During the last several years machine learning started to revolutionize many industrial fields by replacing human intellectual work with recent technologies. Machine learning has started to be used in financial sphere as well for predicting stock prices, detecting fraud actions etc. In this work, we are focusing on financial market risk classification, which is a part of fraud action detection ...

Fraud is causing huge losses to our financial world. This work describes the use of a committee decision system using genetic programming to evolve fuzzy logic rules capable of detecting suspicious home insurance claims. Details of the evolutionary-fuzzy system, the committee decision maker and the data preprocessing are provided. Finally, a series of experiments are described, showing that the...

In recent years, many articles have promoted uses for “Benford’s Law,” claimed to identify a nearly ubiquitous distribution pattern for the frequencies of first digits of numbers in many data sets. Detecting fraud in financial and scientific data is a suggested application. Like the Normal and Chi-square distributions, Benford’s appears to offer an appealingly clear-cut, mathematically tractabl...

Travel and Entertainment (T&E) expenses are under increasing scrutiny as one of the largest controllable indirect expenses in a firm. This involves internal audits and analysis by business controls personnel to identify fraud and misuse and to take appropriate corrective actions. We have developed a set of statistical models to identify suspicious behavior for further investigation. Our Behavio...

Fraud detection is a fundamental data mining task with a wide range of practical applications. Finding rare and evolving fraudulent claimant behaviour in millions of electronic Medicare records poses unique challenges due to the unsupervised nature of the problem. In this paper, we investigate the problem of efficiently and effectively identifying potential non-compliant Medicare claimants in A...

A new approach to detect suspicious, unknown event patterns in the field of fraud detection by using a combination of discriminant analysis and neural network techniques is presented. The approach is embedded in a Complex Event Processing (CEP) engine. CEP is an emerging technology for detecting known patterns of events and aggregating them as complex events at a higher level of analysis in rea...

In this paper, a fuzzy rule-based classifier (FRBC) system was developed and used in two Evolutionary Algorithm Models to detect patterns of financial statement fraud and assess the effectiveness of a subset of SAS No. 99 red flag variables. Each FRBC was evolved by using Generic Algorithm(GA) and MARLEDA – a modern estimation of distribution algorithm (EDA) – and trained with a data collection...