulations with the Riegle-Neal Interstate Banking and Branching Efficiency Act which ended the geographical limits on banking activity and thus the McFadden Act (Ansart & Monvoisin, 2015). What started as a decision to allow interstate banking quickly gained momentum with the development of regional banking mergers. The aforementioned Riegle-Neal Interstate Banking and Branching Act of 1994 removed federal impediments to interstate banking and, in the process, ignited a series of mergers of like-sized banks. Coupled with the 1999 Gramm-Leach-Bliley Financial Modernization Act , it led to the rise of JP Morgan Chase, Citigroup, Bank of America, and Wells Fargo, among others (Maxfield, 2013).

As a result of the structural changes over the past 40 years and the accompanying financialization of business and society, today’s markets are composed of large, complex, and highly leveraged companies, immersed in a sector where securities are financially engineered and linked to derivative instruments . This interconnected lattice is often touted to represent the adaptive and innovative nature of finance as it keeps pace with main street entrepreneurial innovations.

For instance, proponents of big banks state that large banks encourage the widespread adoption of new financial innovations, as they have a large customer base. Large institutions are thus better positioned to spread the costs of investment in a technology over more users, allowing them to offer new technological innovations at lower average costs than their new entrants. This in turn allows banks to offer economies of scale, as they offer clients a plethora of services under a single umbrella. It is for these reasons that banks undertake in mergers-and-acquisitions, as it allows them to harvest opportunities in providing customers with a range of transaction-related services, such as financing, risk management (in the form of derivative products), and foreign exchange, among others. It is hence more economically efficient for banks to provide numerous services in combination.

Although this is true to a certain extent, the stark fact is that banking innovations affect monetary and fiscal conditions on which the entire economy is girded, and thus changes in this sector affect the economic conditions to a greater degree. While large banks might offer greater efficiencies, their size also comes with more process, more red-tape, increased amounts of opaqueness, and larger mistakes.

Consider the case of the J.P. Morgan’s “London Whale” episode in 2012. As the traders executed their hedging strategy by entering into a series of derivative transactions involving credit default swaps (CDS) , one of the JP Morgan traders, Bruno Iksil, accumulated outsized CDS positions in the market and began distorting the market with massive bets. As other traders in the CDS market began to notice this activity, they moved in the opposite direction and began to take positions that were contrary to the J.P. Morgan positions. At the same time, they called foul and, following an investigation, at the end of which J.P. Morgan, the largest bank in the USA, suffered $6.2bn in trading losses (Scannell, 2016).

The inquiry of the incident further highlighted the inefficiency that is at the core of large institutions. When questioned about his actions, Iksil responded that not only were his actions in 2012 authorized, but that he was instructed repeatedly by the CIO and senior management to execute this trading strategy (FT, 2016). As per his interpretation, the investment office in London tried to sidestep capital regulation laws of risk management by fulfilling the bare minimums of regulatory requirements. Traders were thus given the incentive to score big, and therefore, instead of focusing on simplicities, the traders focused on the complexities of derivative markets and ignored the danger signals provided by the stress tests (Forelle, 2012).

The London whale incident is just one of the many financial scandals that have involved the TBTF banks following the crisis. Since 2008, HSBC has been involved in the LIBOR scandal, Standard Chartered in money laundering transactions, and JP Morgan, Citigroup, Bank of America, RBS, Barclays, and UBS (also known as the “Bandits’ Club ”) were all involved in rigging the Forex market (Independent, 2015). Between 2010 and 2015, Barclays, HSBC, the Lloyds Banking Group, and the Royal Bank of Scotland have together incurred costs of £55.8bn to cover conduct and litigation issues, after being penalized for rigging Libor and foreign exchange markets, and for the misselling payment protection insurance (PPI ) incident (Treanor, 2016). When talking in terms of efficiency, it’s surprising how often advocates of big banks seem to leave these incidents out of their sermons.

What these incidents show us is that large banks are capable of making large mistakes. While there are benefits of scope and scale, such institutions are also harder to manage and oversee. Hence, the size of the banks and their stronghold on the direction of monetary and fiscal policy is a topic worthy of public discussion owing to the influence and the implications of the actions of banks that are TBTF.

#Ending TBTF ¹⁰

It could be argued that breaking up the banks would not yield better results. Advocates of big banks often ask questions along the following lines when engaging in this repertoire:

• Had the banks been broken into smaller pieces two decades ago, would it have stopped the crisis?

• Is there a success probability in terms of financial stability that is calculable by breaking up the banks?

• Even if we break up the banks, then to what extent do we break them up?

• And is this break to be done on the basis of activities or in terms of asset size?

• Who will decide how to break up a bank?

All of these questions are valid and necessitate responses. If we are to ask for a break-up of banks, then the criteria for the breakup needs to be anchored to a measure of the systemic risk generated by the bank. This in turn would help us understand what the threshold of organic growth of a bank needs to be before it poses systemic risk, and what needs to be done when it reaches this limit. If splitting institutions by specialty or business functions reduces their efficiency, then what are the quantitative arguments against it? As it stands, most responses to the questions posed by fans of big banks are based on subjective judgments. This is not to say that they are unusable, but it must be recognized that more quantitative proof needs to be gathered in order to avoid the implementation of unambiguous guidelines to breaking up an institution. We will explore these quantitative methods in later parts of the book.

From a more ideological outlook, it could also be argued that markets should be allowed to make mistakes as part of the innovative process . This is a seemingly logical rationale, especially when taking into consideration the way the innovation mantra is being chanted across every sector and industry today. Indeed, this has even happened in the past. In the 1980s during the savings and loans crisis (S&L crisis), 1,043 out of the 3,234 savings and loan associations ( FDIC, 2000) failed and affected millions of everyday investors. In 2000, the bursting of the technology bubble did affect investors and technology in general. Yet none of these failures posed systemic risks and came at the cost of a financial meltdown. The plumbing of the financial system and its connection to other institutions ensure that large, complex financial organisations are systemically important financial institutions (SIFI¹¹) that pose risks to the financial system and the economy.

