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USE CASE

Buy Now, Pay Later (BNPL)

Objective

  • Future-proof BNPL offerings with AI to manage risk, respond quickly to new revenue opportunities and shift as needed to keep pace with the regulatory environment 

  • Harness alternative data beyond traditional credit checks (Behavioral scores, telco info, transactional data, etc) to support AI based credit assessment that will speed customer and merchant acquisition

Outcome

  • Match BNPL customers with appropriate credit offers based on their history 

  • Help BNPL providers retrain risk models for significant improvements in decisioning performance. 

  • Provide transparency into automated decisions for pre-approved BNPL loans and recommended payment schedules to ensure customers can afford repayments. 

Business Value

  • Make digital BNPL onboarding seamless and secure

  • KYC to ensure BNPL offerings remain fully compliant 

  • Integrated fraud detection to find fraud patterns quicker and more efficiently

  • Identify propensity-to-buy customers for up and cross sell

H2O's AI and Data Approaches

  • Use datasets with meta-features in a networked context to generate accurate credit scores of individuals by training machine learning models to generate credit scoring models

  • Capture Network Properties of the Customers

  • Predictive Modelling to give better credit scores.

  • Create models and use custom H2O recipes specifically built for generating features/variables that provide associated information about defaulting behavior. 

  • Create models and use custom recipes that generate features/variables that provide a probabilistic estimation of whether customers will perform any of such actions or not i.e. a propensity score.

Resources

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