AI & Finance

The implementation of computers into different finance processes is nothing
new; high speed trading and the dominance of algorithms in the markets is a trend
that has been discussed, analyzed, and reported on at length. 

In areas such as fraud detection, risk management, credit rating and wealth advisory, AI is already augmenting or even replacing human decision makers. In fact, not deploying AI capabilities in these fields can be considered disastrous. Withthe ever-increasing amounts of data that needs to be processed, AI systems are a must-have to improve accuracy.

The key point to remember during this conversation is that, as computers become increas-
ingly sophisticated there will also be drawbacks. As increasing amounts of trading
are connected to computers, programs, and algorithms that operate without direct
human oversight and intervention there is a possibility that large swings in the mar-
ket (that volatility word) will become more frequent. 

As technological capabilities continue to improve, the amount of available data grows, and competitive pressures mount, the use of AI in finance will be pervasive. However, as with any new technology the adoption of AI brings its very own set of challenges. There are a number of concerns often cited by regulators, customers and experts which can be grouped into the following categories:

  • Bias
  • Accountability
  • Transparency

Potential causes of Bias:

  • An AI model is biased when it takes decisions that can be considered as prejudiced against certain segments of the population. One might think that these are rare occurrences – as machines should be less ‘judgmental’ than humans. Unfortunately, as has been proven last year, they tend to be far more commonplace. AI failures can happen to even some of the largest companies in the world.
  • How do these biases happen? One reason why algorithms go rogue is that the problem is framed incorrectly. For instance, if an AI system calculating the creditworthiness of a customer is tasked to optimize profits, it could soon get into predatory behavior and look for people with low credit scores to sell subprime loans. This practice may be frowned upon by society and considered unethical, but the AI does not understand such nuances.
  • Another reason for unintended bias can be the lack of social awareness: The data fed into the system already contains the biases and prejudice that manifests the social system. The machine neither understands these biases nor can it consider removing them, it just tries to optimize the model for the biases in the system.
  • Finally, the data itself may not be a good representative sample. When there are low samples from certain minority segments, and some of these data points turn out to be bad, the algorithms could make some sweeping generalizations based on the limited data it has. This is not unlike any human decisions influenced by availability heuristics.

Accountability Challenges:

  • The question who’s responsible if AI makes a wrong decision. If a self-driving car causes an accident, should it be the fault of the owner who didn’t maintain the car correctly, or did not respond when the algorithm made a bad call? Or is it purely an algorithmic issue? What about our previous example of predatory pricing – within which time frame is the firm employing this algorithm supposed to know that something is amiss and fix it? And to what extent are they responsible for the damages?
  • These are very important regulatory and ethical issues which need to be addressed.There are risks related to the technology which need to be carefully managed, especially when consumers are affected. This is why it’s important to employ the concept of algorithmic accountability, which revolves around the central tenet that the operators of the algorithm should put in place sufficient controls to make sure the algorithm performs as expected.
Missing Transparency:

  • Many algorithms suffer from a lack of transparency and interpretability, making it difficult to identify how and why they come to particular conclusions. As a result, it can be challenging to identify model bias or discriminatory behavior. 
  • It’s fair to say that the lack of transparency and the prevalence of black box models is the underlying cause for the two challenges outlined above.

From anecdotal evidence and review of market commentary, it does seem that the
increasing technological dominance of trading may be leading to several different

First, while volatility while judged by historically levels, has been at low levels
in the 2015–2018 time period this does not provide the entire picture. The decrease
in volatility may not, as some has speculated, be associated with the increased effi-
ciency generated by algorithmic trading programs, but rather a related trend. ETFs,
passive investing tools, and the growing (trillions as of this writing) assets invested
in these options may also be having an outsized impact on volatility and training
patterns. Put simply, as larger and larger percentages of investors and funds are
investing in similar, if not identical, trading tools and platforms, this may very well
have a depressive impact on market volatility. 

This may very well seem like a positive effect to retail investors with jitters linked to increases in market volatility, but masks an underlying problem. If investing decisions are made outside of human

oversight and supervision this can inadvertently lead to market selloffs, runoffs, and
other actions that do not reflect the underlying economic reality.

