Artificial Intelligence for Reducing the Impact of Bad Loans on Bank

The COVID-19 outbreak is not only a health crisis but also an economic shock. Economic activities have been abruptly halted by the pandemic. Nations around the world are struggling to contain the virus and its economic impacts. The pain has been felt across industries – due to lack of demand and employee layoffs, the cash flow of many businesses has been collapsing. As a result, there will be a huge spike in both commercial and retail non-performing assets as borrowers struggle to repay their loans.

Banking sector is responsible for restoring social and economic development across the globe. In developing nations, banks play a bigger role than mere enablers of financial intermediation and support the additional responsibility of achieving the nation’s social agenda. Without preventive actions to resolve the banking crisis, there could be catastrophic failure across the market.    


Banks in the world are holding a massive volume of debt. Due to the current economic crisis, the recovery of the debt can be a daunting task for financial institutions. Many world’s largest financial institutions collapsed and dozens were forced to seek bailouts from world governments during the 2009 financial crisis.

Recent development in artificial intelligence is likely to play a major role in protecting banks against and responding to the financial crisis, enabling alternative channels of interaction with defaulters that will ultimately achieve better outcomes with more revenue and more satisfied customers. Here is how banks can use AI to implement debt restructuring efforts for better recovery, fewer calls and customer satisfaction.


Innovative approach to default management

Financial institutions have traditionally interacted with delinquent customers or potentially delinquent customers over phone calls or emails in their attempts to drive collections. While banks help customers in the crisis, the methods of collection are considered intrusive and often create a negative impact leading to customer churn.

Financial institutions can leverage alternative channels of communication such as mobile apps, online portals and social media messaging platforms to reach out to customers or vice versa before delinquency takes place. It will enable banks to improve interaction and default management.

Intelligent chatbots guide customers to suit their specific needs and reduce human interactions across default management processes, such as defining effective recovery strategy, loss mitigation, asset management, etc. Such methods of communication save customers embarrassment associated with default and poor financial management. As a result, customers become more responsive to banks’ efforts for collection. Such channels of communication will encourage customers to reach out to their lenders when faced with default problems.


Adoption of AI in loan default management

Financial institutions have traditionally not focused on adopting digital technologies. Adopting digital technologies like artificial intelligence in loan default management can help banks restructure the customer journey. Collections is reminding customers to repay outstanding balance; it is suggesting a way out of the crisis. AI can play a big role in bridging this gap between banks and customers. The AI-enabled system will also deliver efficiencies and free up bank employees for value-added services.

  • AI in identifying potential defaulters: The significant value of a loan is linked to the creditworthiness of the individual or business that availed the loan. The more data lenders have about a customer, the better they can assess his creditworthiness. Banks can get a better insight into customers by combining information from their accounts and their online and social activities. It will help banks to detect potential instances of default and take preventive actions before default actually occurs.  

For example, technologies like machine learning (ML) and natural language processing (NLP) can be used to analyze an individual borrower’s digital footprints to identify their adverse financial situations. Suppose the bank account of an individual shows a sudden decline in his income and analysis of his digital activity suggests job loss as a possible reason, in such case, banks can proactively identify the potential default.

  • Collection strategy definition: Collection strategies are now personalized for individual customers, making mass personalization possible in the default management space. To build a right collection strategy for each customer, banks must evaluate the past repayment strategies that brought defaulters to the mainstream, as well as methods that did not work. The past behavior of data points can help banks build predictive models based on persona segmentation. Given the changing market and customer dynamics, predictive models will need to be updated from time to time. AI will play an important role in finding critical new factors and tweaking the existing strategies to ensure that the model is in line with the evolving conditions, thereby improving its predictive power. As a result, bots can be adopted to define the optimal strategy based on an evaluation of past behavior of data points gained by leveraging the ML algorithms and facilitate the best possible collection outcome.  


  • Loss mitigation: To avoid foreclosure and loss of ownership, lenders are under obligation to offer loss mitigation strategy to credit delinquents. Banks can leverage ML models to analyze customer profile and identify the optimal mitigation strategy. The intelligent bots can prefill the customer information in the loss mitigation application, which will simplify the process for customers.
  •  Recovery strategy: Banks can implement AI technologies like ML and data science to assess the costs associated with default and determine the right strategy. Intelligent bots can be implemented to handle routine queries to minimize the costs and for faster turnaround. Lenders can deploy AI technologies to evaluate things like percentage of recovery, cost of recovery, timelines, feedback, the process used and a lot more. The data collected can be used for the benefits of internal and external stakeholders such as collection agencies.


Bottom line

The potential of AI to improve how lenders engage with customers for collection and recovery is immense. Banks are evaluating the implementation of AI for non-intrusive yet effective methods of interaction with delinquent customers to improve their experience and bring them back into the mainstream. AI-powered solutions can potentially enhance customer experience and deliver commercial benefits for lenders.  

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