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How artificial intelligence (AI) is transforming the financial sector

Artificial intelligence (AI) is significantly changing the traditional operating models of financial institutions, shifting strategic priorities and upending the competitive dynamics of the financial ecosystem. With this in mind, how can financial institutions better utilise AI and prepare themselves for the future?

With millions of customers, billions of transactions and trillions worth of assets, the financial sector is the propelling force of the global economy and the world in which we live.

A big driving force behind the advancements in the sector has been digital transformation. The way people conduct financial transactions today is vastly different to 20 years ago and financial organisations have had to quickly adapt to these changes, not only in embracing digital transformations, but leading the way when it comes to early adoption of new technologies.

Slowly but surely, AI is quietly impacting the world through numerous and varied applications. AI technology is already powering many everyday activities, from driving us to work to automatically adjusting the thermostat, and often without our knowledge.

According to a 2019 pre-COVID report by Gartner, 40 percent of major businesses were anticipated to implement AI solutions in 2020, and more than half will double existing implementations in 2020. While the COVID-19 pandemic undoubtedly had an impact on those figures, there is no doubt that AI is now a major ‘player’ in the financial sector.

We know that AI is being used widely across the financial industry, so let’s take a closer look at how it is transforming the sector.

Personalised banking

Many believed the banking sector is lagging behind other sectors when it comes to customer service. Long gone are the days of face to face transactions in branches. Instead, customers are turning to digital mediums for customer support and banks have finally caught on.

Chatbots and live chat software have become the first point of contact for customers who are looking for customer support. Chatbots are essentially Artificial Intelligence programs that work based on pre-set rules. Advanced chatbots can also be integrated with deep learning capabilities that enable them to learn continuously from customer conversations and improvise their customer service.

In addition, many banks now offer personalised financial advice through their mobile app. These AI-powered systems can keep track of income, regular expenses and spending behaviours, and provide financial plans and suggestions. Mobile banking apps can also reminder us to pay bills, compete transactions and interact with the bank more conveniently and efficiently.

Reliable risk management

Artificial intelligence plays a crucial role in efficiently managing risk, and in the world of finance, time is money. For risk cases, algorithms can be used to analyse case history and identify any potential problems. This involves the use of Machine Learning (ML) to create models that enable financial experts to follow particular trends and notice possible risks. These models also provide reliable information for use in future modelling.

When it comes to accounting and risk assessment, even with the best minds, manual errors can still occur. Financial institutions are governed by very strict regulations and ensuring compliance while dealing with securities, insurance products, debt and other financial products can be difficult.

It is here where AI can help by improving the reliability of risk assessment by introducing systemised frameworks that reduce or eliminate manual error. According to McKinsey, this could generate a value of more than $250 billion in the banking industry.

As well as improving reliability and reducing the human error factor, AI and ML means large amounts of data can be subject to powerful processing in a shorter space of time. Both structured and unstructured data can also be managed with cognitive computing. All of this would otherwise equate to long hours of manual work.

Fraud prevention

With a massive growth in digital customer transactions in recent years, reliable fraud detection models are required to protect sensitive data. AI can be used to strengthen rule-based models and assist human analysts. This can in turn improve efficiency and accuracy and reduce costs.

AI can be used to review spending history and behaviours so that it can highlight irregularities, such as a card being used in different global locations within a short space of time (aka “Superman travel”). AI is also able to learn from human corrections and apply decisions based on what should be highlighted.

According to an IBM report, “72% of business leaders cite fraud as a growing concern over the past 12 months.” The report goes on to state that worldwide losses due to fraud are expected to reach USD $44 billion by 2025. IBM is one of many companies investing in AI and has developed an AI-powered platform to help teams around the world combat fraud.

Quantitative trading

More and more, financial firms are turning to machines to do the job humans have done for decades.

As far back as 2015, wealth management firm Charles Schwab launched a service called Schwab Intelligent Portfolios. The service was unique in that it wasn’t a person who decides where to invest your money, it was an algorithm – lines of code programmed into a computer. This was ground-breaking at the time.

