All Discussions Tagged 'credit scoring' - AnalyticBridge2020-04-07T10:47:49Zhttps://www.analyticbridge.datasciencecentral.com/forum/topic/listForTag?tag=credit+scoring&feed=yes&xn_auth=noHow to prevent scores from caking in scoring models?tag:www.analyticbridge.datasciencecentral.com,2008-03-14:2004291:Topic:63192008-03-14T12:14:42.709ZVincent Granvillehttps://www.analyticbridge.datasciencecentral.com/profile/VincentGranville
The general question is actually about how to produce a nice score distribution, with no large gaps and no huge spikes.<br />
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For instance, if a score S = A1*R1 + A2*R2 + A3*R3 + A4*R4, where R1, R2, R3, R4 are four binary rules (e.g. R4 is "no late payment in last 12 months"), and A1, A2, A3, A4 are weights (penalties) respectively equal to 5, 5, 10 and 20 points, then we have few unique scores because 5+5 =10, 5+5+10 = 20. The weights 4, 5, 10, 20 eliminate this problem, but still produce largeā¦
The general question is actually about how to produce a nice score distribution, with no large gaps and no huge spikes.<br />
<br />
For instance, if a score S = A1*R1 + A2*R2 + A3*R3 + A4*R4, where R1, R2, R3, R4 are four binary rules (e.g. R4 is "no late payment in last 12 months"), and A1, A2, A3, A4 are weights (penalties) respectively equal to 5, 5, 10 and 20 points, then we have few unique scores because 5+5 =10, 5+5+10 = 20. The weights 4, 5, 10, 20 eliminate this problem, but still produce large gaps. Gaps can be reduced by choosing the weights 2, 4, 8, 16, but then this is a too drastic change to the weights, and if rules have highly variable triggering rates ranging from 2 to 60%, we can still end up with an "ugly" score distribution.<br />
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I was wondering if there is some literature on this subject, or how did you address this issue? In particular, in systems with more than 100 rules.