Lifetime PD Analytics for Credit Portfolios: A Survey

42 Pages Posted: 24 Oct 2016 Last revised: 10 Nov 2016

Date Written: November 8, 2016


The recent publication of the IFRS 9 norms has emphasized the fact that a shared and comprehensive methodology for PD analytics on credit portfolios was still lacking. Credit risk assessment is often static and short term because the industry has focused on assessing risk over a one year horizon, pushed forward this way by common practices and by the regulatory framework. Dynamic aspects are crucial though. IFRS 9 requirements raise new issues regarding dynamic and long term risk assessment. Plenty of information is available for calibrating PD curves (scores, risk classes, risk class migrations, observed defaults, delinquencies...) and there is a large set of statistical methods at hand as well. This paper surveys the main the available models for PD analytics. We compare the different methods for calibrating PDs and give the pros and cons of each one. We focus on retail lifetime PDs because, contrary to the case of wholesale portfolios, no consensus has emerged yet on the way to calibrate lifetime PDs for retail exposures.

Keywords: Lifetime PD, IFRS 9, PD term structure, probability of default, scoring, retail, cure rate, vintage analysis, risk factors, hazard model

JEL Classification: G21, G28, G13

Suggested Citation

Brunel, Vivien, Lifetime PD Analytics for Credit Portfolios: A Survey (November 8, 2016). Available at SSRN: or

Vivien Brunel (Contact Author)

Société Générale ( email )

Paris-La Défense, Paris 92987

Register to save articles to
your library


Paper statistics

Abstract Views
PlumX Metrics