This is a tremendous opportunity for financial advisors, planners, and other advi-
sory focused finance professionals to offer real time, real world, and actionable
business insights to clients and customers in a market that can seem as it operates
outside the realm of normal possibility. Volatility, although depressed during 2017,
seems to have returned to the market with force in 2018, emphasizes the important
of having a professional behind the wheel of various automated services and pro-
cesses. Simply executing certain processes, trades, and business transactions faster
will offer no benefit to either the organization or clients if those said processes are
poorly written or designed. 

In order for practitioners to effectively leverage technology they must understand not only how the technology itself works, but also how it can – and should be – applied to the business decision making process itself.

Another area where can, and already is, having an impact on the financial ser-
vices landscape is the realm of ad hoc and management reporting, which constitutes
a rather large percentage of the actual work performed by professionals working in
the space. Generating reports for management and supervisors simultaneously
forms a plurality of work performed by many accounting professionals and a way
that professionals can quantitatively add value to the organization. Despite of this,
one of the key issues raised and problems associated with internal management
reporting, or ad hoc reporting, is that data is not generated consistently, systems do
not communicate with each other, and there are inevitably time lags between when
different classes of information are generated. 

In the context of accounting professionals seeking to elevate both themselves and the work performed internally, the amount of time spent correcting errors, manually adjusting entries and
information deprives professionals of the time necessary to instead focus on higher
level activities. In other words, if accountants are spending too much time manually
creating reports and fixing errors, those same professionals will never be able to
achieve the oft-cited role as strategic advisor or business partner.

Audit and attestation work, discussed previously and to be expanded upon
throughout this text, represents a prime area where artificial intelligence will have
an impact on the profession. Currently, the entire process of auditing has several
pain points, namely the fact that the final audit opinion is heavily (if not exclusively)
reliant on expanding on findings generated from a small sample of organizational

Even with the subsequent analytical procedures and substantive tests
added into the audit examination process, audit failures are all too common. AI
tools, such as those represented by the partnership between IBM Watson and
KPMG, are already having an dramatic impact on audit testing, procedures, and
how auditors interact with both clients and future clients. This evolution and transi-
tion, from a compliance oriented function that focused exclusively on financial
information, to a more comprehensive process that can operate on a continuous
basis also connects to several other trends. Introduced here, but examined in more
detail later in this book, the connection between assurance work, non-financial
information, and the importance of this data to the decision making process opens
up a proverbial work of opportunities for accounting practitioners.

Tax reporting and the discussion of taxation issues are normally not associated
with pleasant news or something that management professionals, but that is not some-
thing that should be perceived as the final state of the conversation. Specifically, and
even in the current environment beset by changes in tax reporting, this debate and
analysis can, and should, be perceived both as an opportunity and part of the continu-
ous management dialogue. Put simply, although the Tax Cuts and Jobs Acts was
passed right at the end of 2017 – December 22nd to be specific – the ripple effects as
a result of this legislation are still being analyzed and processed by both individuals
and organizations. Processing the sheer number of changes, running scenario analy-
ses, and putting the results of these analyses into a format and report that are under-
standable for management decision making is both a role accounting professionals
should play, and a function enabled by AI tools. Taxes have an impact on the bottom
line, will continue to guide investment and operational decisions moving forward, and
will play a prominent role in the implementation and analysis of AI.

For financial institutions, it is clear that guidelines need to be put in place to help avoid bias, ensure safety and privacy, and to make the technology accountable and explainable. AI doesn’t have to be a black box – there are ways to make it more intuitive to humans such as Explainable AI (XAI).

XAI is a broad term which covers systems and tools to increase the transparency of the AI decision making process to humans. The major benefit of this approach is that it provides insights into the data, variables and decision points used to make a recommendation. Since 2017, a lot of effort has been put into XAI to solve the black box problem. DARPA has been a pioneer in the effort to create systems which facilitate XAI and it has since gained industry-wide as well as academic interest. In the past year, we have seen significant increase in the adoption of XAI, with Google, Microsoft and other large technology players starting to create such systems.

There are still challenges to XAI. The technology is still nascent. And there are concerns that explainability compromises accuracy, or that adopting XAI compromises the IP of the firm. However, the success of AI will depend on our ability to create trust in the technology and to drive acceptance among users, customers and the broader public. XAI can be a game changer as it will help increase transparency and overcome many of the hurdles that currently prevent its adoption.