AI for Forensic Accountants

Forensic accountants investigate fraud, disputes and financial crime. AI mostly changes how much data they can look at. Three techniques do much of the work. Benford's law describes how leading digits are spread in many naturally occurring datasets. A first digit of 1 appears about 30 percent of the time, while 9 appears less than 5 percent of the time. Mark Nigrini popularized its use in auditing and forensic work. His tests look at first digits, second digits and first-two digits, and measure conformity with statistics such as mean absolute deviation. Software now runs these tests across every vendor, employee or cost center in seconds. The key limit is that Benford applies only to data that spans several orders of magnitude and is not assigned or capped. Invoice numbers, prices set at $9.99 and amounts limited by policy will not conform, and that says nothing about fraud. Link analysis treats people, companies, addresses, phone numbers and bank accounts as nodes in a graph. Shared attributes can reveal undisclosed related parties, such as a supplier registered at a director's home address. Tools such as i2 Analyst's Notebook and graph databases make these networks visible, and machine learning can score which links are unusual. Fund tracing follows money through accounts, often through mixed (commingled) balances. There, legal tracing rules such as the lowest intermediate balance rule decide what can be claimed. Automation helps by parsing bank statements and rebuilding the flow of money, but the tracing method is a legal choice, not a software setting. A common misconception is that an AI flag is a finding. A Benford spike or a model score is only a lead. In court, the expert must explain the method, its known error rate and why the conclusion follows from the evidence. In US federal courts, Federal Rule of Evidence 702 and the Daubert standard require testimony to rest on reliable methods that were properly applied. That makes opaque models risky to rely on alone.