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Building Credit Scoring Model for Non-Policy House Purchasing Mortgages -A Case Study of M Bank
Credit Scoring,Logistic Regression,Binning,validity,PSI,
|Publication Year :||2010|
Under the effect of globalization, the Financial Tsunami started from USA and rapidly spread to other parts of the world. This crisis first affected the highly industrial developed countries before extending its influence to the newly emerging markets and developing countries. The crisis of mortgage market resulted in catastrophes of financial institutions and the financial market and caused a full scale meltdown in economy. Like a vicious circle, the slowdown of economy backfired. It not only influenced the financial markets but also offset the relief measures from the government. Therefore, good risk control has become a main requirement in BASEL II regulation. On the other hand, when using tools of credit risk management, the most important thing is credit scoring. Credit scoring is to use subjective as well as objective means to identify the solvency ability of the counterparty in the transaction. At the same time, we use this information to evaluate and to give a suitable score according to overall credit ability of the object of study. A scoring model will be analyzed by variables of mortgages customers, aggregating this information to a credit default probability. On a conservative basis, we use logistic regression methodology to evaluate the non -policy general mortgages with the case of M bank as samples, and exercise 37,231 good samples as well as 348 defaulted samples. The samples were took from the year of 1997 to 2003. Also, we use random sampling way about 30% to validate model. First of all, we filter several variables of higher prediction as candidate variables through AUC values and one variable regression rules. Then we filter the final 5 variables by group methodology. From The AUC value of discriminatory development model is 84%, and validation model was used by the final coefficient and variables from development model. Also, AUC is 80% to separate the good or bad customers. We find KS value to be 0.62 and PSI value to be under 0.001 by KS and PSI test, and prove that there is very credible and stable with our model. Finally, we use predicted PD to separate ratings to 10 rankings of risk. Facing the business cycle from recession to boom, we suggest banks build warning index in the early period, and focus on credit scoring card to evaluate assets and portfolio and to execute the overall risk management.
|Appears in Collections:||農業經濟學系|
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