Here’s why AI transforming banking matters more today

As organizations have been accelerating their digitalization journeys in recent years, artificial intelligence (AI) is one of those disruptive technologies that is seeing widespread adoption across sectors, and this includes AI transforming the banking and financial services in more ways than one.

The radical change and improvement to processes that AI can affect in a sensitive space like banking, is one of the reasons why the finance industry cannot rule out its role across departments and processes. There is more than one way to skin a cat, and similarly the applications of AI in banking can be far-ranging.

The technology’s versatility is such that an OpenText survey found that nearly 80% of banks already recognize the benefits of AI. 75% of financial institutes already make use of this technology, and a further 46% plan to implement AI-based systems soon.

AI first saw strong interest for using it to tackle fraud and suspicious transactions, then for intelligent systems that could help ascertain customers’ credit scores and personalize services for them. Disciplines of artificial intelligence like machine learning helped advance cybersecurity and general risk management, while natural language processing (NLP) has been used to process text documents.

At first slow to embrace digitization and migrate their legacy systems to more modern processes, newer advancements such as mobile banking has seen growing AI applications. For instance, bank mobile apps can now incorporate AI-driven virtual assistants like Siri, using their voice and Touch ID to seamlessly perform transactions.

It’s a far cry from the pre-2000s era, and continued advancements in the field are expected so that by 2023, banks are projected to save approximately US$447 billion by developing and implementing AI applications.

AI chatbots in banking

Chatbots are like virtual assistants like Siri or Alexa, except when it comes to B2C, AI chatbots are probably constrained to a tighter set of parameters rather than the broad sets of queries the Siri might handle.

As an extremely customer-centric business, AI chatbots are becoming an expected investment for banks of a certain scale as they can communicate with customers without incurring regular costs beyond their original implementation. This saves money but also time, with research showing that financial institutions save about four minutes on average for each communication that the chatbot handles.

For instance, the Bank of America rolled out a chatbot called Erica that would send users notifications, inform them about their balances, makes recommendations for lowering expenditure, and provides updates on things like credit reports.

Data collection and analysis

Banks generate tremendous amounts of data based on millions of transactions that are performed every day, but the vast volume would make informed analysis an overwhelming task for human employees. Structuring the data into digestible amounts and patterns becomes critical, so that forward-looking strategies and risk management approaches can be practiced.

Just like how AI can process information to aid decision making in granting loans or detecting fraud, it can also collect and analyze data from multiple sources including mobile apps and arrange them in structured ways that can help drive strategizing.

Banks are able to generate nearly 66% more revenue from mobile banking driven by AI, saving costs and making mobile an indispensable channel of modern banking services.

Securing banking data with AI

As with customer service, risk management, and fraud detection, machine learning and AI will be hugely beneficial in deciphering banking transactions from strings of unintelligible characters, and converting that data into readable text that represents transaction details such as merchant name, location details.

Turning encrypted information into human-readable text can help both banks and customers to comprehend where funds are being spent, reducing the number of potential fraudulent claims and investigations which also can incur costs.

The Federal Trade Commission report for 2020 indicates that credit card fraud is the most common type of personal data theft. AI systems have already proven effective in analyzing customer data, location info, spending habits, and machine learning can be taught to look out for such indicators and trigger security mechanisms if unusual activity is flagged.

This will make cybersecurity spending much more targeted into suspicious cases rather than across a broad, unstructured threat surface. Proven and effective use cases is one of the reasons that ABI Research estimates that investing in AI and banking cybersecurity analytics will cross US$96 billion by the end of this year itself.