It is for these reasons that the conversation of ending TBTF has been reverberating and gaining momentum in public and private anterooms. But as it can be seen, it is two-sided, with differences in opinion in terms of the cost, scope, and the scale of TBTF and ending TBTF. As the conversation stagnates, the biggest banks are still TBTF and continue to pose risks to the global economy.

It must be remembered that TBTF was not the sole cause of the crisis. But the job losses, home foreclosures, lost savings, and the costs to taxpayers following the bailout of banks represent the financialization of our societies and showcase the presence of banks that are TBTF in the center of our financial system while highlighting their significant contribution to the magnitude of the crisis and the extensive damage that it perpetrated across the global economy.

This is not to say that nothing has been done in order to address the issue. Following changes in regulation, large banks and SIFIs are now required to hold greater amounts of capital and have larger sources of liquidity. As a result of the Dodd Frank Act , firms identified as SIFIs are subject to stricter oversight from the Federal Reserve, have to partake in stress tests, write a bankruptcy plan known as a living will, and meet stricter capital requirements. All of these measures and a number of other changes were the result of the Dodd-Frank Act which was passed to limit systemic risk, allow for the safe resolution of the largest intermediaries, submit risky nonbanks to greater scrutiny, and reform derivatives trading (Lopez and Saeidinezhad, 2016). Following the crisis, these measures have been enacted not only in the USA, but also across other markets. As per the Basel III accord, large banks in every country are required to have higher capital requirements, annual stress tests, additional capital mandates, and new liquidity and asset-liability matching requirements (Bipartisan Policy Center, 2014).

Thus, stress tests done by regulators today gauge whether the most centrally important institutions can withstand external and internal shocks to the economy. To ensure that banks can fail without requiring massive taxpayer bailouts, regulators have adopted the use of the “Living Will¹² Review” process , which makes banks essentially think about their demise, and forces the banks to describe their company, their risk exposures, as well as their strategy for reorganizing themselves in bankruptcy without causing financial instability or using taxpayer dollars. If the banks fail to persuade regulators that they have a realistic and safe plan for winding down, the government can use Dodd-Frank’s Orderly Resolution Authority to resolve them outside the court system if they get into trouble. In other words, banks have enormous incentive to make sure their Living Wills are convincing.

All these measures are important and significant progress has been made. Table 2-1 provides a summary of some of the goals defined by the Dodd-Frank Act and the degree to which they have been implemented. But despite these efforts, banks can still run into trouble and questions arise as to whether these efforts are sufficient, with the urgency of implementation fading as the crisis fades from memory. There is also the question of cost, scope, and scale: While the Living Will does look at the threat posed by a bank, it fails to consider the massive asymmetrical risks to society from a bailout and the widespread external effects of the failure of a large bank on the rest of the economy, which include lost jobs, lost income, and lost wealth. To complicate the issue, the Dodd-Frank Act does not include any objective thresholds or standards for living wills. For example, there is no consideration given to the conditions under which the bankruptcy plans must work: in the midst of an economic boom when the firm fails in isolation, or in the midst of a financial crisis when many firms fail collectively. These are two very different situations and require very different plans (McCloskey & Kupiec, 2014).

These shortcomings were discussed by Neel Kashkari, the new president of the Federal Reserve Bank of Minneapolis, in a recent speech (February 2016) at the Hutchings Center at the Brookings Institute. In the speech, Kashkari questioned the usefulness and effectiveness of the measures and tools currently at our disposal and enquired if they were sufficient to deal with a future crisis, especially since we have no idea about what form it could take. In this public discussion, he presented two scenarios:

Scenario One: Individual large bank runs into trouble while the rest of the economy is sound and strong.

Scenario Two: One or more banks run into trouble while there is broader weakness and risk in the global economy.

In Scenario One, as per Kashkari, the aforementioned measures would allow us to deal with the failure of an individual large bank without requiring a bailout, but we don’t know that for certain as the work on these measures is far from complete (refer Table 2-1). For example: A review of the Living Wills show that they have significant shortcomings¹³ and do not insure that the failure of a particular bank will not lead to massive fallout. Until this work is complete (which can be years from now), he states that we must acknowledge that the largest banks are TBTF, and expresses doubt regarding the efficacy of the measures, stating that we won’t know how useful they are until we use them.

He further went on to state that the situation in case of the occurrence of Scenario Two was even more dire. As per the Dodd-Frank Act, regulators have inordinate control on the restructuring of companies if their Living Wills are deemed unfit, and can require an institution to restructure, raise capital, reduce leverage, divest, or downsize. Given the other external costs of the bailouts (job losses, lost income, and lost wealth), Kashkari states that no policy maker will advocate for large-scale firm restructuring, as this would adversely affect creditors and shareholders if these measures were deployed in a fragile and risky environment.

Based on his experience as a “policy maker on the frontline responding to the financial crisis in 2008,” Kashkari pushes for breaking up the large banks into smaller, less connected, less important entities. Based on the risks posed by TBTF, he states that large institutions ought to be treated with a legislation system that is akin to that of the rules that govern the operation of a nuclear power plant and pushes for treating large banks as public utilities by forcing them to hold so much capital that they virtually cannot fail. Lastly, he believes in taxing leverage throughout the financial system to reduce systemic risk.

Evidently, there are oppositions to these statements, as many regulators continue to sing the praises of the progress made in the last six years. Shortly after Kashkari’s statements, Janet Yellen, the current chairman of the Federal Reserve, issued a statement that took a stance against the regional Federal bank president, stating, “I certainly have not arrived at the conclusion that my colleague has…. I’m pleased with the way things are going” (Heltman, 2016 ). Three months after Kashkari’s speech, Ben Bernanke published a blog post titled “Ending ‘too big to fail’: What’s the right approach?”, in which the former two-term chairman of the Federal Reserve argued against major structural changes that forced the break-up of large firms, stating that large firms have cost advantages, greater diversification of risk, the ability to spread overhead costs over a variety of activities, and the capability to offer multiple interconnected products and services at a global scale. He went on to state, “Even putting aside the short-term costs and disruptions that would likely be associated with breaking up the largest banks, in the long run a US financial industry without large firms would likely be less efficient, providing fewer services at higher cost. From a national perspective, this strategy could also involve ceding leadership in the industry, and the associated jobs and profits, to other countries” (Bernanke, 2016). Kashkari riposted to these statements by questioning the cost-benefit trade-off and by asking if the benefits of scale of large banks outweigh the massive externalities of a widespread economic collapse.

