Degree

Master of Business Administration Executive

Faculty / School

School of Business Studies (SBS)

Year of Award

2024

Advisor/Supervisor

Dr. Sana Tauseef,Associate Professor and Director QEC, Department of Finance

Project Type

MBA Executive Research Project

Access Type

Restricted Access

Executive Summary

The project aims to deploy Logistic Regression / Scoring techniques to gain operational and cost level efficiency for TheBank. Credit Scoring (based on Logistic Regression) to predict the risk of default amongst a portfolio of customers is a widely applied concept in Pakistan. However, employing scoring techniques to identify risk level for collection / recovery procedures (Collection Scoring) to predict odds of recovery of outstanding once default has occurred is a relatively new / unapplied concept. This report covers detailed steps taken, along with processes applied to develop and evaluate a Collection Scorecard for TheBank. Additionally, the report also covers the possible implementation strategy for TheBank to gain both operational and cost level efficiencies. In this report, the authors demonstrate that it is indeed possible (to a great extent) to develop an effective collection scorecard on the basis of the data available with TheBank to effectively bifurcate customers on the basis of their risk levels. Further, on basis of this bifurcation / prediction, TheBank can customize its collection / recovery strategies for different set of customers to both tweak and optimize their collection / recovery efforts to potential realize both cost savings and performance enhancement in terms of collection from defaulted customers.

Pages

ix, 104

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