Financial Disintermediation - Reducing asymmetries between consumers and lenders

The World Economic Forum discussed the topic of financial disintermediation in 2015, then again in 2016 and in 2018, the forum declared that “blockchain can no longer be ignored”. The best starting point for understanding why financial disintermediation is such a talked about topic and what this has to do with blockchain is to get a clear definition of what it is.  

What is financial disintermediation?

As the word implies, disintermediation occurs when parties with excess funds (let’s call them ‘surplus entities’) directly transact with parties which are in need of funds (let’s call them ‘deficit entities’), rather than using an intermediary (i.e. a financial institution) to facilitate this process. Traditionally, financial institutions were important because they were able to diversify the risk of providing funds to deficit entities. In a world of disintermediated (or direct) transactions, it was hard for surplus entities to diversify as every time they had surplus funds and wanted to earn interest by providing funding to a deficit entity, they would have high search and research costs. Of course, surplus entities are also limited by the volume of funds they have. One deficit entity’s project may require all of the available funds of a surplus entity, or the surplus entity, on their own, may not have enough funds to get the deficit entity’s project off the ground. Thus, it is not hard to understand why financial intermediaries developed. These intermediaries could pool surplus entities’ funds (offering them a fixed interest rate on their deposit) and become experts in evaluating deficit entities’ funding needs. They could therefore diversify the risk of the entire portfolio of many surplus entities’ investments in all the deficit entities’ projects.  

Many of you are probably thinking, well, wait a minute, when companies do IPOs, they directly acquire funds from surplus entities. This has been occurring for ages, so this doesn’t explain why disintermediation is suddenly a hot topic. You are correct. The relative share of funding provided by the capital markets is generally considered to be a measure of how disintermediated the financial system in a geographic region is. Nonetheless, the systems which were developed to maintain records of who owns which small chunks of what assets used to be complex enough that this was only worth doing with big, valuable assets. Blockchains and distributed ledger technologies are now making this so simple that individual borrowers looking to buy personal real estate can ‘crowdfund’ their mortgage via a platform with a pool of capital providers, who are all willing to fund a small chunk of the mortgage. The platform then keeps track of interest payments and apportioning the loan repayment to the respective lenders.  

what does this really have to do with blockchain?

In fact, these kinds of peer-driven lending platforms which have sprung up as part of the disintermediation trend in recent years don’t even need to be built on blockchain technology. This can be done using other, modern technologies. There are also regulatory and structural drivers which have led companies to launch these sorts of platforms. The reason the regulators of major financial markets are particularly interested in blockchain is its potential to create a decentralised guarantee of trustworthiness. 

It used to be the case that the reputation of a bank was a guarantee of trustworthiness. Nowadays, it is common knowledge that even the largest banks are at risk of failing in a major financial crisis. The complex interdependencies in risk exposures between banks have led to various measures to evaluate the stability of banks. The most important of which is the Basel committee’s methodology which is used to identify ‘global systemically important banks’ (G-SIBs) and to require those identified as such to meet additional loss absorbency requirements. Nonetheless, this evaluation process still does not address the core of the issue of banks being systematically inter-connected in a complex and opaque web of exposures. In reality, it is still impossible to say where the next financial crisis will emanate from and what consequences it will have. 

To be fair, although the recent headlines about Australian banks have done nothing to inspire confidence in the reputation of our banks, none of our institutions are anywhere near being G-SIBs and Australia is highly unlikely to be the epicentre of the next global financial crisis. Still, there are reasons why having a decentralised, non-corruptible record of trustworthiness and creditworthiness might be useful. For example, the Australian government has announced the Australian Business Securitisation Fund to provide additional funding to smaller banks and non-bank lenders with the ultimate goal of encouraging lending to small businesses. This is because small businesses are an important part of the Australian economy – but they are often subject to neglect when it comes to lending as they are not necessarily the most attractive debtors for major banks to have on their books.  

If a small business could reliably communicate its creditworthiness to a pool of potentially interested private lenders – without having to actually expose all of its financial information to every Tom, Dick and Harry who would be an interested lender – this could help the small business to get access to credit on a more flexible basis. It would also open up a whole new world of investment opportunities for private investors, as some of the peer-to-peer lending platforms have started to do, particularly in the mortgage space. This is all possible with a clever blockchain implementation. It’s use cases like these that threaten to make banks obsolete and which will change the game for regulators of the financial system. It is both daunting and exciting in terms of the potential it holds for a whole new world of finance. 

“Blockchain threatens to make banks obsolete and to change the game for regulators of the financial system. It is both daunting and exciting in terms of the potential it holds for a whole new world of finance.”

