Raquel Florez-Lopez

Effects of missing data in credit risk scoring. A comparative analysis of methods to achieve robustness in the absence of sufficient data

Abstract The 2004 Basel II Accord has pointed out the benefits of credit risk management through internal models using internal data to estimate risk components: probability of default (PD), loss given default, exposure at default and maturity. Internal data are the primary data source for PD estimates; banks are permitted to use statistical default prediction models to estimate the borrowers’ PD, subject to some requirements concerning accuracy, completeness and appropriateness of data. However, in practice, internal records are usually incomplete or do not contain adequate history to estimate the PD. Current missing data are critical with regard to low default portfolios, characterised by inadequate default records, making it difficult to design statistically significant prediction models. Several methods might be used to deal with missing data such as list-wise deletion, application-specific list-wise deletion, substitution techniques or imputation models (simple and multiple variants). List-wise deletion is an easy-to-use method widely applied by social scientists, but it loses substantial data and reduces the diversity of information resulting in a bias in the models parameters, results and inferences. The choice of the best method to solve the missing data problem largely depends on the nature of missing values (MCAR, MAR and MNAR processes) but there is a lack of empirical analysis about their effect on credit risk that limits the validity of resulting models. In this paper, we analyse the nature and effects of missing data in credit risk modelling (MCAR, MAR and NMAR processes) and take into account current scarce data set on consumer borrowers, which include different percents and distributions of missing data. The findings are used to analyse the performance of several methods for dealing with missing data such as likewise deletion, simple imputation methods, MLE models and advanced multiple imputation (MI) alternatives based on MarkovChain-MonteCarlo and re-sampling methods. Results are evaluated and discussed between models in terms of robustness, accuracy and complexity. In particular, MI models are found to provide very valuable solutions with regard to credit risk missing data.

Marketing Segmentation Through Machine Learning Models

Customer relationship management (CRM) aims to build relations with the most profitable clients by performing customer segmentation and designing appropriate marketing tools. In addition, customer profitability accounting (CPA) recommends evaluating the CRM program through the combination of partial measures in a global cost—benefit function. Several statistical techniques have been applied for market segmentations although the existence of large data sets reduces their effectiveness. As an alternative, decision trees are machine learning models that do not consider a priori hypotheses, achieve a high performance, and generate logical rules clearly understood by managers. In this article, a three-stage methodology is proposed that combines marketing feature selection, customer segmentation through univariate and oblique decision trees, and a new CPA function based on marketing, data warehousing, and opportunity costs linked to the analysis of different scenarios. This proposal is applied to a large insurance marketing data set for alternative cost and price conditions showing the superiority of univariate decision trees over statistical techniques.

Modelling Credit Risk with Scarce Default Data: On the Suitability of Cooperative Bootstrapped Strategies for Small Low-Default Portfolios

Credit risk models are commonly based on large internal data sets to produce reliable estimates of the probability of default (PD) that should be validated with time. However, in the real world, a substantial portion of the exposures is included in low-default portfolios (LDPs) in which the number of defaulted loans is usually much lower than the number of non-default observations. Modelling of these imbalanced data sets is particularly problematic with small portfolios in which the absence of information increases the specification error. Sovereigns, banks, or specialised retail exposures are recent examples of post-crisis portfolios with insufficient data for PD estimates, which require specific tools for risk quantification and validation. This paper explores the suitability of cooperative strategies for managing such scarce LDPs. In addition to the use of statistical and machine-learning classifiers, this paper explores the suitability of cooperative models and bootstrapping strategies for default prediction and multi-grade PD setting using two real-world credit consumer data sets. The performance is assessed in terms of out-of-sample and out-of-time discriminatory power, PD calibration, and stability. The results indicate that combinational approaches based on correlation-adjusted strategies are promising techniques for managing sparse LDPs and providing accurate and well-calibrated credit risk estimates.

Supply Chain and Risk Management: An empirical approach in food chain businesses

Departing on our limited understanding of how risks give rise to management controls and performance, this paper aims to better understand the role of Performance Measurement Systems (PMS) and Risk Management Systems (RMS) in food chain businesses. To do so, both case-based research and survey methods are used to develop a comprehensive inventory of the main risks that food supply food managers face, and to provide insight regarding the management control mechanisms they use for enhancing relational performance. Results shows a trend toward a higher management control in food supply relationship, with positive effects on partners’ organisational fit and performance. However some risk sources are still under-managed, as those related to technical uncertainties (transportation problems, long-distance, uncertain technology) and second-tier problems, which represent upcoming challenges in food supply networks. DOI: http://dx.doi.org/10.4995/ICBM.2015.1264

An Application of Kohonen’s SOFM to the Management of Benchmarking Policies

The DEA model provides scores regarding firms’ efficiency, but it does not obtain an overall map about each unit’s position, in order to identify competitive clusters and improve the design of complete benchmarking policies. This lack makes the interpretation of DEA results difficult, together with its real applications for the management of firms.