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AI and ML to enhance the efficiency of the credit sector

The ability of machine learning models to analyse with more granularity and offer a deeper analysis can improve both speed and accuracy of the risk models

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The wave of Artificial Intelligence (AI) and Machine Learning (ML) as a concept has swept across the world. The wave which started more as an intelligent machine competing against champions in mind games (Chess, Go), has traveled a long distance to shape and simplify the way we live, work, travel and communicate. A Japanese farmer’s family has been using deep learning-based methods to classify their crop production and hence ensures higher efficiency.  Saudi Arabian government has given citizenship to Sophia, the humanoid robot. Almost every industry is in the mode of exploring how it can be benefited by the application of artificial intelligence. In this article, we will cover how the credit risk management industry can reap the benefits of AI&ML.

Where and How AI&ML can be applied in Credit Risk Management?

Thanks to the rapid increase in information availability, the world’s most valuable resource are now data, the new oil of the digital era. As a popular adage states, “the more data you have, the more accurate your decision making will be.” The saying holds significant value in credit risk management where access to customer’s information is of utmost importance. A lot of activities that happen in the credit management cycle can be automated with the help of Artificial Intelligence leading to higher efficiency. Many banks and financial institutions have started focusing on this arena. ENBD, a major bank in UAE have introduced Pepper, the robot which engages with the customer and ensures process improvement in customer management.

The core area of credit risk management has been assessing the creditworthiness of the customer and assigning a risk score to him/her. Currently, traditional statistical techniques are deployed to assign credit scores based on which a customer’s loan application is either approved or rejected. The traditional model, white box in nature also explain to the consumers the factors that contribute to the credit score assigned. Most of the Machine Learning algorithms, on the other hand, are a black box and hence cannot be directly applied in predicting the credit risk of the customer. There are several steps that are being followed in developing predictive models to assign the credit risk. ML-based algorithms can be applied for the intermediate steps. The ability of machine learning models to analyse with more granularity and offer a deeper analysis can improve both speed and accuracy of the risk models.

The other way ML enabled algorithms can help in credit scoring models is to segment the customers using unsupervised and supervised learning techniques. So, while the final model is still the white box, it is enabled by the power of capturing nonlinearity and adopting newer data sources. This ML blended methodology for standard scorecard development is the way forward for risk scorecard development.

In the era of social media and the advancement of technology, a plethora of information is available for a customer today. Artificial intelligence algorithms can message the unstructured data sources (text, image, voice, sensor) and extract meaningful insight from these data sources which can be coupled with the existing information available about the customer to enhance the decision- making process. The banks and financial institutions today spend a significant amount of money in the verification of details provided by the applicant to decide on her/his authenticity. Alternate data sources can be leveraged to verify the authenticity of the information provided by the applicants without physical investigation. This will lead to a significant reduction in the processing cost for each loan.

Similarly, Unstructured data that a bank or financial institution collects during their day to day business environment can also be leveraged to make different decision processes better and more powerful. For example, the notes captured during the personal discussion with the customer at the time of application is a very effective source of information in assessing the risk of the customers. The conversations of the call center professionals with the customers can be leveraged to generate a series of insights about the customer which can be leveraged for cross-sell potential assessment and many other effective measures.

The other benefits of AI and ML include fraud prevention, better customer experience, significantly better racking across services, reduced costs through better efficiency, and reduced time to market.

Conclusion

Artificial Intelligence and Machine Learning will enrich the credit risk management process, bring more efficiency, leverage more alternate data sources and look dramatically different by 2025. Its capacity to deal with numerous risk types while getting ready for new mandates and consenting to current ones is expected to make it even more invaluable to financial institutions, and its part in making satisfying customer experiences will most presumably change it into a key benefactor. The overall credit scoring models will remain to be based on traditional methods but will be strengthened more by leveraging ML enabled methodologies. The decision of creditworthiness of the NTC customers will be more accurate and scientific. The process automation will continue ensuring more and more efficiency in the credit risk management process.

Authored by Anindya Sengupta, Vice President – Analytics, CRIF India


If you have an interesting article / experience / case study to share, please get in touch with us at [email protected]

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1 Comment
  1. Valeria says

    Using AI/ML data analytics module can be developed that can forecast credit risks based on the user parameters compared to the custom unique dataset of huge insurance history.

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