Using Data Analytics to Spot Fraud
Much has been written about the potential of data analytics to increase sales, improve return on investment and bring more dollars to the bottom line. Identifying otherwise unseen patterns among customers, stores, and other units of measure can help companies boost sales among “heavy users” and other profitable market segments.
However, the same analytical tools that enable companies to mine their vast stores of data can also help mitigate fraud.
Addressing fraud has never been more important. Companies are under increasing pressure to effectively and transparently improve their corporate governance practices. Large-scale financial scandals in the last decade, government regulations and increasing scrutiny of corporate behavior by the general public are all factors pushing companies to ramp up their efforts to control fraud.
Looking at All the Data
Traditional internal controls rely on an audit-driven approach that is based upon a statistical sampling methodology. Because this, by definition, means that internal auditors do not look at all data, they tend to focus on the largest transactions, or those with the highest perceived risk–especially in companies with tens or hundreds of thousands of daily transactions.
In contrast to traditional methods, data analytics generally does not rely on transaction sampling. Rather, in most cases, the use of data analytics techniques provides the ability to analyze the entire population of electronic data available for a given scenario, and to look for connections or other unusual characteristics that might indicate fraud, enabling you to then target high-risk transactions for further examination.
Suspicious Transactions
Certain types of transactions have inherently higher levels of risks and are candidates for closer analysis. Some of these include:
• Payments to risky vendors, including reimbursement expenses to business development personnel who deal with government officials nationally or internationally
• Payments made from and to foreign bank accounts
• Use of new attorneys, accountants, consultants or other professionals with no prior relationship to the company
• Suspicious payment transactions, including lavish entertainment expenses, gifts, facilitation expenditures, trips with undocumented or unclear business purpose, etc.
• Journal entries with non-typical or infrequent large offsets to cash account credits–this may indicate someone plugging a less scrutinized ledger account to force cash reconciliations with the objective to conceal an embezzlement scheme
As your clients conduct their year-end audit, they may want to consider a fraud risk audit using data analytics in addition to the normal reviews and protocols. If you or your clients identify suspicious or concerning behavior, consider applying the power of data analytics to examine all transactions, not just a sample, especially if a traditional internal scan fails to identify any suspicious transactions. To learn more about using data analytics to prevent fraud, read our whitepaper, Data Analytics is a powerful fraud prevention and corporate enforcement tool.
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