Financial Fraud & Error Detection for CEOs & CFOs
Get started with Predictive Analytics and Machine Learning
The Old Way
CFOs and CEOs historically needed to set up expensive infrastructure to hold data warehouses, process heavy analytic workloads on expensive CPUs, and hire expensive coders to analyse their financial data for errors and fraud. The large initial infrastructure investment precluded cash poor and small-to-mid-sized organisations from proactively monitoring their financial data Furthermore, headcount restrictions often prevented organisations from hiring and keeping the right coders/analysts to produce the reporting.
The New Way
Today, with a small investment, savvy organisations could (and should) be routinely monitoring and evaluating their financial data in a secure cloud, to automatically produce exception reports which identify cases of potential fraud and errors. The new paradigm… pay for only what you use (storage and compute), then shut down the platform till you need it again! Whether you have an existing cloud data platform or not, this is a risk that can be eliminated with a minor investment. Furthermore, many organisations with medium to large transaction volumes immediately discover/recover/rectify errors greater than the value of the initial investment!
Why?
Ask yourself the following questions:
- Have we ever entered and/or paid an vendor twice for the same invoice?
- Do we have any vendors who’s bank details match those of our employees?
- Are the vendor ABNs in our system valid?
- Have any payments been entered and approved by the same employee?
- Have you overpaid (or underpaid) any of our employees?
- Are we correctly paying our employee's superannuation?
- Are there any 'ghost employees' (fake employee receiving payments)?
- Have we overpaid (or underpaid) GST?
- Are there any suspicious or unusual transaction amounts?
There are hundreds of similar questions prudent CFOs and CEOs should be asking of their data. Due to availability of cloud platforms, the analysis of small and large data sets is a reality within the reach of any CFO or CEO.
What can be achieved?
The following analytic techniques are a realistic outcome for organisations large and small:
- Rule-based Analytics: Detecting errors and fraud based on known relationships or criteria.
- Anomaly Detection: The identification of abnormal data (e.g. conformation to Benford’s Law).
- Predictive Analytics: Statistical analysis of current and historical data for forecasting purposes.
- Machine Learning: Identifying unknown patterns using historical data.
- Visual Analytics: Gaining insights through the graphical representation of relationships and data.