All three central bankers cited above provide different levels of perspective on a singular issue. Nevertheless, they all agree that TBTF does pose systemic risks and has to be addressed. The point of divergence seems to be with regards to what is the optimal level of fragmentation that is needed for an economy to function efficiently while reducing systemic risk. Just as the last chapter raises the question of what is the optimal amount of debt that is acceptable to have, we are once again faced with a similar predicament with respect to what level of fragmentation is ideal for the spreading of risk in the economy?

It is important to underline the word “optimal” in the above statements. Judging an optimal level is a complex and complicated endeavor, given that markets are highly interconnected, densely opaque (shadow banking), and increasingly rarified. Often the blame is directed at regulators for not being able to determine what this optimal level is, even after almost a decade since the crisis. But given the challenges and limitations that befall regulators, maybe a different manner of looking at the subject is required in the search for answers.

Before we delve into the investigation of how to determine the optimal level, it is important for the reader to understand that there are reasons for taking a stance favoring the breaking of TBTF. However, this stance is not being taken in order to turn the readers’ thinking towards a certain bias. It is being done in order to look at the conversation from a much-needed different point of view. This is because, first, we already have TBTF. So from an investigative perspective, it makes sense to explore the other extreme. Second, although Kashkari pushes for ending TBTF,¹⁴ his arguments are grounded in legislation and are challenged by others who base their statements on past laurels. Hence, Kashkari’s hypotheses need to be tested via the scientific method. Third, as it will be shown in the next sections of this chapter, the fragmentation is already underway, with and without the blessings of regulators.

With these tasks in hindsight, we can now go about the challenge of understanding the fragmenting of an industry. To help us understand whether there are structural benefits to the fragmentation, we will need to see if this has occurred in the past in other sectors, as this provides us with some frame of reference. To this purpose, Sidebar 2-1 summarizes a case-study done by the Deloitte University Press titled “From monopoly to competition,” which showcases how fragmentation of the US telecommunications industry worked in increasing competition, quality, and efficiency of the market.

The banking industry is certainly not the telecom industry, but it can be seen that their market structures certainly bear elemenets of commonality. In light of the above stated remarks and with a retrospective view of the lessons learned from the telecommunications sector, it is thus safe to say that the fragmentation of the financial markets is an objective worth pursuing. But owing to the breadth, depth, and extraordinary role that this sector plays in all aspects of business and the economy, it would be obtuse to believe that a “plug-and-play” approach would be satisfactory to breaking TBTF.

To come to a more logical conclusion about breaking TBTF, we first need to comprehend what forces are currently changing the makeup of the sector, and based on elements of commonality, what takeaways could be applied from the experiences other sectors. Second, we must determine why we are so fixated with a regulatory approach to changing the composition of the sector when most of the change today is occurring due to technology. Third, we need to ask ourselves that if we are going to undertake an endeavor which will have large-scale effects on society, should we follow the models of the past, or do we need to move our gaze from regulatory and historical analyses, to understanding the behavioral complexity of societal groups owing to the multiple spillover effects?

A new way of looking at fragmentation

Is it not curious to note that the solutions being discussed with regards to TBTF are highly concentrated in the realm of regulations? Why is it that when we talk about the complexities of such socially intricate institutions that we do not use the research findings and lessons from group behaviors as seen in evolutionary biology or sociology? Would it not be prudent to take into consideration the lessons from the past with regards to how decisions are made in complex systems and then hunt for indications that the same conditions are being created in our ever-changing financial ecosystem? Is the realm of finance that different from other human intrinsic ecological systems?

The reason for this regulatory-centric approach to solving TBTF is certainly political in nature. But it also the result of how the centralization of banking structures has occurred over the past few centuries. From its infancy in the 17^th century,¹⁵ the banking system has been constructed around a central bank and second-range banks around it. As a result, the banking framework has grown as a structure of strong, hierarchical institutionalization, and the construction of these edifices has mobilized governments to create elaborate, regulated, banking frameworks. The irony of this structure is that while banks have fundamentally contributed to the development of democracies, they by themselves are least representative of this trait and the increasing liberty given to banks has resulted in them becoming more unequal (Ansart & Monvoisin, 2015), with only a handful of banks¹⁶ being considered TBTF . As a result, any talk of fragmentation resonates with haunting tones of disestablishmentarianism.

But in light of the current inefficiencies, maybe what is truly required is a new way of looking at ending TBTF that is not regulatory in nature. As the arsenal of policy makers is increasingly tested, some recent studies now say that mainstream finance is acknowledging the existence of a world beyond equations and regulations and is moving towards behavioral finance (Davison, 2015). A recent comment by Andy Haldane, chief economist at the Bank of England, bolsters this statement: “Truth be told, the workhorse model in economics and finance, God bless it, does come with some strong simplifying assumptions, some of which mean it’s not often well-equipped to deal with situations of stress” (Nordrum, 2016).

Haldane made this statement in February 2016, shortly after the publication of an article (of which he is one of the co-authors) in the major scientific journal, Science. Having acknowledged the insufficiencies of current economic models, the authors, who are experts in finance, sociology, and physics, state that traditional economic theory could not explain, much less predict, the near-collapse of the financial system. The subjects of complexity economics and econophysics, (which will be analyzed in detail in the last chapter), offer regulators a host of insights. While terms and concepts such as tipping points, networks, contagion, feedback, and resilience have entered the financial and regulatory lexicon (Battiston et al., 2016), the learnings from these subjects are not being used when considering how to build a better model from an economic or regulatory standpoint.

For example, when talking about tipping points, the authors of the article state, “Analyses of complex systems ranging from the climate to ecosystems reveal that, before a major transition, there is often a gradual and unnoticed loss of resilience. This makes the system brittle: A small disruption can trigger a domino effect that propagates through the system and propels it into a crisis state…. Markers include rising correlation between nodes in a network and rising temporal correlation, variance, and skewedness of fluctuation patterns. These indicators were first predicted mathematically and subsequently demonstrated experimentally in real complex systems, including living systems.