As reported on the BBC, there are many benefits of using an AI-powered solution for investment, “It’s lower cost for the investor,” says Tobin McDaniel, who leads the Schwab Intelligent Portfolios team.

As opposed to working with a traditional advisor where you might pay up to 1%, here you get portfolio management at essentially no management fee.

Artificial intelligence also provides the advantage of being able to observe patterns from past data and make predictions on whether they are likely to repeat in the future. When there are certain anomalies in the data, such as a financial crisis, AI can study the data and notice possible triggers, then prepare for them in the future. AI is also able to personalise investment for particular investors to help in their decisions.

While there are definitely benefits to AI when it comes to making investment decisions, the COVID-19 pandemic has thrown a potential spanner in the works, “Disruptions such as the COVID-19 pandemic is causing historical data that reflects past conditions to quickly become obsolete, which is breaking many production AI and machine learning (ML) models” said Jim Hare, distinguished research vice president at Gartner. “In addition, decision making by humans and AI has become more complex and demanding, and overly reliant on data hungry deep learning approaches.”

In fact, Gartner, Inc. predicts that by 2025, 70% of organisations will shift their focus from big to small and wide data, providing more context for analytics and making artificial intelligence (AI) less data hungry. According to Gartner, “Although traditional AI techniques may rely heavily on historical data, given how COVID-19 has changed the business landscape, historical data may no longer be relevant. This means that AI technology must be able to operate with less data via “small data” techniques and adaptive machine learning. These AI systems must also protect privacy, comply with federal regulations and minimize bias to support an ethical AI.

Intelligent personal finance management

We have already touched on the use of AI by banks to deliver a more personalised banking experience and we can expect this to go one step further in the very near future.

Personal financial management does not come easy to a lot of people. Managing spreadsheets, understanding incomings and outgoings and ‘balancing the books’ is a daunting task for people all over the world. It’s also one of the key reasons why mobile apps and web-based applications have become popular for personal financial management.

Now AI and ML are adding a layer of convenience to these mobile apps. The purpose of AI in personal financial management is simple; On a daily basis, we make lots of financial transactions, from picking up a chocolate bar at the supermarket to making a mortgage payment to getting paid for our work. Compiled together and analysed, these independent financial transactions can throw light on better ways of organising our finances.

In fact, there are wealth management tools that are powered by AI that help users to quantify their probable savings and passive income earnings based on their current spending patterns. With every single day, we are getting closer to autonomous finance becoming a reality.

Credit decisions

In many fields, AI is effectively used to better inform decision-making processes. One of these areas is credit, for which AI can provide accurate assessments of potential borrowers quickly and at a lower cost. Compared with traditional credit-scoring systems, AI credit scoring can be much more complex. They can help to identify applicants who are more likely to default, and those that lack any reliable credit history.

AI can help bankers and financial institutions create predictive models of individual income earning capacity and repayment capacity by connecting several data points. They can fast-track the pace with which credit decisions are taken. This would enable in identifying the accounts that can yield good returns and those which have a high probability of turning into bad and non-performing loans.

Future trends for AI in the financial sector

Artificial Intelligence is introducing sweeping reforms across the finance sector. The reforms are so drastic that in a few years from now the finance sector as we know it today might become completely unrecognisable.

Smarter, more responsible, scalable AI will enable better learning algorithms, interpretable systems, and shorter time to value. Organisations will begin to require a lot more from AI systems, and they will need to figure out how to scale the technologies — something that up to this point has been challenging.

Systems that are powered by artificial Intelligence can be made faster, more efficient, and more reliable. These technologies are finding more applications in the world of finance, and they are being widely adopted by financial firms. Those that accept the risks that early adoption may entail are frequently rewarded by operations that are streamlined and much more productive. AI holds great potential for the world of finance, where business leaders are able to make the smartest decisions with the right data.


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