Net flow rates versus roll rates as non-performing consumer loans forecasting methodologies

Authors

DOI:

https://doi.org/10.46661/revmetodoscuanteconempresa.5489

Keywords:

roll rates, net flow rates, consumer credit risk, non-performing loans, default, delinquency, expected loss

Abstract

Roll rates and net flow rates can be seen as the evolution of ageing of accounts receivable and Markov chains. They are accepted methodologies to model the behavior of non-performing consumer loans by buckets and to predict losses, but we find that quite often they are wrongly used as interchangeable concepts, although roll rates track individual accounts across buckets in consecutive months and net flow rates just compare consecutive buckets in consecutive months. We determine their matrices of transition probabilities and analyze them in both stationary and steady-state conditions. Net flow rates have many advantages over roll rates, but a quite important finding for financial institutions and supervisors is that historical flow rates are not conservative for forecasting: when the level of new delinquencies soars, contemporary flow rates will tend to be lower than they would be in steady-state conditions, creating a feeling of false confidence and leading to the underestimation of future losses.

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Author Biography

Francisco de Asis de Ribera Martín, Universidad Pontificia Comillas (España)

Estudiante de doctorado

Programa de Doctorado en Competitividad Empresarial y Territorial, Innovación y Sostenibilidad

References

Acenden (2012). Overview of arrears roll rate. UK and Ireland mortgage and property monthly, 6, 1-3. https://www.acenden.com/docs/default-source/newsletter-uk-ireland-mortgage-property-monthly/uk-and-ireland-mortgage-and-property-monthly-issue-6-nov-2012.pdf.

Anderson, R. (2007). The credit scoring toolkit: theory and practice for retail credit risk management and decision automation. Oxford University Press.

Clemons, E., & Thatcher, M. (1998). Capital One: Exploiting an information-based strategy. In System Sciences (Ed.), Thirty-First Hawaii International Conference, 6, 311-320. https://doi.org/10.1109/HICSS.1998.654788

Coffman, J.Y., & Chandler, G.G. (1983). Applications of performance scoring to accounts receivable management in consumer credit. Krannert Graduate School of Management, Purdue University. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.197.9454&rep=rep1&type=pdf

Cyert, R.M., & Thompson, G.L. (1968). Selecting a portfolio of credit risks by Markov chains. The Journal of Business, 41(1), 39-46.

Cyert, R.M., & Trueblood, R.M. (1957). Statistical sampling techniques in the aging of accounts receivable in a department store. Management Science, 3(2), 185-195.

Cyert, R.M., Davidson, H.J., & Thompson, G.L. (1962). Estimation of the allowance for doubtful accounts by Markov chains. Management Science, 8(3), 287-303.

FDIC (2007). Risk Management Examination Manual for Credit Card Activities. Division of Supervision and Consumer Protection. https://www.fdic.gov/regulations/examinations/credit_card/pdf_version/

Hong Kong Monetary Authority (2006). Supervisory Policy Manual: Credit Card Business. Hong Kong Monetary Authority.

Kellett, D. (2011). UK Credit Card Loss Forecasting Using Markov Chain Models. In Capital One (Ed.), Credit Scoring and Credit Control XII conference. Edinburgh.

Office of the Comptroller of the Currency (2004). Retail Lending Examination Procedures. Comptroller's Handbook. U.S. Department of the Treasury. http://www.occ.gov/publications/publications-by-type/comptrollers-handbook/_pdf/retaillendingexaminationprocedures.pdf

Office of the Comptroller of the Currency (2015). Credit Card Lending. Comptroller’s Handbook. U.S. Department of the Treasury. https://www.occ.treas.gov/publications/publications-by-type/comptrollers-handbook/credit-card-lending/pub-ch-credit-card.pdf

Office of the Comptroller of the Currency (2016). Installment Lending. Comptroller’s Handbook. U.S. Department of the Treasury. https://www.occ.treas.gov/publications/publications-by-type/comptrollers-handbook/installment-lending/pub-ch-installment-lending.pdf

PwC (2015). IFRS 9: Credit Modelling and Implementation. http://www.pwc.com/ca

Rosenberg, E., & Gleit, A. (1994). Quantitative methods in credit management: a survey. Operations Research, 42(4), 589-613.

Santander Consumer Finance E.F.C. (2016). Securitization Fund Santander Consumer Spain Auto 2016-1. [Issue Prospectus]. https://www.cnmv.es/Portal/Consultas/Folletos/FolletosEmisionOPV.aspx?isin=ES0305124002

SAS (2014). CCAR: An Appraisal of Current Practices. https://www.sas.com/content/dam/SAS/en_us/doc/whitepaper1/ccar-appraisal-of-current-practices-107211.pdf

So, M.M., & Thomas, L.C. (2010). Modeling and model validation of the impact of the economy on the credit risk of credit card portfolios. Journal of Risk Model Validation, 4, 93-126.

Stretton, C., & Burra, P. (2011). Financial instruments impairment: Adapting to change. Johannesburg: Deloitte. https://www.iasplus.com/de/binary/safrica/1107impairmentaccounting.pdf

Published

2022-12-01

How to Cite

de Ribera Martín, F. de A. (2022). Net flow rates versus roll rates as non-performing consumer loans forecasting methodologies. Journal of Quantitative Methods for Economics and Business Administration, 34, 37–59. https://doi.org/10.46661/revmetodoscuanteconempresa.5489

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Articles