Survey of Insurance Fraud Detection Using Data Mining Techniques
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Title: Survey of Insurance Fraud Detection Using Data Mining Techniques

Section 1: INTRODUCTION
Description 1: Discusses the nature of insurance, common reasons for insurance uptake, and the prevalent fraud issues within the insurance sector.

Section 2: Insurance generally classified into Four types
Description 2: Describes the types of insurance, with a particular focus on motor and medical insurance fraud problems.

Section 3: Various stages of insurance fraud
Description 3: Explains the stages at which fraud can occur, including pre-insurance (application fraud) and post-insurance (claims fraud).

Section 4: Types of insurance claims frauds - Motor insurance frauds
Description 4: Details the nature of fraud within motor insurance, distinguishing between hard and soft frauds, and the challenges of data availability for research.

Section 5: Health Insurance
Description 5: Discusses health insurance frauds, including characteristics of fraudulent claims, the national perspective, and strategies for detection using data mining techniques.

Section 6: DATA MINING
Description 6: Introduces data mining as a technology for fraud detection, summarizing the process and its relevance in detecting financial fraud.

Section 7: Process Model of Data mining in financial fraud detection
Description 7: Outlines the process models and methods used in data mining for financial fraud detection.

Section 8: Classification
Description 8: Describes the classification techniques in data mining and their applications for fraud detection.

Section 9: Clustering
Description 9: Explains the clustering techniques used to group similar objects for fraud detection and investigation.

Section 10: Prediction
Description 10: Discusses prediction techniques and their usage in estimating future values for detection of fraudulent activities.

Section 11: Outlier detection
Description 11: Covers the methods for detecting outliers, which can indicate potential fraud in insurance claims.

Section 12: Regression
Description 12: Describes the use of regression techniques in uncovering relationships between variables to detect insurance fraud.

Section 13: Visualization
Description 13: Highlights the importance of visualization in presenting data mining results for clearer understanding and detection of fraud patterns.

Section 14: CONCLUSION
Description 14: Summarizes the findings from the survey, reiterates the importance of data mining in combating insurance fraud, and suggests future directions for research.