A recent study of the Dutch interbank network showed that standard analysis using a homogeneous network model could only lead to late detection of the 2008 crisis, although a more realistic and heterogeneous network model could identify an early warning signal 3 years before the crisis. Ecologists have developed tools to quantify the stability, robustness, and resilience of food webs and have shown how these depend on the topology of the network and the strengths of interactions. Epidemiologists have tools to gauge the potential for events to propagate in systems of interacting entities, to identify superspreaders and core groups relevant to infection persistence, and to design strategies to prevent or limit the spread of contagion.”

With this introductory analogy to natural ecosystems and the functioning of networks, the article goes into deeper detail to showcase how the current network topology of systemically important banks and the systemic risk tied to the interconnectedness between banks could lead to the collapse of the global financial network even if individual banks seem to appear safe.

This is because, first, the banking network consists of a number of banks that have relatively similar business and risk models and whose defaults tend to be highly correlated. Banks realize that in a situation of distress, they are underwritten and likely to be supported by the United States government and the governments of Europe (Freeman, 2011 ). As a result, they tend to structure themselves along similar lines and this causes a herd mentality that reduces the diversity of behavior, notably when dealing with risk.

Second, there is information asymmetry within the banking networks as banks do not share their information with each other. Thus the tools, such as the aforementioned stress tests and Living Wills, being developed to reconstruct the network and estimate systemic risk, are done using partial amounts of publicly available information. These tests would be greatly improved if the banks publicly reported more data and informed us of their connections with other banks. But it is not the case today. As stated by hedge-fund manager Paul Singer, “The opacity of financial institution financial statements has not been addressed or changed at all… Rumor and feeling is all you have. You don’t know the financial condition of [Citigroup], JPMorgan, Bank of America, any of them… We have a very large analytical research effort here and we have not found anybody that can parse the sensitivity of big banks to changes in interest rates, asset prices, and the like. You can’t do it” (Freeman, 2011 ).

Coupled with these limitations is the fragility caused by individual nodes. The complexity article goes on the state that “systemic repercussions of the failure of individual nodes…shows that the issue of too-central-to-fail may be more important than too-big-to-fail” (Battiston et al., 2016).

Hence, although Kashkari and others who are pushing for the end of TBTF are right in generating scenarios, what needs to be done is to think about the concept of ending too big to fail from a more multidisciplinary perspective. Using the scientific methods cited in the aforementioned article would not only give the regulators the ability to simulate more scenarios, but would also help answer the questions of those critics who have defended the current structure of the financial system under the pretext of advantages offered by economies of scale.

The concept of fragmentation by itself is thus worthy of deep exploration, for if we are to legitimize the breaking up of banks, then it becomes mandatory to determine whether larger or smaller groups function better in an interconnected economy. Without this understanding, we have no logical ground to justify fragmenting an institution that is a societal functionary and even less chance of determining what is the optimal threshold of fragmentation.

To find a response to the optimal threshold of fragmentation, we first turn our attention to the pith and marrow of what the banking network is. When boiled down to its essence, the network is essentially a large group of individuals who are collaborating with each other for maximising individual benefit and curtailing risk. When looked at from this perspective, the question then becomes how group behaviors—such as collective actions or decision making—are done, based on experience and how these behaviors emerge and persevere in an evolving system.

Thankfully, research from the field of evolutionary biology offers us a response to these questions. In another recent paper published in Nature titled “Small Groups and long memories promote cooperation,” researchers show that wherever social interactions play a part, organisms behave differently depending on their social environment and their past experience. By developing a framework that looks at the evolution of multiple players exchanging “public goods,” the researchers demonstrated that when groups were small, longer memory strategies made cooperation easier to evolve, as they allowed for an increase in the number of ways cooperation could be stabilized until a point of optimal stability was reached. By exploring the co-evolution of behavior and memory,¹⁷ they found that that even when memory had a cost, longer-memory strategies often evolved, which in turn drove the evolution of cooperation, even when the benefits for cooperation were low (Stewart & Plotkin, 2016).

The reason for citing the findings from evidently different disciplines of study is because they offer problem-solving approaches when we think about fragmentation. Moreover, the findings from the Science and Nature journals are industry- and technology-agnostic. For example, as we saw in Sidebar 2-1, the US telecommunications industry went through a series of transformations that changed the way the industry defined itself. All of these changes involved the entry of new players, old players redefining their business model, introduction of new technology, and the removal of monopolies. Over the past 30 years, as competition and openness increased, not only did the sector see a myriad of new entrants, but in terms of services offered, it cannot even be compared to what it was before. From the days of dial-up Internet access , the industry has grown to offer free encrypted texts (WhatsApp) and free calls (Skype). The change has not only resulted in creating metrics-saturated, hyper-efficient business models, but it has led to a change in the mindset of society. Ask a teenager to surrender their smartphone for a day and watch the symptoms of a panic attack set in.

Thus fragmentation does seem to be the antidote to the current economic malaise . It might be argued that based on the opinions of certain academics, business magnates, and policy makers, there is no certainty that breaking up the banks will save us. But much in the same way that technology feeds on technology to create new technology, the introduction of an unfamiliar concept is bound to raise doubts. Hence, it is important to recognize and acknowledge that some readers might find this intermixing of findings from other subjects unsubstantiated and divergent from the subject at hand. But this is exactly the point of this book. In light of the inefficiencies and ineptitude of regulatory filibusters, what is needed is a new way to look at the question of fragmenting TBTF.

If this by itself is not enough reason to support the fragmentation argument, then there is one final reason worth considering: With the advent of technological progress and increasing support for entrepreneurial initiatives, the fragmentation of the financial sector is already underway. You may have heard of this fragmentation is less sinister terms… In today’s popular culture it is goes under the aliases of FinTech or Blockchain.

Sharding

In early 2015, at the World Economic Forum in Davos, the current governor of the Bank of England, Mark Carney, told a room full of the most influential voices in business and economics that we’re now looking at “an Uber-type situation” for banking (Edwards, 2015).

