Five Problems AI Can Solve in the Banking Sector

AI Can Solve in the Banking Sector

Artificial intelligence (AI) is being used in a wide range of industries, from healthcare to manufacturing. But one of the most exciting uses of AI is in banking. AI can be used to provide better customer service, help banks identify fraud and risk, better manage their finances, and even help underwriting and credit decisions. This has the potential to improve the lives of millions of people, with the potential to save banks billions of dollars and improve the quality of service they provide to their customers.

According to a poll of financial services professionals by OpenText, the majority of banks (80%) are quite aware of the potential advantages that AI and machine learning may offer. But there are also several problems that AI can solve in the banking sector, from fraud prevention to customer service. 

Decrease Customer Support Costs

Customer support is one of the most expensive areas in the banking industry, with many banks investing billions of dollars in their call centers in dealing with customer queries and complaints. But with the increasing use of AI in banking, it’s possible that customer support could be reduced, which would save banks money in the long run. The use of AI in banking has been improving over the past few years, allowing the systems to process large amounts of data and make decisions on their own. This can potentially reduce the amount of human interaction required by the systems, which will reduce the cost of customer support.

Across digital banking, front- and middle-office AI technologies have the greatest potential for cost reductions. AI can help reduce the cost of customer support in banking, which will help banks increase the number of interactions they have with their customers, increasing revenue and profitability. The technology is already being used to respond to customer queries and perform basic lending functions, such as underwriting and checking an applicant’s credit history. The critical next step is for AI to be able to answer complex questions and provide customized advice.


Legal document tracking is an important practice in the banking industry, but current technology is not up to the task of efficiently and effectively managing large volumes of paper documents. New laws and rules are passed almost every day. They are oftentimes lengthy and difficult to read. 

One answer could be to hire a lawyer, but just like everyone else, lawyers can make mistakes. Many banks use manual processes that require a lot of time and resources to achieve a small level of accuracy. As a result, many businesses are considering employing AI to work with legal papers.

Using machine learning for banks to facilitate legal document tracking has the potential to transform the way legal departments operate, improving compliance and reducing costs. It can be used for tracking new legal papers, keeping an eye on modifications, pulling the most crucial instructions, alerting management, and document classification.

Prevent Fraud and Money Laundering

The world of banking is changing rapidly. New technologies allow for the automation of processes previously performed by human employees. This has led to a reduction in the operating cost but has also opened the door for cybercriminals to exploit existing vulnerabilities in the system. One of the most common ways cybercriminals exploit banking systems is through the misuse of financial information.

When it comes to fraud solutions, banks have to be ultra-fast, accurate, efficient, scale fast and find cost-effective ways to detect fraud. All of these can only be accomplished with AI and ML technologies, which require processing tens of thousands of inquiries and analyzing each customer’s behavior in the context of their “usual” daily activities to identify abnormalities.

AI is used to prevent fraud and money laundering in banking. The technology has also been used to identify customers at risk of financial abuse, such as those with a history of fraud or those who have been evicted from their homes, to provide them with tailored services. 

Handle Market Instability and Unpredictability

As technology has evolved, so too have the markets it has affected. Today’s markets are much more complex than they were even a decade ago, with a dizzying array of products, securities, currencies, and other instruments. The market speed, complexity, and diversity have made it challenging for humans to navigate. This has led to a growing demand for systems that can perform complex tasks with greater speed, accuracy, and efficiency than humans.

The financial services industry is in the midst of a tumultuous era as the industry is experiencing periods of market instability and unpredictability. Over the next few years, the banking industry will continue to experience these periods of uncertainty and change, and it will be up to the technology of artificial intelligence to help mitigate some of the damage that is being caused by the shifts in the industry. 

AI has the potential to improve the efficiency of banking systems, reduce the amount of human labor required to perform certain functions, and enhance the experience of customers and clients, thereby improving the long-term profitability of banking organizations and their shareholders. Over time, AI will be able to make predictions about the direction of the market, and identify trading opportunities when they arise, the bank says. This will help the bank to protect against volatility and to make better business decisions.

Boost Customer Experience

The same technology that is used to analyze large amounts of data to reveal trends and predict future behavior can be used to help banks serve their customers better. It’s already being used to improve the speed and accuracy of loan assessments, reduce the number of incorrect payments and provide better advice on savings and credit. It’s also being used to personalize the customer experience, such as identifying when someone is experiencing financial difficulties and offering to help. 
By diverting and prioritizing customer support questions, conversational AI may cut expenses without compromising the standard of the customer experience. For example, DBS bank uses only one-fifth of the resources needed by a typical bank because of cost-saving features like an AI-powered virtual assistant that serves as the bank’s front-facing assistant. In its digibank application, the assistant manages 82% of all client queries without involving customer support personnel, freeing up these teams to concentrate on activities that require a human touch.

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