Journal of Intelligence and Information Systems (지능정보연구)

Korea Intelligent Information System Society (한국지능정보시스템학회)
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Illegal Cash Accommodation Detection Modeling Using Ensemble Size Reduction

신용카드 불법현금융통 적발을 위한 축소된 앙상블 모형

  • Received : 2010.02.10
  • Accepted : 2010.03.07
  • Published : 2010.03.30
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Abstract

Ensemble approach is applied to the detection modeling of illegal cash accommodation (ICA) that is the well-known type of fraudulent usages of credit cards in far east nations and has not been addressed in the academic literatures. The performance of fraud detection model (FDM) suffers from the imbalanced data problem, which can be remedied to some extent using an ensemble of many classifiers. It is generally accepted that ensembles of classifiers produce better accuracy than a single classifier provided there is diversity in the ensemble. Furthermore, recent researches reveal that it may be better to ensemble some selected classifiers instead of all of the classifiers at hand. For the effective detection of ICA, we adopt ensemble size reduction technique that prunes the ensemble of all classifiers using accuracy and diversity measures. The diversity in ensemble manifests itself as disagreement or ambiguity among members. Data imbalance intrinsic to FDM affects our approach for ICA detection in two ways. First, we suggest the training procedure with over-sampling methods to obtain diverse training data sets. Second, we use some variants of accuracy and diversity measures that focus on fraud class. We also dynamically calculate the diversity measure-Forward Addition and Backward Elimination. In our experiments, Neural Networks, Decision Trees and Logit Regressions are the base models as the ensemble members and the performance of homogeneous ensembles are compared with that of heterogeneous ensembles. The experimental results show that the reduced size ensemble is as accurate on average over the data-sets tested as the non-pruned version, which provides benefits in terms of its application efficiency and reduced complexity of the ensemble.

불법현금융통 적발모형 개발에 앙상블 접근방법을 사용하였다. 불법현금융통은 국내 신용카드사의 손익에 영향을 미치며 최근 국제화되고 있음에도 불구하고 학문적인 접근이 이루어지지 않았다. 부정행위 적발모형(Fraud Detection Model, FDM)은 데이터 불균형 문제로 인하여 좋은 성능을 얻기 어려운데, 다수의 모형을 결합하는 앙상블이 대안으로 제시되어 왔다. 앙상블에 포함된 모형들의 다양성이 보장된다면 단일모형에 비해 더 좋은 성능을 보인다는 점은 이미 인정되고 있으며, 최근 연구 결과는 학습된 모든 기본모형들을 사용하는 것보다 적절한 기본모형들만 선택하여 앙상블에 포함시키는 것이 바람직하다는 것이다. 본 논문에서는 효과적인 불법현금융통 적발을 위하여 축소된 앙상블 기법을 사용하는데, 정확성과 다양성 척도를 사용하여 앙상블에 참여할 기본모형을 선택하는 것이다. 다양성은 앙상블을 구성하는 기본모형들 사이의 불일치 (Disagreement or Ambiguity)를 의미하는데, FDM에 내재된 데이터 불균형문제를 고려하여 두 가지 측면에 중점을 두었다. 첫째, 학습 자료의 추출 과정에서 다양성을 확보하기 위한 소수 범주의 과잉추출 방법과 적절한 훈련 방법에 대해 설명하였다. 둘째, 소수범주에 초점을 맞추어 기존의 다양성 척도를 효과적인 척도로 변형시키고, 전진추가법과 후진소거법의 동적 다양성 계산법을 도입하여 앙상블에 참여할 기본모형을 평가하였다. 실험에 사용된 학습 알고리즘은 신경망, 의사결정수와 로짓 회귀분석이었으며, 동질적 앙상블과 이질적 앙상블을 구성하여 성능평가를 하였다. 실험결과 불법현금융통 적발모형에 있어 축소된 앙상블은 모든 기본모형이 포함된 앙상블과 성능 차이가 없었다. 축소된 앙상블은 앙상블 구성의 복잡성을 감소시키고 구현을 용이하게 한다는 점에서 FDM에서도 유력한 모형 수립 접근방법이 될 수 있음을 보였다.

Keywords

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