Data Trends

Author

Maria Donato

Consumers have always been at the heart of every business, although it was not until recently that companies started realizing that. In many industries, companies have been shifting their mindset from product-focused business models to consumer-focused business models.

The banking industry in particular, still to this day, has a very product-focused approach. The focus of banks is still on the different products they offer and as such, consumers find themselves having to deal with different product managers when it comes to their finances. One single consumer’s data is spread throughout different departments which usually leads to an ineffective account and client management that causes the loss of customers.

Consumers demand to be well-looked after and are prepared to switch to a different services provider if they feel they will be better-served, especially when it comes to their finances and life savings. They are looking to be offered personalized and personal service and support which puts a strain on companies or entities such as banks that are not structured in a way that allows them to easily respond to this need.

But wait! Now we have AI and it is God’s gift to Humanity.

Here are some of the ways in which AI can be of use for financial services providers.

Customer Service

Artificial intelligence and machine learning are increasingly being used for customer service improvements. With its ability to connect large data sets and act upon the information they gather, they can help solve the problem that banks are faced with. However, this does not relieve them from the need of restructuring and shift towards a more customer-centric business model.

At the moment, AI is being used by many financial institutions as a way to improve their relationship with customers. Self-help smart chatbots are an example of this. With these chatbots, customers can get 24/7 support without putting a strain on call centers.

AI-powered tools are also being used as a way to provide financial advice to customers. Many banks already have apps that help plan out expenses and send out notifications as a reminder that a payment date is approaching.

Bank of America’s Erica is an example of how these apps can be taken to the next level. Erica is an intelligent virtual assistant that through the use of predictive analytics and cognitive messaging, offers personalized financial advice to customers.

All of these advances in customer support are sure to improve customer satisfaction and retention, which is extremely important in a time when consumers are very strong-minded about how they demand to be treated.

Credit Decisions & Risk Management

By compiling and analyzing all the data referring to a particular customer, AI is able to quickly and efficiently determine whether that customer should be granted a loan. It, not only, goes through all the data related to past loans but it can also predict, based on the data analyzed, whether a customer is likely to default on a loan in the future. How? It takes into consideration past employment patterns, loan requirements, transactions history, and buying patterns, and uses that data to predict whether it is likely that the customer will become unable to comply with the payments for the loan.

AI, unlike humans, makes these decisions objectively and because of all the information it takes into account, can significantly reduce the amount of non-performing loans.

Fraud Detection

AI can be used as a way to detect fraud and provide safer online transactions. More and more financial institutions are relying on AI and Machine Learning to analyze consumer behavior, buying habits and transaction patterns in order to quickly identify unusual behavior that may be the result of credit card fraud, identity theft or money laundering.

And with the continuous growth of e-commerce and online payments, credit card fraud is becoming more and more frequent.

Before AI, this process of fraud detection was done manually and based on certain rules and it did not deliver completely accurate results. This was a useful process for detecting situations that fell within the patterns of what was considered fraud. However, once a situation fell outside of what was expected because it followed new or different patterns, it wasn’t considered fraud, even if it was. This ended up causing damage not only to the bank but also to the customer.

Machine learning algorithms allow for the fast processing and accurate interpretation of large data sets, therefore, facilitating this process of fraud detection and quickly resolving the issue without a lot of damage being done.

Smart Trading

Hedge funds and brokerage companies are using AI to trade more efficiently. By being able to predict and forecast market fluctuations and price movements they can, not only, minimize the impact of these movements on their clients’ portfolios but they can also potentially beat the market and profit from these forecasts.

Of course, the stock market is unpredictable (ask those who were around in 2008) and one can never fully eliminate risk but, through the analysis of trends and price movements, it is possible to bring a higher level of accuracy to trading and minimize that risk to some extent.

If clients understand that their broker has their best interests at heart and that their assets are “safe”, they will remain loyal to that broker and to that brokerage firm, hedge fund, etc.

This allows for better customer retention and a consequent increase in assets under management and revenue obtained.

Conclusion

As we’ve seen, AI is already very much a reality in the financial industry and it has an extremely positive impact on these institutions. As financial providers continue to adopt AI across several business areas, its role becomes more important. According to an Accenture 2018 study, 79% of bankers believe that “in the next two years, AI will work next to humans in their organizations as a co-worker, collaborator and trusted advisor” and that “the majority of bank-customer interaction will be conducted via AI in the next few years”.

But is the impact of artificial intelligence always positive? And if not, can we ensure it is?

As AI substitutes or complements more and more tasks previously performed by humans, we need to make sure that AI-powered tools act by the same rules and regulations as bank managers do, including data-protection regulations, among others. Financial institutions who do not adopt AI will fall behind competition and struggle to survive in a market where consumers are no longer brand loyalists but instead, strive to find the best deal for themselves, at all times.

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