Consumer finance: challenges for operational research

Consumer finance has become one of the most important areas of banking, both because of the amount of money being lent and the impact of such credit on global economy and the realisation that the credit crunch of 2008 was partly due to incorrect modelling of the risks in such lending. This paper reviews the development of credit scoring—the way of assessing risk in consumer finance—and what is meant by a credit score. It then outlines 10 challenges for Operational Research to support modelling in consumer finance. Some of these involve developing more robust risk assessment systems, whereas others are to expand the use of such modelling to deal with the current objectives of lenders and the new decisions they have to make in consumer finance. <br/

The relationship between default and economic cycles for retail portfolios across countries

In this paper, we collect consumer delinquency data from several economic shocks in order to study the creation of stress-testing models. We leverage the dual-time dynamics modeling technique to better isolate macroeconomic impacts whenever vintage-level performance data is available. The stress-testing models follow a framework described here of focusing on consumer-centric macroeconomic variables so that the models are as robust as possible when predicting the impacts of future shocks

Religion for a postsecular society? Discourses of gender, religion and secularity in the reception of BBC2’s The Monastery and The Convent

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Constructing ‘suspect’ communities and Britishness: mapping British press coverage of Irish and Muslim communities, 1974–2007

There exist many parallels between the experiences of Irish communities in Britain in the past and those of Muslim communities today. However, although they have both been the subject of negative stereotyping, intelligence profiling, wrongful arrest and prejudice, little research has been carried out comparing how these communities are represented in the media. This article addresses this gap by mapping British press coverage of events involving Irish and Muslim communities that occurred between 1974 and 2007. The analysis shows that both sets of communities have been represented as ‘suspect’ to different degrees, which the article attributes to varying perceptions within the press as to the nature of the threat Irish and Muslim communities are thought to pose to Britain. The article concludes that a central concern of the press lies with defending its own constructions of Britishness against perceived extremists, and against abuses of power and authority by the state security apparatus

Evaluating Innovative Health Programs: Lessons for Health Policy

The Global Development Network’s (GDN) project “Evaluating Innovative Health Programs” (EIHP), funded by the Bill & Melinda Gates Foundation, seeks to inform policy on the effectiveness of health solutions that have the potential to improve health outcomes in developing countries. It evaluates the impact of nineteen programs from across developing and transition countries that focus on the health-related Millennium Development Goals (MDGs) of reducing child and maternal mortality, and halting and reversing the trend of communicable diseases such as HIV/AIDS, malaria and other diseases. The policy implications of the diverse set of interventions are distinguished between programs that involved earmarking resources, changing incentives, and developing innovative methods of health care delivery.Millennium Development Goals; child and maternal health; communicable diseases; impact evaluation; capacity building; Asia; Africa; Latin America

Methods for Evaluating Innovative Health Programs (EIHP): A Multi-Country Study

Designed as a global research initiative, the EIHP project aims at adding to the evidence base of health interventions that have the potential to improve health outcomes in Africa and Asia. The project focuses on rigorous, quantitative evaluations of innovative local initiatives that address the Millennium Development Goals for health: reductions in child and maternal mortality and communicable diseases. This overview brings together the outcomes and lessons from the project for evaluation methods. It draws together the methodological implications of carrying out impact evaluations under very different settings and emphasizes the need to build in evaluations in project designs.Millennium Development Goals; child and maternal health; communicable diseases; impact evaluation; capacity building; Asia; Africa; Latin America

Mining whole sample mass spectrometry proteomics data for biomarkers: an overview

In this paper we aim to provide a concise overview of designing and conducting an MS proteomics experiment in such a way as to allow statistical analysis that may lead to the discovery of novel biomarkers. We provide a summary of the various stages that make up such an experiment, highlighting the need for experimental goals to be decided upon in advance. We discuss issues in experimental design at the sample collection stage, and good practise for standardising protocols within the proteomics laboratory. We then describe approaches to the data mining stage of the experiment, including the processing steps that transform a raw mass spectrum into a useable form. We propose a permutation-based procedure for determining the significance of reported error rates. Finally, because of its general advantages in speed and cost, we suggest that MS proteomics may be a good candidate for an early primary screening approach to disease diagnosis, identifying areas of risk and making referrals for more specific tests without necessarily making a diagnosis in its own right. Our discussion is illustrated with examples drawn from experiments on bovine blood serum conducted in the Centre for Proteomic Research (CPR) at Southampton University

When to rebuild or when to adjust scorecards

Data-based scorecards, such as those used in credit scoring, age with time and need to be rebuilt or readjusted. Unlike the huge literature on modelling the replacement and maintenance of equipment there have been hardly any models that deal with this problem for scorecards. This paper identifies an effective way of describing the predictive ability of the scorecard and from this describes a simple model for how its predictive ability will develop. Using a dynamic programming approach one is then able to find when it is optimal to rebuild and when to readjust a scorecard. Failing to readjust or rebuild a scorecard when they aged was one of the defects in credit scoring identified in the investigations into the sub-prime mortgage crisis

Improving credit scoring by differentiating default behaviour

We present a methodology for improving credit scoring models by distinguishing two forms of rational behaviour of loan defaulters. It is common knowledge among practitioners that there are two types of defaulters, those who do not pay because of cash flow problems (‘Can’t Pay’), and those that do not pay because of lack of willingness to pay (‘Won’t Pay’). This work proposes to differentiate them using a game theory model that describes their behaviour. This separation of behaviours is represented by a set of constraints that form part of a semi-supervised constrained clustering algorithm, constructing a new target variable summarizing relevant future information. Within this approach the results of several supervised models are benchmarked, in which the models deliver the probability of belonging to one of these three new classes (good payers, ‘Can’t Pays’, and ‘Won’t Pays’). The process improves classification accuracy significantly, and delivers strong insights regarding the behaviour of defaulter

Stress testing credit card portfolios: an application in South Africa

Motivated by a real problem, this study aims to develop models to conduct stress testing on credit card portfolios. Two modelling approaches were extended to include the impact of lenders’ actions within the model. The first approach was a regression model of the aggregate losses based on economic variables with autocorrelations of the errors. The second approach was a set of vintage-level models that highlighted the months-on-book effect on credit losses. A case study using the models was described using South African credit card data. In this case, the models were used to stress test the credit card portfolio under several economic scenarios