Artificial Intelligence otherwise commonly referred to as “AI” is a field of computer science that mainly focuses on creating models that use cognitive intelligence to perform tasks which were (and, to an extent, still are) traditionally performed by humans. For some, a blessing and for others, humankind’s downfall and apocalypse.
This insight will not argue for or against any of the said propositions – we leave the philosophical discussion for those that are more qualified in that area – but through a few bullet point notes will attempt to shed light on how AI has integrated within the financial services sector:
- AI has played a crucial factor in the recent evolution of decision-making within the investment, lending and credit assessment spheres as it has demonstrated to permit the utilisation of various types of data from several sources that can produce accurate (and sometimes with a precision-like accuracy) risks assessments of markets, strategies and products, leading to a better decision-making process for the client.
- The rapid growth of algorithmic trading has significantly impacted the functioning of stock markets. The fast-paced data processing has given flexibility to transactions and has been proved to be evidently better than human traders, as AI-models rely on real-time data and factor in market anomalies which enables them to identify unexpected market trends. Potential illegal insider trading based on non-public information can also be traced by AI-models which is a crucial weapon for the financial industry to protect investors from potential losses and stabilise security in the market with a focus on detecting and preventing illegal trading practices.
- Robo-advisors can now offer a variety of services such as financial planning, investments advice and can help with onboarding and account opening tasks. These digital advisors are analysing customer preferences and behaviour, and therefore offering tailored recommendations to users. Through this process client preferences are successfully matched with suitable possibilities without being subject to the hustle of deciding themselves or even asking a human advisor which would not be as time and cost efficient as using a digital advisor.
- Automating traditional banking and payment systems has restructured the services offered to the public and revolutionised customer experience. Clients are catered with self-customed and readily accessible solutions that suit their needs and are given the financial independence they have long asked for, thus making their overall customer experience more coherent and swifter as a result of smart 24/7 chatbots.
- With the automation of data collection, service providers are not only investing in providing better customer experience but are also enhancing their readiness in meeting regulatory compliance obligations by using AI to collect and interpret their data so as to generate more accurate reports within the specific timeframe. Faster compliance deriving from the implementation of AI, regarding due-diligence and KYC purposes, enables better monitoring and prevention of fraudulent actions such as money laundering and credit fraud. This is because AI can monitor transactions and identify changes in the client’s purchase behaviour or any suspicious abnormality in the transactions and can trigger security measures when contradictions or irregularities arise with regards to the historical orthodox pattern of the client.
But, unavoidably, the counterargument to the above pluses of AI as well as other pluses that are not mentioned in this insight, is that:
- Performance of mundane tasks is now commonly done almost entirely by AI. With a prescribed set of parameters, AI can extract, analyse and interpret information from various sources at a higher speed. This eliminates repetitive task human error which allows companies to use their workface in areas that require human intervention. It is estimated that reduction of the workface and job displacement would (and, to an extent, it already is) be a common phenomenon as AI becomes more integrated and widespread. Having said that, AI models cannot ultimately perform all necessary tasks within the ambit of the financial sector. Under some circumstances, objective thinking, fairness and other human characteristics are needed to perform certain tasks since human behaviour cannot always be anticipated and neither a computer system can mimic human skills entirely.
- Fairness as well as consideration of social or environmental accountability will not always be possible to accomplish. This is because AI algorithms rely heavily on historical data which entails that unforeseeable events as well as unfolding market dynamics are not taken into account. There is also the possibility for AI to introduce biases that would lead to unfair behaviour and discrimination of some type of groups because of the use of historic unrepresentative data. Most importantly, there is a possibility of client privacy and security to be under threat due to the usage of vast information collected from various different sources.
- Any radical deployment of AI usage within the financial sector will consequently lead to the lack of the transparency of decisions and accountability of actions within financial institutions in the event of malfunction, defect or even failure of the system.
In the end, utilisation of AI in financial services is not and should not be about displacement or replacement of humans or platforms (controlled by humans) but rather about bettering, enhancing and supporting human skills and capabilities. Paring people and machines and allowing each one to contribute to areas which they are best equipped in will be the key to unlocking the full potential of the industry. Managing machines in an evolving legal landscape would be central in exploding the financial services sector to new territories and greater heights.
We fully appreciate that this insight may not answer all your questions on AI but, we would be happy to hear from you and help you navigate you the ever-changing regulatory landscape.