Leased items depreciates rapidly and seizure of collateral does not recover the debt
The company currently cherry picks customers who seldom go bad
They want to expand customer base while controlling risk
For this they want a scorecard to replace rule driven underwriting for better screening
Company book identified only 2.5% bad lease – payment history data was fraught with inconsistent figures
After incorporating liquidation/insolvency/dissolution status and rating from credit bureau record, the incidence was boosted to 12%. The process classified non takers of loan to Good and Bad by a logical method and not by reject inference
Advanced Statistical Techniques
Equipment Leasing Company
Credit Scoring
UK
Model developed by R program
2 scorecards with and without credit bureau ratings were delivered
Discriminatory power of the scorecards were high as seen from high KS and GINI
Scorecard developed by Statistical method
Scrutiny restricted to high scorers reducing manual work by a factor of 5 -10