Carney, who is Canadian in origin, is a person worth listening to not only because of his past achievements in the private sector ,¹⁸ but also because he is in a truly unique position today. As the 120^th governor of the UK’s Central Bank, he had to act quickly to stem the panic in the aftermath of Brexit, Britain’s referendum vote to leave the European Union. The list of challenges he faces today is intimidating: he needs to preserve London’s position as a global financial hub, prevent a downward spiral of lack of market confidence following Brexit, equalize the UK’s economy, avoid making a recession a depression, and continue to fan the flames of entrepreneurship in the City, the financial district of London. In regular circumstances, a central bank governor would be content to take on one or two of these objectives. So to say that he has a full plate is an understatement.

A little over a year after his speech at Davos, Carney once again made some thought-provoking remarks at a speech given on the 16^th of June, 2016 at the Lord Mayor’s Banquet for Bankers and Merchants of the City of London.¹⁹ In his speech, titled “Enabling the FinTech transformation : Revolution, Restoration, or Reformation?”, Carney went on to state that FinTech and Blockchain could transform the global financial system and UK economy. Here below are a few extracts from his speech, which shed light on his vision of the future and which, coincidently, also touch upon a few topics discussed previously in this book:

• “FinTech…will change the nature of money, ²⁰ shake the foundations of central banking, and deliver nothing less than a democratic revolution for all who use financial services.”

• “FinTech [has the] potential to deliver a great unbundling of banking into its core functions of settling payments, performing maturity transformation, sharing risk, and allocating capital. This would mean revolution, fundamentally reshaping the financial system.”

• “…Some financial technologies could make incumbent banks more efficient and profitable, reinforcing existing economies of scale and scope in banking. This would mean a restoration, reinforcing incumbents’ power.”

• “The balance of these forces may yield a third alternative—a reformation—a more diverse, resilient, and effective system for consumers. One where large banks exist alongside new entrants who compete across the value chain.”

The speech goes on to describe five steps that will be put in place simultaneously over the course of the year to enable the FinTech transformation of banking. These steps include the testing of new proofs of concept, the use of a distributed ledger, and the launch of a FinTech accelerator which will help boost the partnership between the Bank and selected FinTech companies. The point of displaying excerpts of Carney’s speech is not just to show his forward thinking mentality or his vision of the future of finance. It is to show that the fragmentation of banking is already underway in the more subtle and decorous guise of technological change.

FinTech is short form for Financial Technology . Over the greater part of the past decade, new technology firms have been able to leverage digital technology to develop financial services and banking products that are more customer-oriented, cost less to deliver, and which are native to digital networks. As these players are less fraught by stringent regulatory compliance, which banks are subject to, they have greater space to move in specific markets. Unburdened by complex and complicated legacy information systems, they have a greater degree of technological flexibility, which makes them more adaptable to changes in markets. Being small in size, they generally focus on a single product or service and place the emphasis on customer ease of use. Lastly, they are more in tune with the peer-to-peer (P2P) culture that has formatted the social media generation in the past decade. The result of their flexibility, low costs, and user-focused strategies have resulted in rapid popularity, staggering successes, and immense growth.

Globally, investment in FinTech ventures climbed to $22.3 billion in 2015, up from $1.8 billion in 2010 and $12.6 billion in 2014 (Accenture report, 2016). 2016 may hold greater promise, though, as Fintech investments in Q1 2016 surged 67% compared to the same period in 2015, to reach US$5.3 billion (Fintech Innovation report, 2016). The first quarter of 2016 saw thirteen rounds of VC funding to FinTech companies that crossed the $50 million mark, a slight rise from the ten rounds of similar amounts that were seen in the fourth quarter of 2015 (KPMG report, 2016). Moreover, the growth is global. While North America leads the way, Asia (notably China) and Europe are increasing VC funding into this space (see figures 2-3 and 2-4).

Figure 2-3. Global Fintech financing activity (2008–2014) Image source: “Cutting through the noise around financial technology,” McKinsey (2016). Data source: CB Insights

Figure 2-4. Global Fintech financing activity (2010–2015) Image source: “Fintech and the evolving landscape: landing points for the industry,” Accenture (2016). Data source: CB Insights

The reason for this widespread investment is because of the widespread impacts in a variety of financial services. FinTech inventions are affecting services such as lending, payments, asset management, transactions, capital markets, trade finance and even insurance. The Blockchain can also be roped into this basket case as it is fundamentally a technology that is native to the exchange of value. But for the case of simplicity, we will consider it separately, owing to its uniqueness.

As it can be imagined, this level of investment has also generated curiosity at different levels of business and academia. My first piece work on the Blockchain was put online in 2014. Since that time, there have been over fifty books, just in the English language, published on FinTech and Blockchain. Try conducting a search on Amazon with the keywords “FinTech” or “Blockchain” and observe the results. It would be pointless to even try and count the number of blog posts on these subjects over this time period. Even on a single platform such as LinkedIn Pulse, the results are too many to count. Coupled with the innumerable conferences, speeches, and round-tables, it is almost impossible for a researcher with my qualifications to come up with an original text on the subject that mentions some technical point about these topics that has not been mentioned before.

It is for these reasons that it is not the primary goal of this book to explain these technologies in a granular level of detail. As most of the books published today focus on the applications of the technology, they explain the technical underpinnings in incredible detail and offer sharp business insights based on decades of specialist acumen. Thus, rewriting a text on these subjects would be repeating the same endeavor with negligible marginal utility. As we are more focused on the implications of this technology from a macroeconomic context, we shall stick to this objective.

Nevertheless, prior to understanding the implications, we ought to have a quick revision on the current usages of these technologies. Sidebars 2-2 and 2-3 provide brief notes on FinTech and Blockchain, respectively, and showcase the reason why they are bringing about a revolution in the industry. For those readers looking to gain more specific technical knowledge, a list of recommended books worth consideration is provided in the notes section at the end of this chapter.

*SWIFT: Society for Worldwide Interbank Financial Telecommunication

**RTGS: Real Time Gross Settlement

Using the Skeleton Keys

What the condensed insights in Sidebars 2-2 and 2-3 provide is the evidence that the financial ecosystem is being transformed and fragmented by actors and technologies that are exogenous to the traditional financial sector. Mark Carney’s statements about FinTech changing the nature of money and central banking thus hold sway as the fragmentation is happening at every level of this sector.

