Correlation of Complex Evidence in Forensic Accounting Using Data Mining

The classical statistical correlation is an efficient technique for linking simple numerical data sets via a single correlation coefficient. The modem schemes for money laundering, financial fraud are becoming very sophisticated and are changed all the time. To be able to discover such schemes we need to deal simultaneously with a diverse set of numeric and non-numeric data types that include different numeric data types, ordered sets, graph structures, texts, schemes, plans, and other information. Often any individual evidence does not reveal a suspicious pattern and does not guide investigation in forensic accounting. In contrast correlation of two or more evidences with each other and background knowledge can reveal a suspicious pattern. A new area of Link Discovery (LD) emerged recently is a promising new area for such tasks. This paper outlines design of such a new technique called Hybrid Evidence Correlation (HEC). It combines first-order logic (FOL), probabilistic semantic inference (PSI) and negative rules for designing HEC to deal with rare suspicious patterns. The approach is illustrated with an example of discovery of suspicious patterns. Computational efficiency of the algorithm is justified by a computational experiment. Conceptual advantages of the algorithm such as completeness have been reported in previous mathematical analysis of the base concepts of the algorithm. The approach was successfully tested for detecting transactions fraud on synthetic data. Data contained several attributes of a transaction such as seller, buyer, types of buyer and seller, sold item, amount. price and date.