SBIR Phase I:Advanced Irregularity Prediction System (AIPS) to identify Accounting Errors and Financial Fraud in Small & Medium Businesses

Period of Performance: 01/01/2010 - 12/31/2010


Phase 1 SBIR

Recipient Firm

3495 Piedmont Road
Eleven Piedmont CenterAtlanta, GA 30305
Principal Investigator


This Small Business Innovation Research (SBIR) Phase I project aims to assess the feasibility of using statistical methods such as Hierarchical Clustering and Binary Classification to predict accounting errors and financial fraud amongst small and medium businesses (SMBs). Checks and balances in place to protect larger businesses from accounting errors and financial fraud are too complex, time consuming, and costly for SMBs. As a result, small and medium businesses suffer greater losses from accounting errors and financial fraud than any other sized businesses. Advanced data mining and analysis techniques may offer a simple and cost effective solution to the problem. The proposed research includes data collection and statistical assessment to predict accounting errors and fraud in financial systems. While the techniques proposed have been successfully used in fields such as information security to determine threats and prevent risks (e.g. intrusion prevention, anti-virus, anti-spyware, web content filtering), they have not been applied nor tested to transactional accounting data sets.