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/

Operations research in 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 the 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 ten challenges for Operational Research to support modelling in consumer finance. Some of these are to developing more robust risk assessment systems while 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 financ

Commercial bank load loss recoveries

We present a new approach to analyse historical recovery rates on distressed bank assets. Our approach uses banks’ reported impaired assets and the corresponding specific provisions. The dynamics and drivers of this credit loss recovery proxy are studied for a comprehensive sample of Australian banks from 1989 to 2005. We find that macroeconomic and bank-specific factors influence banks’ estimates of loan loss recoveries, consistent with banks smoothing their earnings. In contrast with findings based on prices of distressed corporate bonds, banks record lower recoveries in years of strong economic growth

The Impact of “Rollover” Contracts on Switching Costs in the UK Voice Market : Evidence from Disaggregate Customer Billing Data

In February 2008, British Telecommunications (BT) introduced automatically renewing, or “rollover”, contracts into the UK market for fixed-voice telephone service. These contracts included a 12-month Minimum Contract Period (MCP) with associated Early Termination Charges (ETCs). Unless customers opted out, at the end of the 12 months they would automatically be rolled over into a new MCP and face new ETCs if they later wished to leave BT. Using a unique, disaggregate, customer billing dataset, we measure the impact of rollover contracts on BT customers’ decision to switch to another provider. We find that, controlling for the effects of tenure, broadband purchase, price discounts, and self-selection, rollover households switch after their first MCP 34.8% (54.8%) less than comparable customers on standard plans (fixed-term contracts). These imply rollover contracts induce switching costs on the order of 33.0% of the monthly price of the average BT fixed-voice telephone service. This raises significant concerns about the competitive effects of such contracts n media and telecommunications markets.

Bagging and boosting classification trees to predict churn.

In this paper, bagging and boosting techniques are proposed as performing tools for churn prediction. These methods consist of sequentially applying a classification algorithm to resampled or reweigthed versions of the data set. We apply these algorithms on a customer database of an anonymous U.S. wireless telecom company. Bagging is easy to put in practice and, as well as boosting, leads to a significant increase of the classification performance when applied to the customer database. Furthermore, we compare bagged and boosted classifiers computed, respectively, from a balanced versus a proportional sample to predict a rare event (here, churn), and propose a simple correction method for classifiers constructed from balanced training samples.Algorithms; Bagging; Boosting; Churn; Classification; Classifiers; Companies; Data; Gini coefficient; Methods; Performance; Rare events; Sampling; Top decile; Training;

Collections policy comparison in LGD modelling

This paper discusses the similarities and the differences in the collection process between in house and 3rd Party collection. The objective is to show that although the same type of modelling approach to estimating Loss Given Default (LGD) can be used in both cases the details will be significantly different. In particular the form of the LGD distribution suggests one needs to split the distribution in different easy in the two cases as well as using different variables. The comparisons are made use two data sets of the collections outcomes from two sets of unsecured consumer defaulters<br/