Moreover, the fragmentation process is occurring at two echelons. While FinTech innovations are more concentrated on providing front end solutions that offer better benchmarks for speed, agility, and user- friendliness, Blockchain and Smart Contracts are bringing about change at the infrastructural level by providing better security, transparency, and automation of operations. This two-sided attack from the front-end and the infrastructure side of financial operations are the reason why the fragmentation of finance is occurring at a blitzkrieging rate and why investment has been increasing in the sector.

It is for this reason that when we talk about the Blockchain, it is imperative that we also consider the other side of the FinTech coin. One of the first things I mention to students when giving lectures about the Blockchain is to tell them to “Cool it with the Blockchain,” for as we have seen in Sidebar 2-2, there are a range of other mature technologies which are fragmenting and changing the financial sector in ways that are more significant at the current time period. The Blockchain is not a panacea; it is an infrastructural technology that should be looked at as part of a toolkit. This toolkit is made up of all the technologies cited in Sidebars 2-2 and 2-3, and when they are used together, they will create the infrastructural foundation for the next chapter of financial services.

Consider the case of the payments and remittances sector . As mentioned before, the cost of using a non- bank remittance provider can be frightfully expensive. If a user intends to use their bank for the transfer, then the average cost involved is 11.12% of the sum being transferred (World Bank, 2015). While it cheaper to use a post office (5.88%), it depends on the country where the money is being transferred. Sending money to countries in sub-Saharan Africa and China remain very expensive, and the average cost of conducting a transfer using any channel totals to 7.37% (World Bank, 2015), assuming that the receiver has a bank account. With over two billion people still considered unbanked, this is not always an assumption that can be generalized. With profit margins being this high, it is unsurprising to learn that the global payments market is growing at 5% a year globally. Yet inspite of these profits, micropayments are not feasible, owing to the fees involved in making payments, and as per Stephan Thomas, CTO of Ripple, 2% of wire transfers fail, take over two days to settle, and cumulate to an annual cost of $1.6 Trillion.

The reason for high transfer fees is because of the number of players involved in transactions and the regulatory compliances each player has to respect. Currently when making a transfer, apart from the sender’s and receiver’s banks, the flow of money includes the involvement of non-bank companies (such as Western Union), the correspondent bank which deals with foreign exchange, the clearing networks (such as SWIFT and ACH ²¹), and regulators from central banks and financial authorities that monitor KYC and AML standards. As there are many separate players involved, the information about the sender needs to be verified by each participant, which results in repetitive business processes, accumulated costs, delays, errors, and multiple operations.

However, as we have seen in Sidebars 2-2 and 2-3, this system is currently in a state of flux. Companies like M-Pesa are allowing the unbanked to send and receive payments without depending on the traditional players, and companies like Transferwise ²² are reducing the FOREX risk that is involved in cross-border transactions. If the Blockchain were to be inculcated in this value exchange process, it would further streamline the entire transaction. Senders and receivers would still have to go through a KYC process to have a digital wallet and identity, but once this has been verified, there is no need to repeat the verification process at every step. A smart contract can be generated that stipulates the conditions of the transfer between the counterparties, and currency conversion can be done via liquidity providers who are willing to facilitate the conversion or by adopting methods similar to Transferwise. As the smart contract executes, the transaction would occur in real time and the entire history can be verified by a regulator on the Blockchain. This would automate compliance, insure trust, reduce settlement times by orders of magnitude, and reduce costs for the end users. The only losers in this process would be the current intermediaries who presently perform the role of inter-operators.

Lending institutions also face changes from the amalgamation of the technologies described in Sidebars 2-2 and 2-3. When business entities require loans to grow their businesses, they face similar procedures and obstacles, as seen in making transfers. The lender or lenders first need to conduct a manually intensive due diligence process to ascertain if the business is sound and financially healthy. This involves analyzing a multitude of data sources, which is time-consuming and prone to errors. As the underwriting process is often not integrative of the due diligence process and often involves depending on third-party providers, it leads to duplication of efforts, additional costs to the investors, and information asymmetries owing to the siloed structure of the entire operation.

However, companies like Kabbage are showing us that via the use of Big Data analyses, these steps can be automated. Based on the digital footprint of the company, the lending institution can use advanced analytics to perform its due diligence with a better understanding of the risk involved, with a higher level of transparency, and with less manual intervention at higher speeds. Apart from a better matching of counterparties based on acceptable risk levels, using a smart contract can further tailor partnering criteria based on programmable selection conditions, hence reducing the time for receiving a loan. Big data also allows the analysis of other attributes of financial information, which allows investors to gain a more holistic understanding of the company’s character. Using smart contracts, this automated due diligence can then be leveraged to automate the underwriting process, which reduces execution time and reduces the number of supplementary intermediaries that are currently employed, thus reducing operational risks and information asymmetries .

The P2P lending sector is already making strides in this direction to connect savers and lenders. Companies like MoneyCircles connect savers with borrowers who are part of their social circles. Lenders can create or join circles based on their Gmail, Facebook, Twitter, or LinkedIn networks and loan money based on their criteria. The conditions are embedded into a smart contract and the transactions occur via a blockchain. The smart contract also ensures the disbursement of the principal and interest payments over the loan period. As the transactions occur in real time over the loan cycle, regulators are provided with a real-time view of the financial details, which simplifies the KYC/AML procedures . As it stands, MoneyCircles is regulated by the UK Prudential Regulation and Financial Conduct Authorities.

While machine learning and robo-advisors are changing the investment landscape, compliance, auditing, and regulatory costs continue to surge (Noonan, 2015). It is in this sector that banks, taxation bodies, and regulators stand most to gain by the adoption of Blockchain, as the technology has the potential to provide them with a heightened enforcement tool. As things stand, regulators and auditors are required to be provided with annual financial statements and access to systems to conduct risk assessments and stress tests. In case of any discrepancies, a process is launched to determine the scale of the issues. Based on the audit report, the institution then issues its quarterly/annual report to investors. This process is resource- and time-intensive, as it affects the day-to-day operations of the company. Owing to the lack of interoperability of IT systems, the auditing process is manually intensive and prone to errors.

