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• W67500077 abstract "Continuous Risk Management (CRM) is a practice in which there is a continuous assessment of the risks that exist or could arise. In addition, in CRM it is determined which risks have priority to be dealt with and strategies are considered that could be implemented to deal with the risks. This thesis demonstrates how CRM of (mortgage) customers can be implemented, based on a case study conducted at the ING Arrears Department. As a proof of concept, the thesis shows how CRM can be combined with Data Mining to create a powerful approach that can enable a reduction in risk costs to be achieved. The central research question that is answered in this thesis is: How to reduce the risk costs for ING through the implementation of Continuous Risk Management of customers at the ING Arrears Department, to identify customer risk categories to be handled with a differentiated approach? In this thesis a first iteration of the CRM cycle at the ING Arrears Department is described, in which a differentiated approach is devised for different customer risk categories. The thesis shows how a risk score as calculated by a logistic regression classifier can be used to define a number of distinct customer risk categories, each of which corresponds to a different amount of average resulting risk costs. Based on this, a stacked set of affordable mitigation actions, which each correspond to a positive business case for ING (on average), is defined for the customers in each customer risk category. A further differentiation in approach is obtained by specifying applicable mitigation actions depending on the type(s) of payment problem(s) of a certain customer. Together, the affordable and applicable mitigation actions result in a risk mitigation strategy that is proposed for ING based on the first iteration of the CRM cycle. As part of this first iteration, part of the proposed risk mitigation strategy is piloted on one specific group of customers to give an indication of the potential results and to be able to show how this information can be used to learn and improve the approach in consecutive Continuous Risk Management cycles. The same approach presented in this thesis can potentially be applied in other application areas of (Continuous) Risk Management. It is expected to be particularly useful in areas with a large number of potential risks or risk events (requiring classification) and sufficient data available on these risk events (to enable Data Mining). Beside the financial sector, these areas include (i.a.) logistics and supply chains, the automotive industry, the aviation industry, consumer products manufacturing and telecommunications." @default.
• W67500077 created "2016-06-24" @default.
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• W67500077 date "2014-08-07" @default.
• W67500077 modified "2023-09-26" @default.
• W67500077 title "Continuous Risk Management of Customers, Application at the ING Arrears Department" @default.
• W67500077 hasPublicationYear "2014" @default.
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