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

Vertical occupational mobility and its measurement

This paper describes a number of alternative approaches to devising a vertical occupational scale and compares the outcomes of different scales on calculations of occupational mobility. The paper describes the conceptual issues relevant to calculating occupational mobility and documents the measurement error embedded in the choice of measure, as applied to different data sets. The ranking schemes used include SOC (9) major codes ranked by mean occupational hourly earnings, Hope-Goldthorpe collapsed 36-point scores, a 15-category SOC ranking based on educational qualifications, and a 77 category ranking based on 2-digit SOC90 occupations, wage rates, educational qualifications, training and job tenure. These ranking schemes are applied to data from the 1958 NCDS cohort between the ages of 23 to 33 and 33 to 42, and to 1.25 year transitions in the Quarterly Labour Force Survey panel data. The calculations carried out show that variations in the extent of vertical occupational mobility, both upward and downward, had systematic elements. The extent of mobility was found to vary by the composition of the individuals´ data particularly in terms of lifecourse stages and gender, the number of categories in the ranking scheme, attrition in the data and flows out of employment over the mobility period, and changes in labour market conditions over time. However, the sizes of these effects were very variable

Improving customer churn prediction by data augmentation using pictorial stimulus-choice data

The purpose of this paper is to determine the added value of pictorial stimulus-choice data in customer churn prediction. Using Random Forests and 5 times 2 fold cross-validation, this study analyzes how much pictorial stimulus choice data and survey data increase the AUC of a churn model over and above administrative, operational and complaints data. The finding is that pictorial-stimulus choice data significantly increases AUC of models with administrative and operational data. The practical implication of this finding is that companies should start considering mining pictorial data from social media sites (e.g. Pinterest), in order to augment their internal customer database. This study is original in that it is the first that assesses the added value of pictorial stimulus-choice data in predictive models. This is important because more and more social media websites are focusing on pictures

Ensembles of probability estimation trees for customer churn prediction

Customer churn prediction is one of the most, important elements tents of a company's Customer Relationship Management, (CRM) strategy In tins study, two strategies are investigated to increase the lift. performance of ensemble classification models, i.e (1) using probability estimation trees (PETs) instead of standard decision trees as base classifiers; and (n) implementing alternative fusion rules based on lift weights lot the combination of ensemble member's outputs Experiments ale conducted lot font popular ensemble strategics on five real-life chin n data sets In general, the results demonstrate how lift performance can be substantially improved by using alternative base classifiers and fusion tides However: the effect vanes lot the (Idol cut ensemble strategies lit particular, the results indicate an increase of lift performance of (1) Bagging by implementing C4 4 base classifiets. (n) the Random Subspace Method (RSM) by using lift-weighted fusion rules, and (in) AdaBoost, by implementing both