However, with a Blockchain the process can be streamlined across multiple platforms. As auditors and regulators are provided with authorized access, the institution’s employees and leadership do not need to be intrinsically involved in the auditing process. Using the data from the Blockchain, the auditing of the institution can be done in real time with a smart contract supplying the information to the auditors’ reporting instruments at predetermined periods. This allows for faster assessment, precise tax determination, quicker detection of discrepancies, and easier enforcement of new regulations. As startups like FundApps provide regulatory information in machine-readable language, the next step in this direction would be to allow a smart contract to make changes in regulation as an input, and adjust the investment and reporting procedures based on these changes. This would reduce time delays in the execution of new regulations, provide greater transparency of financial records, and allow sovereign regulatory bodies with automated capital analysis. It would also allow investors to make more informed decisions. Chapter 3 of Dan Tapscott’s book (see notes for reference), provides a detailed understanding of how auditing firms will be affected by the Blockchain.

One of the most important uses of Blockchain in investment markets will be in the area of asset rehypothecation . Financial institutions often participate in this practice wherein an asset posted as collateral by a client is used by the financial institution for its own purposes, such as getting a loan. Clients who permit rehypothecation may be compensated either through a lower cost of borrowing or a rebate on fees. However, there need to be limits to which this process can be done, as excessive rehypothecation creates a collateral chain, misallocates the asset, and attaches additional risks to the collateral. The ambiguity of ownership of the asset and the ballooning of counterparty risks thus requires a mechanism which allows for tracing the transaction history of the asset. However, today’s systems do not provide this level of insight, as secondary trading markets are not required to detail the transaction history of assets. As a result of this lack of transparency, regulators are unable to track securities and investors are unable to determine the true value of an asset, as each rehypothecation leverages a percentage of the collateral. If there is a default, the snowball effect then corrupts the entire transaction chain and can have spill over effects in other sectors. This was seen in gargantuan proportions during the financial crisis.

The Blockchain’s ability to tokenize physical assets is thus an invaluable tool to investors and regulators. As the asset is tokenized, its transaction history can now be traced on the Blockchain with a smart contract enumerating its value and ownership as it begins to get rehypothecated. Regulatory limits can be set and encoded based on the percentage value of the asset once it is rehypothecated a certain number of times, with the smart contract restricting additional rehypothecation when the limit is reached. This provides investors and regulators with a transparent view of the asset’s real value and if the investment firm is violating any stipulations. In sum, it provides a clearer view of the derivatives market.

In the world of AI-led high frequency trading , generic algorithms might allow for the development of automated learning strategies and execute trades in nanoseconds. Yet the post-trade processes are time-intensive and take up to T+3 days to settle. As the trades are executed in bulk at the end of the day and involve the involvement of intermediaries such as custodian banks, clearing houses, and securities depositories, the efficiency of the trade between the buyer and the seller is adversely impacted. This time lag affects the actions that investors can take over this period.

The Blockchain is perfectly suited to overhaul this process, as the transaction of securities is tailored for a ledger. Counterparties can use a smart contract to “book” a trade, and the liquidity and the tokenized asset (bond/equity) can be transferred to the Blockchain. Once the conditions for the execution of the smart contract have been met, the smart contract would execute and the exchange of the asset for the funds can be conducted on the Blockchain. A real-time transaction notification can then be sent to the counterparties’ banks and custodians, which would allow for faster settlement time, reduce operational complexity, remove intermediaries, and improve the efficiency of the clearing processes.

The unglamorous yet immense insurance industry is another financial sector where the Blockchain can have significant impact. As mentioned in Sidebar 2-2, fraudulent claims pose a significant risk in the insurance industry. While new P2P insurance models are beginning to address this issue, owing to the reputational effects within a group, the Blockchain can help in dispersing the same practice in other branches of insurance as well. Currently, false claims are related to the multiple versions of truth that are associated with claims. When an insured reports a claim, they normally have to interact with a broker who confirms the validity of the claim. This is followed by a second assessment by the insurer who verifies the associated documentation from the broker and additional sources. This multi-step process is customer-unfriendly and based on the “one-size-fits-all” package. Intermediaries are costly, and manual data verification from third-party data providers increases the risk of fraud, the amount of time, and the resources necessary to process a claim.

With the Blockchain and IoT, this process can be simplified to a great extent. Sensors (for life or property insurance) can submit data to trigger a claim based on predefined conditions. Using data analysis and smart contracts, the due diligence and analysis process can be automated, codified, and executed. When additional data sources (such as weather conditions) need to be verified, this can be included and automated as well. The payment can then be made via the smart contract if all conditions are met.

All in all, across the various financial services, the use of Blockchain and FinTech can lead to simplified operational procedures, lesser risk, lower liquidity requirements, fewer intermediaries, higher transparency, better regulatory oversight, and easier multi-stakeholder agreement.

In spite of this potential, the large-scale deployment of the Blockchain has been hindered for two main reasons. First, as traditional analyses of macroeconomics have been based on methodologies that do not consider the actions of the financial system, it is increasingly difficult for macroeconomists to gauge the consequences of these changes using the existing methodologies and theories. It is for this reason that we must tackle the task of understanding the future impact of these changes by depending on the learnings of other disciplines, such as evolutionary biology and complexity science, as they are more suited to this task in spite of their relative bifurcation from a subject matter context. This is why the findings regarding the behaviors of micro-groups in complex systems hold so much pertinence; for as the influx of investment and players augments, we require a new methodological process that helps us adapt to this change at a rate at which it can be cognitively absorbed. Without a firm understanding of new theories that provide this ability, we have no compass with which we can navigate these frothy waters.

Second, the large-scale deployment of the Blockchain also necessitates a conversion towards a more digital system. As we have seen, the current processes are largely manual in nature and still involve the use of physical documents and person-to-person interactions. For the Blockchain to be effective at a societal level, it needs to overcome these physical barriers , namely with respect to:

1. digital identity and KYC

2. scalability

Both these topics will be addressed in relative detail in the final part of this chapter.

The Enemy of my Enemy is my Friend

“Good derives its virtue from evil, just as it is the silent pause that gives sweetness to the chant.”

