Data is often regarded as the new ‘oil’ of the 21st century due to its immense value. With the rapid advancement of technology, digitisation is becoming increasingly prevalent. The widespread use of high-speed internet creates enormous amounts of data in the digital world.

According to the World Economic Forum, the world will generate 463 exabytes of data by 2025. These massive volumes of data are called Big Data. Big Data is popularly known as the fuel that powers Artificial Intelligence (AI).

What is AI?

In simple terms, Artificial Intelligence (AI) means adding intelligence to machines. John McCarthy, popularly called the father of Artificial Intelligence, defined it as “The science and engineering of making intelligent machines, especially computer programs.”

Over time, AI has evolved significantly, and Generative AI (Gen AI) is currently a hot topic. Traditional AI systems function and make decisions based on a set of predefined instructions, which are also called programs. On the other hand, Gen AI is a type of AI that focuses on generating new content.

In simple terms, traditional AI is the ‘predictor’, while generative AI is the ‘creator’.

Gen AI in banking

 

Banks deal with a vast amount of data stored in their database systems, which can be challenging to manage and analyze. Gen AI can analyze large amounts of data and assist banks in making informed decisions. Additionally, it has the potential to offer a tailor-made approach to routine banking tasks, including automating customer services through AI-based chatbots. With the help of Gen AI, banks can improve their operational efficiency and accelerate the decision-making process.

• Credit Rating: Credit analysts can use Gen AI to determine a customer’s credit rating by gathering data from various sources. Apart from credit reports from credit bureaus, Gen AI can also fetch account statements, spending behaviour, and average account balances maintained in accounts.

Traditional credit reports focus on a few factors like borrowings, timely repayments, credit utilization, etc. However, there may be cases where a customer’s maximum earnings are consumed by repayment of EMIs and credit card bills, which is not captured in the traditional credit report.

Gen AI increases the scope of credit reports by including additional information such as account-related details, source of earnings, savings, and savings-to-loan repayment ratio. With the help of these factors, credit analysts can obtain a clearer picture of a customer’s financial history. With the help of this report, Gen AI can predict a customer’s future financial behaviour, thereby improving the quality of loans and reducing the possibility of loan defaults in the future.

• Fraud detection: Traditional fraud detection systems are designed to detect and raise alerts based on pre-defined instructions. However, as cyber fraud cases continue to rise, the need for a stronger fraud detection system that can detect any intrusions and block them in real time has become necessary. Fraudsters are constantly devising new strategies and tools to break into the bank’s cyber security system and gain control of the bank’s server. The challenge with traditional fraud detection systems is that they can only be updated once a loophole has been discovered.

With the help of Gen AI, the system can analyze and process large volumes of data in real time, enabling it to identify unusual behaviour that may indicate unauthorized access and alert the system administrator.

In a traditional fraud detection system, the system may only raise an alert if a user performs a valid transaction in a new location for a high-value amount or both. However, unauthorized transactions of small amounts may not trigger an alert. Gen AI’s self-learning ability can plug these loopholes in real-time without requiring manual updates.

• Customer service: Customer care plays a vital role in the banking industry. If a customer encounters an issue outside of regular business hours, then customer care is often their first point of contact. The customer care executive listens to the customer’s problems and tries to resolve them to the best of their ability, using their knowledge and the resources available to them.

Suppose the customer care executive is unable to resolve the issue on the call. In that case, they will either transfer the call to a senior representative or take a service request on behalf of the customer. The turnaround time for resolving issues can depend on how many executives are available at the call centre.

With the help of Gen AI, banks can implement virtual customer care agents who can assist customers according to their needs. Virtual agents can attend to multiple customers in real-time, in various languages, thereby reducing turnaround times and increasing efficiency in customer service.

• Lead generation: With the help of Gen AI, product advisers can offer personalized product recommendations based on the customer’s requirements, behaviour patterns, financial records, and economic scenarios.

When processing a loan application, Gen AI analyses the customer’s earnings, spending behaviour, financial records, and credit reports to suggest the best loan scheme and the ideal tenure of the loan that would have minimal impact on their finances.

Similarly, when recommending investment products for cross-selling, Gen AI analyses the customer’s financial goals and details to suggest the best product that aligns with their financial health.

In the traditional approach, product advisers tend to follow the ‘one product fits all’ approach, while Gen AI can make customized offerings as per the requirements of the customers. This personalized approach increases customer satisfaction, leading to longer product retention.

Morgan Stanley implemented a Gen AI-based solution in Q2 of 2023, resulting in a 16% increase in revenue since implementation.

 

Shortcomings of Gen AI

Gen AI can generate new content based on its past behaviour and learning. It may portray partiality towards a specific person, group, or thing as it is designed with complex algorithms and machine learning models that make it capable of identifying patterns and forming associations. One must remember that Gen AI is made up of millions of lines of code, and no system is completely foolproof. Any inadvertent error can potentially lead to the exposure of sensitive information. Moreover, due to the “weak logic” in coding, Gen AI may fail to detect anomalies in the system, which could result in unnoticed hacking attempts.

 

The way forward

With the help of Gen AI, banks can streamline their operations, reduce costs, and ultimately maximize profits. It can perform routine and repetitive tasks much faster than humans by executing commands with great precision and accuracy. These tasks include data entry, processing of loan applications, and other tedious and time-consuming activities.

However, the use of Gen AI in the banking sector is still in its infancy, and the shortcomings should be resolved to ensure it meets the industry’s highest standards before it is commercially rolled out.

Despite the shortcomings, the scope of AI in the banking sector is vast. By working alongside humans, Gen AI can help redefine the banking experience and create a more seamless and personalized experience for customers, thereby increasing customer satisfaction and ultimately driving growth.