Simulation-based optimisation of the timing of loan recovery across different portfolios

TL;DR

This paper introduces a simulation-based method for optimizing the timing of loan recovery decisions in banks, considering financial losses, costs, and delinquency measures to improve collection policies.

Contribution

The paper presents a novel expert system that objectively compares delinquency thresholds and adapts to different credit risk scenarios and delinquency measures.

Findings

Thresholds exist across various default risks and loss rates.

The procedure responds well to systematic and episodic delinquencies.

It enhances decision-making over arbitrary discretion.

Abstract

A novel procedure is presented for the objective comparison and evaluation of a bank's decision rules in optimising the timing of loan recovery. This procedure is based on finding a delinquency threshold at which the financial loss of a loan portfolio (or segment therein) is minimised. Our procedure is an expert system that incorporates the time value of money, costs, and the fundamental trade-off between accumulating arrears versus forsaking future interest revenue. Moreover, the procedure can be used with different delinquency measures (other than payments in arrears), thereby allowing an indirect comparison of these measures. We demonstrate the system across a range of credit risk scenarios and portfolio compositions. The computational results show that threshold optima can exist across all reasonable values of both the payment probability (default risk) and the loss rate (loan collateral). In addition, the procedure reacts positively to portfolios afflicted by either systematic defaults (such as during an economic downturn) or episodic delinquency (i.e., cycles of curing and re-defaulting). In optimising a portfolio's recovery decision, our procedure can better inform the quantitative aspects of a bank's collection policy than relying on arbitrary discretion alone.