Part of the Credit Research Centre Seminar Series


Advances in Credit Scoring: Combining performance and interpretation in kernel discriminant analysis

📅10 November 2017
14:00 - 15:30 (1 hour 30 minutes)


👤Dr Caterina Liberati, University of Milano-Bicocca



Due to the recent financial turmoil, a discussion in the banking sector about how to accomplish long term success, and how to follow an exhaustive and powerful strategy in credit scoring is being raised up. Recently, the significant theoretical advances in machine learning algorithms have pushed the application of kernel-based classifiers, producing very effective results. Unfortunately, such tools have an inability to provide an explanation, or comprehensible justification, for the solutions they supply. In this work, it is proposed a new strategy to model credit scoring data, which exploits, indirectly, the classification power of the kernel machines into an operative field. A reconstruction process of the kernel classifier is performed via linear regression, if all predictors are numerical, or via a general linear model, if some or all predictors are categorical. The loss of performance, due to such approximation, is balanced by better interpretability for the end user, which is able to order, understand and to rank the influence of each category of the variables set in the prediction. An Italian bank case study has been illustrated and discussed.


Conference Room (4th floor)
University of Edinburgh Business School
29 Buccleuch Place, Edinburgh, Lothian EH8 9JS, United Kingdom


👤 Yvonne Crichton
📞 +44 (0)131 650 8342

Associated files