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The growing impact of artificial intelligence on financial institutions
Artificial intelligence (AI) is increasingly becoming more important as financial institutions adapt the technology in order to digitise, and keep up with competition; however digital transformation is seldom an easy task especially when proper measures are not taken to safeguard against the threat of cyber criminals

August 28, 2018 | Daniel Wagner
  • Some financial institutions are continuing to use 1980s-era computer technology and are finding it difficult and costly to transition to the 21st century
  • AI should be embedded in customer-facing applications, but also in the back-end, to protect against fraudulent activity and cyberattacks
  • Regulators must maintain a watchful eye on the impact of AI on the financial markets, which will diminish the importance of evaluating the fundamental financial performance of companies and gradually increase market volatility

The initial phase of the fintech revolution was the digitisation of processes and procedures. The next phase, which is already underway, is the cognification of that digital infrastructure. Financial Institutions (FI) are positioned to experience an enormous amount of disruption as a result of the corresponding rise of artificial intelligence. New risks will emerge that must be managed, further dramatic change will occur in how FIs function, and additional consolidation will likely occur in the industry. This will be prompted by the adoption of AI-driven technologies and the desire to acquire more data. In the future, the acquisition and manipulation of data will transform how FIs (and other organisations) determine their own value.

This may ultimately result in creation of a new accounting standard - the Enterprise Value of Data – which could become an integral part of financial statements, capturing the value of the largest and most ignored corporate asset: data. Meaningfully defining and incorporating EvD on to balance sheets will help ensure that corporate accounting and risk management standards incorporate an AI-driven future. It will also put firms that are not specifically data-oriented on an operational equilibrium with those that are. However, those FIs that fail to embrace their own AI future will become less relevant, competitive, and profitable in time.

As fintechs build digital platforms that can accumulate data in a way that is conducive to implementing AI, traditional FIs are busy trying to keep their aging technology systems from crashing. Hard as it may be to believe, some FIs are continuing to use 1980s-era computer technology and are finding it difficult and costly to transition to the 21st century. Those FIs should, at a minimum, build more data lakes (large repositories that can hold structured and unstructured data) as an interim measure, but soon enough, they will have fallen so far behind the leaders in the FI industry that there will be no hope that they will ever catch up. That is probably already the case.

While massive amounts of data are one key prerequisite for FIs to become viable contenders in the race of AI supremacy, the data must be in a location where it can be processed according to the rules of Big Data. FIs may therefore need to rely on external sources to harness the power of Big Data through parallel computing. Some FIs may opt to implement advanced software within their own data centers and manage the parallel computing process themselves. Others may choose to leverage cloud providers to scale up their computing capacity, which raises questions such as, what data can be sent outside of the organisation and what data cannot, and must the data first be anonymized? FIs will be grappling with such questions for many years to come.

The increasing digitisation of banks is a double-edged sword, however, which needs to be handled with care. On one hand, FIs must digitise all of their operations as a matter of survival in the new age of the digital economy because consumers demand it. Yet, when an FI digitises its end-to-end operations it opens itself up to an even greater extent to new and fast-moving fraud and cyber risks. This is why AI should be embedded in customer-facing applications, but also in the back-end, to protect against fraudulent activity and cyberattacks.

FIs can achieve AI supremacy in the financial markets by deploying algorithms that process alternative data and use the latest machine learning methodologies to outperform their competitors.FIs should strive for AI transformation for all of their business operations. Some aspects of the AI revolution in financial services will be focused on reducing costs and assessing credit default risk more accurately. Compliance functions within FIs can benefit from AI by providing more efficient ways to comply with the expanding complexity of the regulatory landscape.

The financial markets are already extraordinarily complex, with hidden feedback loops that are impossible to sufficiently model. When AI - a complex learning agent - disrupts an already complicated system, it will impact the overall ecosystem in ways we cannot even begin to fathom. There will likely be a wave of fortunes made in the financial markets driven by AI. The quantitative fund industry is only beginning to heat up, and AI traded securities will be the hot new product on Wall Street and beyond.

One thing is certain - regulators must maintain a watchful eye on the impact of AI on the financial markets, which will diminish the importance of evaluating the fundamental financial performance of companies and gradually increase market volatility. Extreme volatility will, however, accrue quietly in the background, waiting patiently to trigger the next financial crisis. The role of AI may or may not play - whether itwill either diminish the impact of that crisis or make it worse than it might otherwise have been - will remain unknown.

Daniel Wagner is CEO of Country Risk Solutions. Keith Furst is Managing Director of Data Derivatives. They are the co-authors of the forthcoming book “AI Supremacy”, which will be published in September.




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Keywords: Digitization, Data, Fintech, Technology, AI, Big Data, Financial Markets

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