Can artificial intelligence help banks reduce non-performing loans?

Illustration: TBS

The expression "artificial intelligence" (AI) is used to illustrate the innovation of computer approaches that are competent at accomplishing exercises that usually demand human intelligence, such as mouthpiece distinction, decision-making, and terminology translation. Machine learning algorithms, which allow the system to learn from data, spot patterns, and base forecasts or judgments on that learning, are used to build AI technologies.

AI will have a major impact on the financial sector. Chatbots that are powered by AI can provide round-the-clock customer service, answer queries, fix issues, and offer personalised recommendations based on user behaviour. AI has the potential to greatly increase financial operations' efficacy and productivity, cut expenses, and improve customer service. It also brings up social and legal issues, though, which demand cautious thought and consideration.

While AI has the potential to handle some duties that are presently carried out by human employees in Bangladesh's financial sector, it is unlikely to fully replace human workers. Instead, AI is more likely to supplement human abilities, freeing up workers to concentrate on higher-value activities that call for human knowledge, like relationship management and decision-making.

Data entry, record-keeping, and transaction handling are examples of commonplace jobs that can be automated with AI. Because of this, the staff is able to focus on the more difficult and important commitments. With the help of artificial intelligence, banks can streamline their operations to save money and get more done. Because organisations preserve the exact class of employee efficiency, they may be competent to execute additional tasks repeatedly with inadequate support. 

Due to automation, the banking industry as a whole would undoubtedly require more personnel and skill sets. Institutions might, for instance, need staff with expertise in data analytics, algorithms, and hacking. In Bangladesh's banking industry, AI is likely to change the nature of work; however, it is not likely to completely replace human workers. Instead, it is more likely to lead to the creation of new jobs, the requirement for the acquisition of new skill sets, and the liberation of workers to concentrate on higher-value tasks necessitating human expertise.

Artificial intelligence can be very useful in reducing Non-Performing Loans (NPL) in Bangladesh's financial sector. NPL is indisputably the biggest concern for Bangladesh's banking sector because it may result in substantial financial casualties for the institutions and impair the nation's economic development. The Bangladeshi financial industry could benefit from AI in the following ways to reduce non-performing loans:

AI can be used to evaluate data from various sources, such as credit records, transaction histories, and customer behaviour, to forecast the probability of debt failure. Banks can spot high-risk borrowers and take preventative action to lower the chance of default by using actionable insights.

Artificial intelligence can use real-time scam monitoring to find fraudulent behaviour. Banks can use machine learning algorithms to evaluate transaction data and detect fraud-related trends. This can assist banks in stopping scams and lowering the hazard of NPLs.

Chatbots with AI capabilities can offer individualised customer support to debtors. Consumers can respond to questions, provide details about lending options, and even get ideas for ways to raise credit ratings. This can increase client happiness and lower the danger of non-performing loans.

Banking institutions can use AI to find clients who are in danger of default and present them with debt restructuring options. Banks can help debtors repay their debts and lower the risk of NPLs by providing alternatives for loan restructuring.

Artificial intelligence can assist institutions in identifying possible hazards and implementing preventative measures to control them. Banking institutions can evaluate data using machine learning-based algorithms to spot potential dangers like changes in borrower behaviour or shifting financial circumstances. The danger of NPLs can be greatly reduced by banks that use this information to guide preventative measures.

Using AI-powered solutions, banks can enhance their risk management expertise, stop fraud, provide individualised customer service, and take preventative measures to lower the risk of debt default.

Sk Shamim Iqbal. Sketch: TBS

Sk. Shamim Iqbal, a Certified Expert in Credit Management (CECM), is currently serving Social Islami Bank Limited (SIBL) as a faculty member at its training institute. 

Disclaimer: The views and opinions expressed in this article are those of the author and do not necessarily reflect the opinions and views of The Business Standard.