The loss optimisation of loan recovery decision times using forecast cash flows

TL;DR

This paper presents a theoretical and empirical approach to optimize loan recovery timing by forecasting cash flows using probabilistic and Markov models, aiming to minimize credit losses in secured lending portfolios.

Contribution

It introduces a novel multi-period optimization framework for recovery timing based on cash flow forecasts and delinquency measures, with empirical validation on mortgage data.

Findings

Forecasting cash flows influences recovery timing decisions.

Portfolio risk profile impacts optimal recovery thresholds.

The method reduces expected credit losses.

Abstract

A theoretical method is empirically illustrated in finding the best time to forsake a loan such that the overall credit loss is minimised. This is predicated by forecasting the future cash flows of a loan portfolio up to the contractual term, as a remedy to the inherent right-censoring of real-world `incomplete' portfolios. Two techniques, a simple probabilistic model as well as an eight-state Markov chain, are used to forecast these cash flows independently. We train both techniques from different segments within residential mortgage data, provided by a large South African bank, as part of a comparative experimental framework. As a result, the recovery decision's implied timing is empirically illustrated as a multi-period optimisation problem across uncertain cash flows and competing costs. Using a delinquency measure as a central criterion, our procedure helps to find a loss-optimal threshold at which loan recovery should ideally occur for a given portfolio. Furthermore, both the portfolio's historical risk profile and forecasting thereof are shown to influence the timing of the recovery decision. This work can therefore facilitate the revision of relevant bank policies or strategies towards optimising the loan collections process, especially that of secured lending.