  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.
