6 research outputs found
Simulation-based optimisation of the timing of loan recovery across different portfolios
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.Comment: Accepted by the journal "Expert Systems with Applications". 25 pages
(including appendix), 9 figures. arXiv admin note: text overlap with older
arXiv:1907.1261
Defining and comparing SICR-events for classifying impaired loans under IFRS 9
The IFRS 9 accounting standard requires the prediction of credit
deterioration in financial instruments, i.e., significant increases in credit
risk (SICR). However, the definition of such a SICR-event is inherently
ambiguous, given its reliance on comparing two subsequent estimates of default
risk against some arbitrary threshold. We examine the shortcomings of this
approach and propose an alternative framework for generating SICR-definitions,
based on three parameters: delinquency, stickiness, and the outcome period.
Having varied these parameters, we obtain 27 unique SICR-definitions and fit
logistic regression models accordingly using rich South African mortgage data;
itself containing various macroeconomic and obligor-specific input variables.
This new SICR-modelling approach is demonstrated by analysing the resulting
portfolio-level SICR-rates (of each SICR-definition) on their stability over
time and their responsiveness to economic downturns. At the account-level, we
compare both the accuracy and flexibility of the SICR-predictions across all
SICR-definitions, and discover several interesting trends during this process.
These trends and trade-offs can help in selecting the three parameters, as
demonstrated in our recommendations for defining SICR-events. In summary, our
work can guide the formulation, testing, and modelling of any SICR-definition,
thereby promoting the timeous recognition of credit losses; the main imperative
of IFRS 9.Comment: 30 pages (including appendix), 9180 words, 10 figure
The loss optimisation of loan recovery decision times using forecast cash flows
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.The Absa Chair in Actuarial Science, hosted at the University of Pretoria.https://www.risk.net/journal-of-credit-riskam2023Electrical, Electronic and Computer EngineeringInsurance and Actuarial Scienc
From footprint to evidence: An exploratory study of mining social data for credit scoring
National Research Foundation (NRF) Singapore under International Research Centre @ Singapore Funding Initiative; Pinnacle Lab for Analytics @ SM