Fraud Investigations & the Future

  • Reduced exposure to fraudulent activities
  • Reduced costs associated with fraud
  • Exposing vulnerable employees at risk of fraud
  • Improves the results of the organization
  • Trust and confidence of the shareholders and customers

Benefits of Fraud Analytics

Identify Hidden Patterns: Fraud analytics identify new patterns, trends, and scenarios under which frauds take place.


Sampling is more effective where there is a lot of data population involved. However, it may not be able to fully detect fraud as it takes only a few populations into consideration. Fraudulent transactions do not occur randomly, therefore, an organization needs to test all the transactions to effectively detect fraud.


This is simply finding out fraud by means of a hypothesis. It allows you to explore. You can test the transactions and find out if there is an opportunity for fraud to take place. You can have a hypothesis to test and find out if there is any fraudulent activity occurring and then you investigate.

Repetitive or Continuous Analysis

This means creating and setting up scripts to run against big data to identify frauds as they occur over a period of time. Scripts are run every day to go through all the transactions and get periodic notification regarding the frauds.

Analytics Techniques

Analytic techniques help you to find out frauds that are not normal, for example:

  • Calculate statistical parameters to find out values that exceed averages of standard deviation.
  • Look at high and low values and find out the anomalies there. Such anomalies are often indicators of fraud
  • Classify the data — group your data and transactions based on specific factors like geographical area.

Benford’s Law

Using Benford’s law you can test certain points and numbers and identify those which appear frequently than they are supposed to and therefore they are the suspect.

Implementing Data Analytics for Fraud Detection

A dependable framework is needed to make the fraud detection process more successful. Some steps on how to implement analytics for fraud detection are:

Perform SWOT

Before embracing fraud analytics an organization should do a SWOT analysis to match with its strengths and weaknesses to enable fraud detection program work to the fullest.

Build a dedicated fraud management team

It is important to have a dedicated team that works to find and prevent fraud in the organization. The team should have a proper flow and a proper reporting fraud detection system.

Build or buy option

Once SWOT analysis is over and team allocation is done it is important for the companies to decide how they want to implement analytics and what resources are required. Should they build or purchase an analytical fraud detection solution from a vendor? A few important factors to be considered while purchasing a fraud analytics solution like cost, user interface, scalability, ease of integration, etc.

Clean data

Integrate all the databases in the organization and remove all unwanted things from the databases.

Setting the threshold

Whether the solution is in-built or purchased from outside the company, it should provide boundary values for different anomalies. Thresholds are set using anomaly detection. If boundaries are set too high then there are chances of frauds to slip through in between. If the boundaries are set too low then a lot of time and resources are wasted.



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