Forensic Data Analytics in Fraud Cases

What Forensic Data Analytics Does in a Fraud Case

Forensic data analytics tests every transaction in the supplied population against defined conditions. A duplicate-payment rule can examine all payments, including low-value transactions that a sample might miss. It also makes the selection repeatable when further records arrive.

The investigator decides which conditions fit the allegation. For a payroll matter, compare employee records with payment recipients and vendor accounts. For suspected misappropriation, start with unusual transfers and cash activity. A flag identifies a lead. Invoices, approvals and explanations establish what happened.

The Association of Certified Fraud Examiners’ 2026 Report to the Nations records tips as the initial detection method in 43% of studied cases. Internal audit accounts for 15% and management review for 13%. Automated transaction or data monitoring accounts for 3%. These are initial detection categories, so the figures do not measure every use of analytics later in an investigation.

Common Tests

Common tests isolate repeated payments, unusual amounts and transactions outside expected processes. Select the population and parameters before interpreting the results.

A statement usually supplies dates and amounts, so round-dollar cash withdrawals can be tested directly. Invoice duplication needs invoice data. After-hours testing needs a timestamp, which many statements do not provide. Employee-vendor matching needs both master files and full account identifiers.

Benford’s law compares observed leading digits with an expected distribution. Fixed salary bands, assigned numbers and constrained amounts can be unsuitable. A small collection also gives unstable percentages. Use the method to select further work, without treating a digit mismatch as a fraud finding.

A Fictional Test Run

A Python run applied the listed tests to eight fictional transactions, supplemented by invoice and employee-vendor records. All amounts are Australian dollars. The posting hours use one assumed local time zone, and the sequence runs from 101 to 109.

The input contains two $1,250 payments for vendor V1’s invoice I7. Other rows include a $5,000 cash withdrawal and cash deposits of $9,800, $9,900 and $9,850. A $2,100 transfer goes to a fictional related account. The final payment is $1,600.

The fictional near-threshold band selects deposits for investigation. The statutory reporting threshold has a different purpose. AUSTRAC’s threshold guidance applies to individual physical-currency transactions of $10,000 or more, including foreign-currency equivalents. The reporting duty belongs to a reporting entity providing the relevant designated service.

Section 142 of the Anti-Money Laundering and Counter-Terrorism Financing Act 2006 addresses conducting transactions to avoid threshold reporting. The offence involves the sole or dominant purpose of avoiding that reporting. Repeated deposits below $10,000 alone do not establish that purpose. Electronic transfers also must not be classified as physical cash merely because their amounts fall near the threshold.

Preparing Clean Data From Bank Statements

Reliable bank-statement tests start with a complete, reconciled transaction ledger. Keep each row connected to its source statement while converting PDFs into data you can filter.

1. Record the supplied account identifiers and statement periods. Preserve the original PDFs and log missing periods.
2. Extract transaction dates, descriptions and debit or credit amounts. Keep currencies separate and retain source filenames and page references.
3. Reconcile each statement’s opening balance, credits and debits to its closing balance. Resolve extraction differences and duplicated pages before testing.
4. Categorise transactions and normalise account identifiers for joins. Preserve the original description alongside any revised label.
5. Add the supporting records each test requires. Invoice references, posting timestamps and employee bank details cannot be inferred from a short statement description.

Overlapping statements can repeat genuine transactions. De-duplicate the extracted population using account and source context before running payment-duplication rules. Otherwise, an extraction overlap can look like a second payment.

FinTrace saves forensic accountants hours of monotonous statement conversion and review. The service returns a reconciled, categorised Excel ledger that feeds transaction tests directly, alongside a clear report flagging suspicious transactions. Every finding cites its source PDF filename and page, putting the supporting evidence at hand.

Advanced AI workflows combine structured extraction with source-grounding checks and independent reviews across large PDF bundles. FinTrace performs the statement-analysis stage at a fraction of the cost of manual review, freeing your time for interpretation and specialist work.

In one recent matter, work estimated at roughly 50 hours of manual lawyer review was delivered in about 10 hours. That example shows the time returned when statement preparation and analysis are handled together.

Tools Used

The tool fits the investigation when it can run your rules on the required data and preserve an explainable record of the work. Different tools suit different parts of that job.

Caseware describes IDEA’s audit analytics as whole-population analysis. Diligent’s ACL Analytics supports audit data analysis and repeatable workflows. The Python CSV module reads structured transaction files, while custom code defines the actual tests.

Microsoft’s Power BI documentation describes data connection, visualisation and report sharing. A chart of rising payments can direct attention to an account. Preserve the underlying selection rules and rows so another analyst can reproduce that chart’s result.

For a broader choice of investigation systems, compare fraud investigation software. For an employee-payment allegation, connect the analytics to the supporting records needed to investigate payroll fraud.

Making Results Hold Up

A result is explainable when another analyst can trace it from the original records through the test to the selected transactions. Retain the source data and the exact rule version used for each output.

For every test, record the population size, account coverage and excluded records. Save the query, script or formula, plus its parameters and software version. Keep the output rows with their source references and record how each exception was resolved. A rerun on the same inputs must return the same selection.

In the fictional example, the duplicate rule selects two rows using vendor V1, invoice I7 and amount $1,250. Record those fields and both source references. Then attach the invoice and approval evidence explaining whether both payments were authorised. Keep the selection separate from the later conclusion about the payments.

Where the Commonwealth Evidence Act applies, section 79 of the Evidence Act 1995 addresses opinions based on specialised knowledge. That knowledge must come from training, study or experience. The opinion must be wholly or substantially based on it.

An arithmetic selection and an opinion about what a pattern means raise different evidentiary questions. If you rely on a specialist interpretation, the expert must explain their knowledge and the reasoning connecting the data to that opinion. Court-specific evidence and expert-report requirements also apply. Build that explanation as you investigate, with the selected transactions and supporting documents beside each conclusion.