Traditional systems of determining credit worthiness takes into account the credit histories of potential borrowers, but this process might not give credit to deserving customers, who could pay back on time. Hence several Fintech companies are focusing on basing their customer evaluation on AI-based credit scoring for better understanding the risks associated with potential borrowers. Key indices which are used for classification are understand client’s demographics, business and industry, operations, processes, governance, strategies, management, finances and bank statements, IT returns, key customers and suppliers, major source of revenue, etc.

As against using traditional manual and gut-based methods of determining the creditworthiness, advantages of using an automated, intelligent, analytical and statistical ways of identifying risks using Machine Learning and Deep Learning include –

  1. Predicting defaulters and non-defaulters
  2. Save costs on manual processes like frequent or repeated site visits
  3. Predict insolvency
  4. Predict high loan balances of customers for frauds
  5. Segmentation of customers to strategize the offerings
  6. Identify the best and worst performing loan product
  7. Delinquency trends by location, etc.
  8. Reduce dependencies on Credit Bureaus
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