The client wanted to have a model to identify post loan behavior of defaulters, a credit risk assessment model for better decision making, and solution to minimize documentation errors.
- We built a team of sector data scientists and data analysts to create insight generation and develop ML & AI based models.
- We designed and implemented ML based hybrid early warning model predicting EMI default- post loan disbursal.
- We designed ML based hybrid customer credit risk assessment model – pre loan disbursal.
- Designed Deep Learning Algorithms for Selfie and Photo match based on facial landmarks.
- Designed and Deployed Name fuzzy match algorithm.
- We generated valuable insights based on above models and did segmentation of customer base and evaluated risk associated with each segment.
Benefits & Outcomes
- Identified distress customers early on which supported client’s collection team.
- Enabled client to forecast possible loss projections.
- Automated majority of underwriting process which helped credit underwriters in rational decision making and increased their work efficiency.
- Algorithms minimized errors in name matching and face recognition to greater extent.
- Models and algorithms to be evolved with changing human behavior.
Events like COVID Pandemic corrupts the data making it futile.