Fraud analytics is an emerging tool of the 21st century as it relates to detecting anomalies, red flags, and patterns within voluminous amounts of big data, which is quite challenging to analyze. The use of fraud analytics does not always have to be complex and costly for small and medium-sized enterprises (SMEs) to afford. While technology has played a key role in increasing opportunities to commit fraud, the good news is that it can also play a major role in developing new methods and strategies that can be used to detect and prevent fraud.
While no fraud prevention measures can ever be perfect, significant opportunity for improvement can be achieved by looking beyond the individual data points in an organization, to the connections that link them. Often these connections go unnoticed until it is too late, something that is unfortunate, as these connections often hold the best clues.
More law enforcement and private companies are finding and integrating fraud analytics within their everyday regime when working on investigations or merely conducting forensic accounting techniques. Craig McAdam (a renowned British fraud resolution lawyer) says, “SMEs are the backbone of any of any economy. When companies lose thousands of pounds due to fraud, it can make a difference between being able to invest and innovate or not.”
Fraud Detection: Using Data Analysis Techniques
Basic to advanced strategies are being employed these days for fraud examination of data. The rationale is that unexpected patterns can be symptoms of possible frauds. The sole objective of fraud analysis is to develop the most precise and valid inferences possible from whatever information is available.
The identification of possible duplicate transactions would be a possible symptom of fraud that should always be examined. Ordinarily, say one would expect that invoice number-vendor number combinations, would be unique. Therefore, the existence of transactions with the same invoice number-vendor number combinations would be an unexpected pattern in the data.
Another digital analysis technique is to identify even value amounts, numbers that have been rounded up. The existence or reoccurrence of even amounts in some accounts may be a symptom of possible fraud and should ideally be probed further. Let us take the case of frequent rounding of travel expenses including boarding and lodging invoice values, as this may points to some interesting story.
Another useful fraud detection technique is the calculation of ratios for key numeric fields. Like financial ratios that give indications of the relative health of a company, data analysis ratios point to possible symptoms of fraud. Three commonly employed ratios are: the highest value to the lowest value (maximum/minimum); the ratio of the highest value to the next highest (Maximum/Second highest) and the ratio of the current year to the previous year. Unexplained deviations could be symptoms of fraud. In a number of cases, high ratios or abnormal values, deviating from industry standards or current business scenarios, have often thrown light on peculiarities, which pushes for further explanation or demands a microscopic relook.
Mapping relationships is equally an important aspect, although a sensitive subject for fraud investigators. It helps identify potential vulnerabilities within a network between entities to identify temporal, event, and association networks. As the organizations grows in size and stature, the more complex relations gets developed within and outside the establishments, which needs to be monitored, off and on for any untoward development of ambiguity other than normal. It could reveal normal and anomalous patterns of interaction within and between people or groups, can expose facilitators and enablers of fraud, follow transaction trails using link analysis, identify key individuals spread from stakeholders to gatekeepers.
Graph and Text Analysis
Sometimes ‘flat’ data does not tell the whole story. Adding spatial operations enhances analytics with an additional dimension based on patterns, relationships, and inferences. Simple graphs or any visualization tool can bring to surface some glaring unusual behavior in business. There may be correlations that are only visible in graphs or in visuals, which could be easily identified using statistical techniques like cluster analysis, spatial recognition and outliers.
Similarly, text examination could be another built-in secret security framework, if done at random intervals and coverage, which could analyze unstructured data like emails, files soft copies of internal or external documents, tweets, blogs and social media contents for sentiments, common themes, and relationships.
Without a major investment for SMEs, a business or tech savvy professional(s) can use one of the applications or statistical package like RStudio, Python, Rattle GUI, RCommander, Rapid Miner, Python, SAS, Tableau, XL Miner etc to effortlessly start a self analysis journey.
Many of them are open source softwares which are easy to learn and adapt to, through their graphical user Interfaces (GUI), with a plethora of support material available on web. These have in-built capability to identify subsets of raw data, clean data, gather and decipher potentially relevant information. It can positively impact detection, recommendations and resolutions. Industry expert Ronald Coase says, “Torture the data and it will confess to anything.”
However, fraud symptoms are only symptoms and care should be taken to properly investigate each aspect before jumping to any concrete conclusion. The actual analysis relies on the critical thinking skills of the fraud examiners or analyst’s ability to integrate the output into a cohesive actionable analysis product. By implementing a solution to combat frauds, SMEs can firmly but surely take the first step towards a proactive approach.
Reposted with permission (original)