Standard definition of “Non-Performing Assets”, NPAs are loans or advances that have been classified as unsatisfactory, uncertain or lost assets by a bank or a financial institution as they are no longer generating income. NPAs can be a significant burden on a financial institution’s balance sheet as they represent a loss of value and can lead to reduced profits and increased risk.


SwissCognitive Guest Blogger:  Utpal Chakraborty, Chief Digital Officer, Allied Digital Services Ltd., AI & Quantum Scientist – “Can AI Predict and Mitigate NPAs?” Image by Freepik


Advanced analytics powered by Machine Learning (ML) can be used to identify the root causes of NPAs, such as late or missed payments, poor credit history, high debt-to-income ratio, mismanagement, fraud or changes in market conditions etc. This information can be used to develop strategies to prevent future NPAs and improve the overall performance of those assets and make more informed decisions about lending and investment.

AI/ML solutions are being used to analyze and predict NPAs in a number of ways. For example, machine learning algorithms can be trained on historical data to identify patterns and trends that may indicate the likelihood of a loan becoming an NPA. These algorithms can then be used to analyze current loans and advance to predict which ones are at risk of becoming NPAs.

There are several ML algorithms that can be used to analyze and predict NPAs, including Decision Trees, Random Forests, Support Vector Machines (SVM) and even Neural Networks. These algorithms are often used to identify patterns and trends in data and can be effective in identifying the root causes of NPAs. It can also help to improve the accuracy of predictions and can be effective in predicting the likelihood of a loan becoming an NPA.

Neural networks can be particularly effective in predicting NPAs, as they can analyze a large amount of data and identify complex relationships between different factors. The choice of an ML algorithm for analyzing and predicting NPAs will depend on the specific needs and goals of the financial institution, as well as the characteristics of the data being analyzed.

On a very high level, for building an NPA analytics system involves several steps like collecting data, preprocessing data, analyzing data, building and implementing a machine learning model etc. And of course, evaluating and improving the system is a continuous process.

The first step in building an NPA analytics system is to collect data about the loans or advances that are to be analyzed. This data may include information about the borrower, the loan terms, and the performance of the loan, repayment history, default rates, etc.

Once the data has been collected, it will typically need to be preprocessed to prepare it for analysis. This may include cleaning the data to remove any inconsistencies and transforming the data into a form that is suitable for use with ML algorithms.

The next step is to use ML algorithms to correlate and analyze the data and identify patterns and trends that may indicate the likelihood of a loan becoming an NPA. This may involve training machine learning models on historical data and then using those models to make predictions about the performance of current loans.

After the data has been analyzed, the NPA analytics models can be implemented to monitor the performance of loans in real time. This may involve integrating the analytics system into the existing Loan Management Systems (LMS) and setting up alerts or notifications to alert relevant stakeholders when a loan is at risk of becoming an NPA.

It is also important to continually evaluate the performance of the NPA analytics system and make any necessary improvements to ensure that it is accurate and effective. This may involve incorporating new data sources, testing different AI algorithms, or adjusting the algorithm parameters. Overall, AI/ML has the potential to revolutionize the entire NPA analytics landscape, and many of the banks and financial institutes are already leveraging these amazing capabilities as part of mitigation strategy.