—St. Thomas Aquinas

As the fragmentation of the financial sector continues, incumbents and startups find themselves as competitors in the same race as market share continues to be broken up. As per a recent PricewaterhouseCoopers report, more than 20% of financial services business will be at risk to FinTechs by 2020. The study also estimates that 28% of the banking and payments sector and 22% of the insurance, wealth management and asset management sectors will belong to the FinTech firms by 2020 (PwC, 2016). According to Autonomous Research, online lenders originated $22 billion in U.S. consumer and business loans in 2015. While this represents only 6% of the total volume for such loans, the sector is expected to grow 75% in 2016, the financial research firm estimates. Goldman Sachs analysts have predicted that banks stand to lose 100% of the student, consumer, and mortgage loan business to online lenders over the next five years (Crosman, 2016).

While 57% of incumbents are not sure or unlikely to respond to Blockchain technology (PwC, 2016), the remainder are taking bold steps to inculcate this technology for obvious reasons. Studies by Goldman Sachs estimate that Blockchain could reduce transaction costs in underwriting insurance by $2–$4 billion, just in the US. By applying Blockchain to streamlining clearing and settlement of cash securities (equities, repo, and leveraged loans), they estimate the industry could save $11–$12 billion in fees, OpEx, and capital charges globally (Schneider et al., 2016).). As the Blockchain also offers a tamper-proof way of storing records, it could ease the process required to know a customer and reduce the number of “suspicious” transactions, resulting in $3–$5 billion in cost savings for KYC (Know Your Customer) and AML (Anti-Money Laundering) compliance (Schneider et al., 2016).

As P2P platforms begin to the change the role of the consumer into a prosumer, the growth of business models that do not have to depend on a central point of authority is gaining stead. As the Blockchain is suited to this environment and is digitally native, its continued use in various forms is bound to increase. While the current total worth of bitcoin in the Blockchain is around $20 billion, the growing popularity and use of Ethereum²³ and Ripple is tipping the scale, and the World Economic Forum estimates that by 2027, up to 10% of the global GDP will be stored on the Blockchain (WEF, 2015). Some skeptics still stubbornly state that, owing to regulatory and operational limitations, this technology will remain on the fringes of finance and exist as just a cryptocurrency. But the evidence shows that it is much more. It is a protocol, a commodity, an automation system, a secure exchange, a ledger, and a community that is creating a counter-culture movement.

At the same time, incumbents and startups face different challenges. While banks invest more heavily in innovation (e.g., Bank of America has a $3 billion annual innovation budget), they still need to find ways to diffuse their innovation strategies across their organizations, yet retain market share. To this end, banks have begun to collaborate with each other to develop new platforms with similar standards to update their legacy infrastructure; one of the main objectives of the R3 consortium is to address the issue of standards for global cross-border Blockchain use. Startups, on the other hand, are trying to find ways to gain customers and scale their business while facing increased regulations, higher costs, and larger infrastructures that will be more difficult to change and manage.

As a result, just as it was mentioned in the evolutionary biology article, banks and FinTech startups are now working together to figure out ways to remain competitive but still (re)gain market share. At the end of 2015, JPM Chase partnered with OnDeck to begin providing quick, small loans to SMEs. In May 2016, UBS partnered with SigFig to provide robo-advisory services to their wealthy clients (Yurcan, 2016). While Kensho functions as an independent technology firm with partnerships with a host of banks, it was initially funded by Goldman Sachs and continues to provide services to them (Nadler, 2016).

As banks continue to evolve from their traditional nature towards a modern outlook, the partnering and alliances will still continue to fragment and metamorphose the sector. This will inevitably be accompanied by new risks and challenges. The hurdles are not just in term of regulations but also in terms of policies. As financial markets get increasingly fragmented by technology, they are also getting increasingly cashless. Thus, future policy for a decentralized and fragmented future needs to adapt to this facet.

In a cashless world, how are KYC and AML compliance measures to be administered when signing up to online platforms? What kinds of regulations need to be developed to aid FinTech firms to continue growing while giving them the leeway to innovate? How is the production of money going to be affected in the fragmented market? What kinds of regulations need to be put in place in order to stop TBTF and can these regulations be adapted to comprehend new technology? For example: Try going to a regulator today and saying, “The exchange of this asset or product is sound and valid because its transaction was done on a cryptographically secure and time-stamped distributed ledger that exists on a hybrid permissioned-permissionless Blockchain.” In terms of adapting regulations to this new environment, regulators are going to be in need of a stiff drink.

But the sobering realization is evident: today we live in a financial quagmire that is besotted with institutions that are TBTF. As technology continues to converge with the changing mindset of society, the emergence of new enterprises is fragmenting the financial sector and calling for academics, organizations, and regulators to begin perceiving the economy via a new lens. This call has been heard by innovators and is now being echoed by central bankers such as Andy Haldane, Neel Kashkari, and Mark Carney. But the main roadblock today seems to be not with technology but in the way regulations and polices need to be structured in order adapt to this new environment. As stated by Jake Kendall, the former director of research and innovation at the Gates Foundation,

“…. [in terms of scaling democratizing financial services], a lot of it is not a technology problem but a regulatory issue or a business model issue,” (Microsoft Research Conference, 2016)

Hence, prior to answering the questions of how much debt should be issued and how big can a firm be allowed to become before it poses systemic risks, we need to understand how the regulations and policies that govern these institutions need to be fabricated while keeping our future cashless economy in foresight. This will be seen in the next chapter.

Challenges and Solution Pathways

As banks and financial institutions begin to gauge the applicability of Blockchain technology, both from the private and public utilization diasporas, the manner in which identity is verified and shared is found to be lagging in context. Know Your Customer (KYC) and Anti-Money Laundering (AML) procedures that are typically used by financial players and governments seem to be at odds with the anonymous and pseudonymous representative possibilities offered by distributed ledger technologies.

Likewise, the scalability issue is a priority technical concern and different efforts such as improving proof-of-work/proof-of-stake protocols, consortium consensus, construction of sidechains, and sharding are currently being explored. It is quite possible that given the investment and current interest in Blockchains that a solution might have been found by the time this book is published. Recent advances by Gavin Wood²⁴ with the Polkadot Framework (see “Polkadot: Vision for a Heterogeneous Multi-chain Framework”) and ByzCoi.

The following two sections review these issues.