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- Fraud Detection (3)
- Bankruptcy prediction (2)
- Survival analysis (2)
- Applied Statistics (1)
- Business Failure (1)
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- Conceptual model (1)
- Data Mining (1)
- Data mining (1)
- Decision tree (1)
- Decision trees (1)
- Discriminant analysis (1)
- Financial distress prediction (1)
- Financial statement fraud (1)
- Fraud (1)
- Fraud detection (1)
- Predictive Analytics (1)
- Techniques for Business Failure Prediction (1)
- Variable selection methodology (1)
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Articles 1 - 6 of 6
Full-Text Articles in Physical Sciences and Mathematics
Predicting Financial Distress: A Comparison Of Survival Analysis And Decision Tree Techniques, Adrian Gepp, Kuldeep Kumar
Predicting Financial Distress: A Comparison Of Survival Analysis And Decision Tree Techniques, Adrian Gepp, Kuldeep Kumar
Adrian Gepp
Financial distress and then the consequent failure of a business is usually an extremely costly and disruptive event. Statistical financial distress prediction models attempt to predict whether a business will experience financial distress in the future. Discriminant analysis and logistic regression have been the most popular approaches, but there is also a large number of alternative cutting - edge data mining techniques that can be used. In this paper, a semi-parametric Cox survival analysis model and non-parametric CART decision trees have been applied to financial distress prediction and compared with each other as well as the most popular approaches. This …
The Fraud Detection Triangle: A New Framework For Selecting Variables In Fraud Detection Research, Adrian Gepp, Kuldeep Kumar, Sukanto Bhattacharya
The Fraud Detection Triangle: A New Framework For Selecting Variables In Fraud Detection Research, Adrian Gepp, Kuldeep Kumar, Sukanto Bhattacharya
Adrian Gepp
Financial Statement Fraud Detection Using Supervised Learning Methods (Ph.D. Dissertation), Adrian Gepp
Financial Statement Fraud Detection Using Supervised Learning Methods (Ph.D. Dissertation), Adrian Gepp
Adrian Gepp
No abstract provided.
Predicting Financial Distress: A Comparison Of Survival Analysis And Decision Tree Techniques, Adrian Gepp, Kuldeep Kumar
Predicting Financial Distress: A Comparison Of Survival Analysis And Decision Tree Techniques, Adrian Gepp, Kuldeep Kumar
Adrian Gepp
Financial distress and then the consequent failure of a business is usually an extremely costly and disruptive event. Statistical financial distress prediction models attempt to predict whether a business will experience financial distress in the future. Discriminant analysis and logistic regression have been the most popular approaches, but there is also a large number of alternative cutting – edge data mining techniques that can be used. In this paper, a semi-parametric Cox survival analysis model and non-parametric CART decision trees have been applied to financial distress prediction and compared with each other as well as the most popular approaches. This …
Business Failure Prediction Using Statistical Techniques: A Review, Adrian Gepp, Kuldeep Kumar
Business Failure Prediction Using Statistical Techniques: A Review, Adrian Gepp, Kuldeep Kumar
Adrian Gepp
Accurate business failure prediction models would be extremely valuable to many industry sectors, particularly in financial investment and lending. The potential value of such models has been recently emphasised by the extremely cosdy failure of high profile businesses in both Australia and overseas, such as HIH (Australia) and Enron (USA). Consequently, there has been a significant increase in interest in business failure prediction from both industry and academia. Statistical business failure prediction models attempt to predict the failure or success of a business. Discriminant and logit analyses are the most popular approaches, and there are also a large number of …
A Comparative Analysis Of Decision Trees Vis-À-Vis Other Computational Data Mining Techniques In Automotive Insurance Fraud Detection, Adrian Gepp, Kuldeep Kumar, J Holton Wilson, Sukanto Bhattacharya
A Comparative Analysis Of Decision Trees Vis-À-Vis Other Computational Data Mining Techniques In Automotive Insurance Fraud Detection, Adrian Gepp, Kuldeep Kumar, J Holton Wilson, Sukanto Bhattacharya
Adrian Gepp
No abstract provided.