How can data analytics help detect fraud? Name at least two methods.

How can data analytics help detect fraud? Name at least two methods.

• A. Data encryption and password cracking
• B. Anomaly detection, Benford's law, duplicate payments, and trend analysis
• C. Image processing and OCR only
• D. A/B testing of ads

Answer: (B)

Data analytics helps detect fraud by systematically examining transactions for unusual patterns, inconsistencies, and telltale indicators, then flagging them for review. One key approach is anomaly detection, which looks for transactions that don’t fit a user’s normal behavior or the organization’s typical patterns. For example, a payment that’s much larger than usual, or a vendor paid after an unusual spike in activity, can trigger a risk score for further investigation.

Benford’s law is another useful tool. It analyzes the distribution of leading digits in financial data; many real-world datasets follow a predictable pattern, and fraud often distorts that pattern. When the leading-digit frequencies deviate from the expected distribution, it can indicate manipulated figures or fabricated transactions that deserve closer scrutiny.

Checking for duplicate payments is a practical, tangible method too. By identifying repeated invoice numbers, identical payment amounts, or the same vendor appearing more than once within a short window, suspicious billing activities or attempts at double-paying can be uncovered.

Trend analysis adds the temporal dimension. By monitoring how values move over time, analysts can spot unusual spikes, persistent increases, or off-season activity that doesn’t align with historical behavior or business cycles, signaling potential fraud.

Other options listed aren’t as aligned with fraud detection. Data encryption and password cracking concern security controls rather than detecting fraudulent activity. Image processing and OCR are about extracting data from documents, not specifically identifying fraud patterns. A/B testing of ads is marketing experimentation and not a fraud-detection technique.