Financial Services

Financial services transactions are all about creating monetary value for the customer. Almost all transactions require a lot of data exchange, validation and analysis. Transactions range from simple, like a basic money transfer from a bank account, to complex, like a credit card payment to very complex, like trading in the stock market.

Since computing devices programmed with AI/ML algorithms can process far more data than humans can, they are well-positioned to manage several types of financial transactions better. Some examples where AI/ML solutions add value are:

• Fraud Detection and Prevention

With almost all financial transactions now being conducted online, there is also an increased opportunity for fraud. Humans can't monitor the huge number of transactions and fraud is detected only after an affected customer reports it, by which time it may be too late to rectify. AI/ML systems can process large numbers of transaction data as they are happening and determine patterns that could be potential fraud. Patterns can be repeated transactions of the same value in rapid succession, unusually high-value transactions, or transactions in unusual locations or at unusual hours. Once a potential fraud is detected, the system can either stop them from happening or alert the concerned authorities who can then follow up quickly.

• Loan Underwriting

One of the services that banks and financial institutions offer is loans. Giving a loan to a customer can be very risky since there is a possibility that the customer does not pay back the loan, which will then be a loss to the bank or financial institution. AI/ML algorithms can go through a lot of data, related directly to the customer and data related to other customers with a similar profile and determine what the likelihood is of the customer defaulting (not paying back) on the loan.

The decisions supported by these algorithms are likely to be more accurate than if a human alone were to decide, resulting in the financial institution giving fewer loans that are not paid back and therefore lower losses.

• Algorithmic Trading

The stock trading business is all about deciding which stocks to buy and sell and at what price. These decisions are taken after evaluating a lot of market parameters, data about the stock, and what other traders are buying and selling. Stock prices change very rapidly, as frequently as every second and therefore decisions have to be accurate and very fast.

AI/ML solutions can be programmed to process all this data and take decisions on what stocks to buy and sell and at what price. Such trading is referred to as algorithmic trading. Algorithmic trading can simultaneously analyze large volumes of data for multiple stocks and make very quick decisions on thousands of trades every day.

In all of the above solutions, by reducing or eliminating human intervention, the failure due to human error, intentional or unintentional, can also be reduced, improving the overall system efficiency.