The Growing Opportunities of AI in Banking
Artificial intelligence (AI) is increasingly applied to revolutionize the banking sector. It helps to address the risks, fight the financial crime, improve the customer service, and gain better ROI from marketing strategy.
In recent years, AI and machine learning (ML) have been transforming industries globally. Among the latest adopters of these new technologies is the banking sector. So, in what ways and to what ends can banks use AI and ML to tackle the challenges of the new era?
Banking and Technologies in Brief
If we consider a general picture of using cutting-edge technologies in banking, it is easy to notice that this industry was among the pioneers. There were foreign exchange trading platforms as early as 2007, and online banking has long been used by both individual customers and enterprises worldwide. However, the very nature of banking services and products entails confidentiality, and this possibly held back further developments in the field.
The pandemic of 2020 and ongoing crises caused by lockdowns dictate that more banking services migrate online and be accessible to clients. However, now it is not only the clients who may benefit from ML and AI in banking – it is the employees themselves. New ML solutions allow them to seamlessly introduce education into the workflow, while AI significantly relieves the burden of paperwork and human transactions, leaving only those that are truly needed.
AI in the Banking Sector: The Opportunities
The principal reason why AI is getting so popular is its ability to identify and gauge the customer needs, which helps businesses better understand customer behavior and adapt their services. On the other hand, customers expect to access banking services on any device and receive offers that best suit their needs.
The global AI market is forecast to reach nearly $130 billion by 2025, and in Fintech alone it promises to grow over $22.6 billion in the next five years. As the demand for advanced technologies is set to increase, what aspects of banking are most likely to be transformed with the help of AI?
AI helps banks to build better models for credit services. The models improve portfolio management and credit approval, including pricing for risk. With their assistance, bank managers can better forecast losses by estimating default probability and loss severity. As AI explores the nuances of behavior of different social groups, banking software is becoming infinitely better at more precise credit scoring.
Understanding client behavior is one of the principal applications of AI in banking. Client experience means that banks can anticipate client needs by correctly using information about the clients’ behavior in similar situations or transactions in the past.
Speaking of precise solutions, chatbots have successfully addressed the problem of long waits on the phone to talk to an operator. AI may be helpful in working with client complaints, thus prompting banks to be proactive. Another way AI helps to improve customer experience is by analyzing customer traffic volume. Relying on the data provided, it predicts the high peaks where a branch or a contact office needs to have the most staff available. Finally, AI collects information about clients via their social networks, email marketing, and other sources to use to improve the customer service.
Digital crime is getting more and more sophisticated, especially when it comes to fraud and money laundering, putting more pressure on banks. Provided with the findings from investigation and fraud losses, AI analyzes this big data to return less obvious information about the crime. As a result, banks are getting more efficient at identifying unusual activity during real-time fraud prevention. Moreover, AI helps to further improve existing training models by incorporating the data with new kinds of criminal activity in banking.
Global Markets and Exchanges
As for financial markets, historical transaction cost analysis (TCA) and execution data help AI to improve order routing and trade execution strategy. The models allow traders to experiment with mixing different algorithms, counterparties, and other aspects of trading. Apart from assisting traders in their decision-making, these models minimize market impact and cost and record the trader’s attempts to carry out the transaction.
Since cash is still being used for various transactions, AI allows banks to forecast the demand for new loans, prepayment speed, and ATM cash requirements. Large volumes of historical data are used to estimate availability of cash. The AI insights from the data analysis indicate the necessary amount of cash the bank has to have. The same findings may be used to optimize the return on excess cash.
Advertising and Marketing
In the world of advertising and marketing, it is imperative to gain the deepest possible understanding of clients, leads, and referrals to manage the budget wisely and get the maximum ROI. AI assists banks with predicting the clients’ reaction to pricing, improving the value proposition, and calculating price-volume elasticity.
Employee Training and Service Automation
This is a less obvious aspect of applying AI in banking, but it deserves the mention. Employees can now be trained in their free time with the help of online learning. It is no longer necessary to organize long educational seminars. Learning becomes a part of the work experience thanks to new technologies. Likewise, the amount of paperwork and phone calls is reduced dramatically, while digital banking has become more effective thanks to AI. Bank analysts and managers now focus on data analysis rather than data input and use the results to develop better offers and services.
As much as the future of AI in banking looks bright, one should not forget that this is a complex technology and its evolution is far from completion. If your bank is currently thinking of adopting AI to enhance its business processes, it is advised to first apply it to the area that needs most improvement. However, the earlier banks adopt AI, the bigger their gains will be in the long run.