Credit risk management remains a significant challenge for banks given the inefficient data management, limited view of risk measures, lack of risk assessment tools, and less than intuitive visualisation process into the borrowers’ ability to pay back. A thorough assessment of the borrowers’ capability and complete understanding of loan loss reserve is crucial to managing credit risk exposure and mitigating losses. In this scenario, traditional scorecards by themselves are no longer enough to determine credit lending.

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The way out? With the rising popularity of newer data sources including smartphone metadata, financial organisations are now embracing artificial intelligence (AI), machine learning (ML) and other advancements in digitisation for better credit risk management. Combining machine learning with traditional scorecards helps continuously run different combinations of variables to arrive at learnings from data gathered across browsing history, SMS, emails, downloaded files and calendar usage. This helps predict variable interactions and clearly identify strengths and weaknesses associated with a loan, improving both accuracy and time taken in credit decision-making. No wonder, 31% of capital market professionals think that the use of non-traditional data leads to better credit decisions than just relying on detached data. In fact, in 2017, JPMorgan Chase introduced COiN, a contract intelligence platform that uses machine learning to review 12,000 annual commercial credit agreements. This helped them reduce review time from 360,000 hours per year to seconds.  Here are 4 reasons why companies across industries should leverage AI to mitigate credit risk:

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Reduce time to credit decisions

‍Financial institutions spend a significant amount of money and time n physically verifying applicant details. AI can be leveraged to extract meaningful insights from unstructured alternate data sources such as text and images and to verify the authenticity of the information provided by applicants without the necessity for physical investigation. This helps significantly reduce loan or corporate credit processing time.

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Improve customer experience with intelligent product selection

Today’s digitally-savvy customers prefer personalised products that are relevant and customised to their needs. Intelligent analysis of smartphone metadata and transactional data help zero in on the most pertinent customer information to offer a pre-selection of suitable credit products. This, in turn, helps enhance the customer experience.

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Check creditworthiness through smart applications

Once the customer zeroes in on the credit products, smart credit scoring apps using AI-based algorithms can help analyse customer behaviour in real-time. This helps extract and contextualise relevant information to verify the customer’s creditworthiness and calculate the maximum credit limit. AI can also be used to enhance decisions for structured financing through reliable estimates of future cash flow and the ability to pay back debt.

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Meet regulatory requirements

It is crucial for banks to meet the regulatory requirement of leveraged transactions which requires them to ensure due diligence for granting loans or refinancing existing transactions. With high quality data input such as smartphone metadata, AI applications help reduce data bias and create a transparent approach to enable credit scoring.

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For banks, using AI and machine learning to enrich the credit risk management process not only brings in greater efficiency but also enhance fraud detection mechanisms and reduce time to market. AI’s power to adopt new data sources and analyse with more granularity is the way forward to ensure accurate credit scores, improve credit risk detection and engage better with customers.

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