Detecting insurance fraud using supervised and unsupervised machine learning

A hybrid model based on machine learning and genetic algorithm for detecting fraud in financial statements

Financial statement fraud has increasingly become a serious problem for business, government, and investors. In fact, this threatens the reliability of capital markets, corporate heads, and even the audit profession. Auditors in particular face their apparent inability to detect large-scale fraud, and there are various ways to identify this problem. In order to identify this problem, the majori...

ahp algorithm and un-supervised clustering in auto insurance fraud detection

this thesis is a study on insurance fraud in iran automobile insurance industry and explores the usage of expert linkage between un-supervised clustering and analytical hierarchy process(ahp), and renders the findings from applying these algorithms for automobile insurance claim fraud detection. the expert linkage determination objective function plan provides us with a way to determine whi...

Detecting Significant Events in Lecture Video using Supervised Machine Learning

This paper describes work we are doing to identify significant events in video captures of academic lectures. Unlike other approaches which tend to define per-image comparison threshold values based on intuition or empirically derived results, we use supervised machine learning techniques to automatically determine appropriate image characteristics based on end-users understanding of what const...

Novel strategies on semi-supervised and unsupervised machine learning

Fraud Analytics: a Survey on Bank Fraud and Fraud Prediction Using Unsupervised Learning Based Approach

Fraud in banks has been steadily growing over the past years and is a challenge to banks worldwide. The complexity involved in detection of such fraudulent activities further adds to the problem. A thorough examination of fraud and its possibilities is necessary to pinpoint and distinguish the few fraudulent cases within the vast volumes of banking data. In this paper we have discussed various ...