5 AI application scenarios in the banking sector

Over the past decades, banks have improved the way they interact with customers. They have adapted modern technology to the specific nature of their work. For example, in the 1960s, the first ATMs appeared, and ten years later, there were already payment cards. At the turn of this century, people discovered 24-hour online banking, and in 2010, they heard about mobile banking. But the development of the financial system did not stop there, as the digital age opened up new opportunities — the use of artificial intelligence. By 2023, banks are expected to save $ 447 billion by applying an artificial intelligence app.

This article explains how financial institutions today use this technology in their operations.

AI-powered chatbots

Chatbots are conversational interfaces enabled by AI. This is one of the most famous cases of AI application in banking. The bots communicate with thousands of customers on behalf of the bank without incurring significant expense. The researchers estimated that financial institutions save four minutes for each communication handled by the chatbot.

Since customers use mobile apps to transact money, banks are integrating chatbot services into them. It helps to grab the attention of users and create a recognizable brand in the market.

For example, Bank of America launched a chatbot that sends notifications to users, informs them of their balances, makes recommendations for saving money, updates credit reports, and more. This is how the bank helps its customers to make informed decisions.

Another example is the launch of the Ceba chatbot, which brought great success to the Australian Commonwealth Bank. With its help, around half a million customers could solve more than two hundred banking problems: activate their cards, check their account balances, withdraw money, etc.

Mobile banking

AI functionality in mobile apps is becoming increasingly proactive, personalized and advanced. For example, the Royal Bank of Canada has included Siri in its iOS app. Now to send money to another card, just say something like, “Hey Siri, send Lisa $ 30!” — and confirm the transaction using Touch ID.

Thanks to AI, banks generate 66% more revenue from mobile banking users than when customers visit branches. Banking institutions pay particular attention to this technology to improve their services and remain competitive in the market.

Data collection and analysis

Banking institutions record millions of business transactions every day. The volume of information generated by banks is enormous, so collecting and recording it becomes an overwhelming task for employees. Structuring and recording this data is impossible as long as there is no usage plan. Therefore, it is difficult to determine the relationship between the data collected, especially when a bank has thousands of customers.

Previously, the approach was as follows: A client would come to a meeting with a bank employee who knew his name and financial history and understood which options were best to offer. But that’s history now. With the wealth of data from countless transactions, banks are trying to implement innovative business ideas and risk management solutions.

AI-powered applications collect and analyze data. It improves the user experience. The information can be used to grant loans or detect fraud. Companies that estimated their profits from extensive data analysis reported an average revenue increase of 8% and a cost reduction of 10%.

Risk management

Extending credit is quite a difficult task for bankers. If a bank gives money to insolvent customers, it may run into difficulties. If a borrower loses a stable income, it leads to default. According to statistics, in 2020, credit card delinquencies in the United States increased by 1.4% in six months.

AI-based systems can more accurately assess customers’ credit histories to avoid this level of default. Mobile banking apps track financial transactions and analyze user data. It helps banks anticipate risks associated with granting loans, such as customer insolvency or the threat of fraud.

Data security

According to the Federal Trade Commission report for 2020, credit card fraud is the most common type of personal data theft.

AI-based systems are effective against wrongdoers. The programs analyze customers’ behaviour, location, and financial habits and trigger a security mechanism if they detect unusual activity. ABI Research estimates that spending on AI and cybersecurity analysis will reach $ 96 billion by the end of 2021.

Amazon has already acquired Harvest.AI — an AI cybersecurity startup — and launched Macie — a service that applies machine learning to detect, sort and structure data in S3 cloud storage.

Conclusion

There are other ways to apply AI in the financial industry. According to an OpenText survey, 80% of banks recognize the benefits of AI, 75% of them are already using this technology, and 46% plan to implement AI-based systems in the near future.

AI-based solutions are an integral part of companies’ development strategies, helping them stay competitive. This technology minimizes operating costs, improves customer support and automates processes.