Case Study

AI-Powered Loan Default Prediction

A Leading Bank in APAC Predicts Potential Defaults before Credit Sanctioning.

A leading bank in the Asia Pacific extended its loan product offerings to customers in the unbanked sector, leveraging Artificial Intelligence to predict potential defaults before credit sanctioning.
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Industry & Region: Banking – APAC (Asia Pacific)
Technology Stack: Google Cloud Platform (GCP), Google Cloud Storage (GCS), DataFlow, TensorFlow, Vertex AI, BigQuery
Client Overview
Our client is a leading bank in the APAC (Asia Pacific) region with a broad customer base. They are well known for their financial inclusion initiatives as they extend their banking products to unbanked populations.
Business Challenge
The primary challenge for our client was to mitigate the risk of loan defaults while extending their loan products to the unbanked sectors in APAC. As unbanked customers do not have a formal financial track record and credit history, normal credit scoring mechanisms cannot be used to appraise loans for them. Credit risk assessment became a difficult task in the process of loan appraisals as the customers had no previous data or credit history, based on which the assessment is usually done. The client was looking for an innovative solution to overcome these challenges in analyzing credit risk for their unbanked customers.
Solution Offered
Our team of specialists at KANINI worked around the challenges and came up with an AI-powered predictive modeling solution to address the challenges of the client. The model used supervised learning techniques to classify defaulting customers and unsupervised learning techniques for anomaly detection in customer segments. Dataflow was used to retrieve data like customer profiles and transaction histories from various data sources and then ingest it into Big Query, which would pre-process and transform that data before finally feeding it into the AI (Artificial Intelligence) model built on Vertex AI.
This model would analyze various customer data parameters such as demographics, occupation data, transaction histories, etc. to predict the credit risk, using alternative credit scoring mechanisms.
Value Delivered
  • Enabled the loan officers in performing the credit risk assessment efficiently.
  • Helped the client achieve their growth objectives by expanding their loan products in APAC.
  • Made accurate loan default predictions and helped avoid NPAs (Non-Performing Assets) for the client.
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