Can AI Predict and Mitigate NPAs?
Published on : Thursday 12-01-2023
It is important to continually evaluate the performance of the NPA analytics system and make any necessary improvements, says Utpal Chakraborty.
Standard definition of ‘Non-Performing Assets’, NPAs are loans or advances that have been classified as unsatisfactory, uncertain or loss 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.
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 overall performance of those assets and make more informed decisions about lending and investment.
AI/ML solutions are being used to analyse 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 analyse current loans and advance to predict which ones are at risk of becoming NPAs.
There are several ML algorithms that can be used to analyse and predict NPAs, including Decision Tree, Random Forest, Support Vector Machine (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 analyse a large amount of data and identify complex relationships between different factors. The choice of ML algorithm for analysing and predicting NPAs will depend on the specific needs and goals of the financial institution, as well as the characteristics of the data being analysed.
On a very high level, building a NPA analytics system involves several steps like collecting data, preprocessing data, analysing data, building and implementing machine learning models, etc. And of course, evaluating and improving the system is a continuous process.
The first step in building a NPA analytics system is to collect data about the loans or advances that are to be analysed. 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 analyse 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 analysed, 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 revolutionise the entire NPA analytics landscape and many of the banks and financial institutes are already leveraging these amazing capabilities as part of mitigation strategy.
(Views expressed above are personal opinions of the author.)
Utpal Chakraborty is Chief Digital Officer at Allied Digital Services Ltd. A former Head of Artificial Intelligence at YES Bank, he is an eminent AI, Quantum and Data Scientist, AI researcher and Strategist, having 21 years of industry experience, including working as Principal Architect in L&T Infotech, IBM, Capgemini and other MNCs in his past assignments. Utpal is a well-known researcher, writer (author of 6 books) and speaker on Artificial Intelligence, IoT, Agile & Lean at TEDx and conferences around the world.
His recent research on machine learning titled “Layered Approximation for Deep Neural Networks” has been appreciated in different premier conferences, institutions, and universities.