대출심사의 예측 정확도 향상을 위한 방법 제안
- 주제(키워드) 데이터마이닝 , LR , NN , SVM
- 발행기관 고려대학교 정보경영공학전문대학원
- 지도교수 장동식
- 발행년도 2010
- 학위수여년월 2010. 8
- 학위구분 석사
- 학과 정보경영공학전문대학원 정보경영공학과
- 원문페이지 45 p
- 실제URI http://www.dcollection.net/handler/korea/000000023922
- 본문언어 한국어
- 제출원본 000045608251
초록/요약
Industry structure and environment of the domestic bank have been changed by an influx of large foreign-banks and advanced financial products when the currency crisis erupted in Korea. In a competitive environment, accurate forecasts of changes and tendencies are essential for the survival and development. Forecast of whether to approve loan applications for customer or not is an important matter because that is related to profit generation and risk management on the bank. Therefore, this paper proposes the method to improve forecast accuracy of loan underwriting. Processes in experiments are as follows. Select the predictor variables which affect significantly to the result of loan underwriting by correlation analysis and feature selection technique, and then cluster the customers by the 2-Step clustering technique based on selected variables. Find the most accurate forecasting model for each clustering by applying LR, NN and SVM. Then compare the forecasting accuracy with existing application way of LR, NN and SVM.
more목차
목 차
영문요약 ········································································· ⅳ
목 차 ········································································· vi
그림 목차······································································ viii
표 목차 ··········································································· ix
제 1 장 서론 ··································································· 1
1.1 연구의 배경 및 목적 ············································· 1
1.2 연구의 구성 ··························································3
제 2 장 관련연구 ·····························································4
2.1 상관분석 및 Feature Selection 기법······················ 5
2.2 군집화 ·································································· 6
2.3 LR (Logistic Regression) ····································· 7
2.4 NN (Neural Network) ·········································· 9
2.5 SVM(Support Vector Machine) ·························· 11
제 3 장 대출심사 모형 설계 ··········································· 13
제 4 장 실험분석 ··························································· 17
4.1 기존 심사 모형의 실험 결과···································19
4.2 제안한 심사 모형의 실험 결과································20
4.2.1 예측변수 선정 ·········································· 20
4.2.2 군집화 (Clustering) ·································· 24
4.2.3 대출 심사 예측 기법별 실험 ······················ 26
4.3 기존 방법과 제안 방법의 예측 성능 비교 ···············28
제 5 장 결론 ································································ 31
참고 문헌 ·······································································33
