Fraud by its nature is hidden. Organizations are facing fraud from external and internal fraudsters and schemes. By using technology and analytics we can reduce the fraud risk substantially.
There are significant gains for companies and organizations that engage in data analytics. The future of leveraging data analytics is all about data; data management, inclusion, and integration, and at the core of this data is the technology that surrounds it. We are becoming data-centric and add transparency.
For external fraud (e.g. cybercrime and even larceny or skimming of assets) data is difficult to collect. So let's focus on the prevention of internal fraud, where we can work with more structured data.
For simplicity, we can analyze assets along with the 3 P's: products, people and physical infrastructure including processes.
Products: Sophisticated products like airplanes have up to 2 million parts/objects, cars 20.000 parts and fashionable handbags only up to 20 combined items. The value varies around the globe depending where we are and where the object is sold.
People: Large organizations, companies, and the public sector employ millions of people, contractors, and advisors. For example, Amazon has about 600.000 plus employees globally. Their tasks are the same and comparable but priced differently.
Physical infrastructure including processes: Physical infrastructure needs to be drilled down and tagged as deeply as possible. Processes inherently bear a lot of intellectual property creating complex valuation problems even with a fair value option.
Products should be tracked using similar features as we have for IoT- devices. We can detect product pricing fraud by analyzing product flow by the lowest SKU level of tracked products.
Strict people fraud is not so much anymore on the value side (i.e. rates applied) of the equation but more on the efficient use of time. Charged hours should be correlated, detailed and analyzed to projects and tasks performed.
The misappropriation of physical assets needs more sophisticated gate-keeping to protect. Value ranges similar to information confidentiality levels should be established and analyzed accordingly.