Fraud data analysis – read the information below and note the analytic chart created in Excel. You are the fraud investigator hired to work on this case.

Please read the information below and note the analytic chart created in Excel. You are the fraud investigator hired to work on this case.

You are presenting your findings to your client.

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What steps would you use to set up such an investigation? What would you need to take into account when planning the investigation?

With its ability to reveal hidden patterns and relationships, fraud analytics can make the difference between identifying all fraudulent transactions, not just the obvious ones in an investigation. We’ll discuss here ways to detect fraud.

The goal of fraud analytics isn’t only to detect and identify fraudulent behavior but to determine the origin of anomalies and determine why and how they exist. For example, an insurance company doesn’t have time to review its thousands of claims, so it might analyze some at random. But it could be more successful in finding fraud if it identified and reviewed the claims that were most at risk based on historical trends and patterns.

During a recent payroll fraud case investigation, we found a specific scheme an employee had used to embezzle money from the payroll function. However, we wanted to scrutinize all instances of fraud the employee may have been involved in during her four-year tenure with her employer, so we didn’t limit ourselves to examining transactions that just matched the fraudster’s schemes. We developed a statistical model that identified all other unusual patterns we deemed to be “outliers.” We established the state of what should have been expected and reviewed any occurrences that didn’t match this normal state. And guess what? If we had only looked for the one pattern that the employee had been using, we would have missed half of the fraudulent transactions.

A four-step approach takes us from data to insight:

Data identification: Pick the wrong data, and you won’t find what you’re looking for; pick too much, and you’ll be sifting through it for no reason.

Forensic data collection: When conducting fraud analytics in an investigation, it’s essential you follow well-defined forensic preservation standards, which include maintaining the data’s chain of custody and performing data integrity validation to ensure that you’ve captured all transactions and assured absence of tampering.

Data normalization and structuring: You’ll have to normalize and structure all collected data so it can be linked, as it may originate internally or from third parties. Some will be structured, such as that originating from databases, while others will be unstructured, such as text-heavy data. Only then will we be able to derive all possible insight from the data we have collected.

Data analysis: We must now determine how to best analyze the collected data. Strategies include simple queries, relationship mapping, link analysis and visual analysis, as well as more advanced models to identify previously unknown patterns.




If patients are traveling long distances, possible pill-mill scheme.


If high velocity of drugs of concern, possible kickback scheme.

Controlled drugs

If high velocity of controlled drugs, possible pill-mill scheme.

Prescribing out of prescribers’ specialty

If high velocity of drugs of concern, possible kick-back and/or pill-mill scheme.

Practice setting

Private practice and clinics (particularly pain or abuse clinics) have experienced higher levels of alleged fraud.

Prescriber type

Evaluate in concert with practice setting. Example: If prescribing volume at a pain clinic is driven by non-physicians (nurse practitioner or physician assistant), concerns arise regarding prescribing controls.

Medical claims

If low velocity of medical claims supporting prescribing volume, possible kickback and/or pill-mill scheme.

Fraud analysts have found it useful to utilize velocity and comparative-based analytics to detect aberrant billing patterns and/or activity.

The chart above shows some prescriber-based analytic tips to identify possible fraudulent activity.

Legault, J. (2012) Fraud Analytics Taking Your Data to the Next Level

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