Our view (and hope) is that the difference between the emerging business models and how we have conducted finance fore the past few hundred years will be that the information asymmetry that has existed in favour of financial institutions will be mitigated. It is currently still the case that banks ask anything they want to know about you - but you don’t know what they know about you or what they think of you. Financial disintermediation and blockchain technology holds the power to give consumers the reigns.

Blog by:

Dr. Christina Kleinau & Alan Hsiao

The Ethics of AI - Technologies change, ethics stay the same

The concept of ethics has existed for about as long as humans have been humans. Although nowadays, many people are disinclined to become entangled in discussions of ethics, whenever a new discipline or technology emerges, the question as to what the ethics of that technology are will inevitably be asked. Such is the case with the ethics of AI [1].

The current consensus around the ethics of AI is that we can build on the four basic principles of bioethics. These are beneficence, non-maleficence, autonomy, and justice. There is also a general tenor that we should augment that list with a fifth principle about ‘explicability’ [2]. Realistically, this was probably always an implicit component of the application of bioethics. Nevertheless, just as when a new CEO comes in, they are obliged to change something about the company, so too when a new technology comes along, we feel compelled to try to re-define the multiple thousands of years old discipline of ethics which has guided us thus far.

To be fair, the application of ethics in any new discipline is a topic which should be discussed and re-discussed. Humans have never agreed on a single, clear definition of what ‘ethics’ is - but we have still been generally pretty good at agreeing on what we definitely do not want to happen. Examples of what not to do in terms of ethics of AI include:

• Not disclosing sensitive information

• Not creating opaque applications, whereby the users and the creators don’t exactly understand what they do

• Not using AI to enhance activities we generally consider to be unethical, such as stealing

Nonetheless, the core problem of ethics has always been bridging the gap between ‘knowing’ and ‘doing’. We can agree on the above examples. The general tenet about what is wrong and right is something that humans have an innate sense for – even if many different words and concepts can be used to describe that.

The question is - How do we ensure that the innate ethical ideals we have been following for centuries are implemented in practice for AI?

• The first thing is to ensure that discussions on ethics are held, even if it is difficult to agree on specific terminology.

• The second thing is to use those discussions to ask pragmatic questions about the concrete applications of AI, rather than trying to put labels on lofty ideals.

Aristotle would want you to ask yourself – Is this action consistent with what I consider to be virtuous behaviour? Use whatever labels which come to mind when you ask yourself that question (honest, integral, just, whatever). Other versions of this question include – Would you still do this action if you had to explain it to your mother or your daughter tomorrow? Would you do it if it were on the front page of tomorrow’s newspaper?

Immanuel Kant, the philosopher who defined deontological (read: rules-based) ethics, would want you to ask – Is this action universalizable? That is, if everyone in the world decided to do this action tomorrow, would that be logically possible? Taking an example from the finance world - momentum trading, it would actually fail this test. Momentum trading lives off the assumption that other market participants have identified fundamental information and are trading on that basis. If everyone only conducted momentum trading, so noone is actually conducting fundamental research, this assumption would not hold. Thus, momentum trading is unethical. Obviously, no one is physically harmed by momentum trading, but financial markets are more prone to bubbles (boom and bust cycles) because of it. There is a general consensus that bubbles are bad because they mislead the productivity of the real economy. This question about the universalizability of an action is likely to be helpful in many AI applications. Often, people will not be directly harmed by AI - but if you have a general feeling of unease about a certain use case, this may be your problem.

Utilitarian ethicists would ask - Does the sum of the benefit of the action outweigh the sum of the negative consequences of the action? This utilitarian idea underpins modern economic theory (yes, economic theory is a practical derivative of ethical theory) and it works quite well in an economic context. Monetary gains and losses can be neatly summed and negated. It becomes more challenging when the benefits and drawbacks leave the economic domain and enter e.g. the social and environmental domain. Still, the main thing is to discuss the list of pros and cons and take a decision you feel comfortable with, on balance.

John Rawls did subsequently invent what is known as contractualism, a school of ethics which focuses on the idea of the social contract and asks – Does conducting this action generate the greatest possible benefit for the person in society who is the worst off? This idea recognises that justice can’t mean that everyone is entitled to the exact same life circumstances. Instead, we have to somehow ensure that the people worst off in society would still consent to the social contract of that society. In terms of the classic question as to whether an autonomous car should run over the elderly person or the baby, we could say the person who dies is the worst off. Arguably, a person would prefer to be run over as an elderly person than as a baby. It’s a tough call to make – but ethics has always been about making decisions in tough situations.

Further reading & references:

[1] This website collates existing attempts to define ethics of AI: https://algorithmwatch.org/en/project/ai-ethics-guidelines-global-inventory/

[2] This publication summarizes the five principles of the ethics of AI and makes recommendations: https://link.springer.com/article/10.1007/s11023-018-9482-5

Blog by:

Dr. Christina Kleinau