Blog Post

Feasible Fraud Detection Methods

David Paschane • Aug 17, 2017

Options for shining a light on nefarious actors

If you needs fraud detection, or fraud prevention, there are many options to use, and the recent emphasis is Artificial Intelligence (AI).

Here, I summarize the AI perspective, and then propose methods that may be more feasible.

The AI promoters imply that a computer does all the thinking for us, thus it will not make errors, and is more thorough. AI tools operate on a simple concept for handling data: An algorithm, with many coded parameters, is used to make decisions about data that have many-to-many relationships; meanwhile, the program is scanning data sets to inform the algorithm so it can complete decisions with more insights.

AI creates the appearance of “smartness,” as it is faster and more comprehensive than people. However, the fraud detection algorithms and their parameters are functional designs based on the work of human analysts. In fact, analysts may do it much better.

Fraud detection and prevention is a human science problem, in that an analyst is interpreting statistical analyses of data patterns, given knowledge about how such data could be manipulated by others. It is enriched by applying robust analytic disciplines, such as Performance Architectural Science Systems (PASS), which is a focus on the complex, layered factors that allow changes to happen, including fraud.

The first objectives are to rationalize data, measure and test pattern variability, and verify the possible explanations, within the scope of possible human factors. Then, create parameters for algorithms, where these are used to repeat the baseline work of the analyst.

Once the algorithms prove their worth, by testing and optimizing the means of anticipating many possible fraud signals or risks, then they can be engineered into other tools, such as a workflow platforms or financial transactions. The algorithms can be made more robust through recursive testing, but their foundation is developed in applied analyses.

While analytic work is advanced, it is not necessarily what is implied in AI.

The basic steps are:

  1. Rationalize data
  2. Test for variability patterns
  3. Verify explanations
  4. Develop data parameters
  5. Build algorithms
  6. Test algorithms on various data sources
  7. Improve algorithms
  8. Integrate algorithms into data handling
  9. Automate algorithm operations
Feasible methods are ones that combine the rigor of statistical measurement with the robustness of human interpretation. The best fraud detection and prevention is developed over time by anticipating risks due to human actions.

The eight steps above outline a feasible process for combining and developing the most effective actions for addressing fraud.

Let's shine a bright light on those committing fraud.


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