No. 005

Paints Receivables ML Model

DataAI

Seen/ Management Intern, Digital and Finance · Apr 2024 to May 2024

Project entry

Moves

• Model the Risk

  Built an ML model predicting accounts-receivable risk and per-dealer default probability from 2+ years of dealer data.

• Validate It

  Validated the model at about 93% accuracy before it went near a real credit decision.

• Design the Framework

  Built a risk-based dynamic credit-limit framework segmenting dealers by payment behaviour and credit risk.

• Pitch It Up

  Presented findings and the framework to senior finance and digital leadership.

Outcome

Projected to cut overdue accounts by roughly 25%, recognized by the CDO, and led to a Pre-Placement Offer.