Once again, ZyLAB sponsored the European Conference of the Association of Certified Fraud Examiners which this year was held in London. Alongside several ZyLAB clients and partners, I too was present at the event that included a number of rather interesting presentations.
Several speakers highlighted the fact that in any business, employees continue to form the greatest security risk, especially in relation to cybersecurity: there are still people who simply cannot resist clicking on clearly suspicious links and thereby downloading malware. The biggest shock to me was that in the US, 27% of all employees were willing to sell their corporate login-credentials for 100 USD! This leaves companies no other choice but to highly compartmentalize information, use several layers of security and work on a need-to-know basis whenever possible.
Another interesting insight from the conference was that the famous Fraud Triangle consisting of 3 dimensions (rationalization, opportunity and pressure) was challenged. Speakers suggested that there are more dimensions to fraud, including softer measures such as culture. Interestingly, the n-dimensional model that was presented resembles ZyLAB’s approach. This model uses text-mining and analytics techniques to extract semantical and syntactical details or large volumes of information related to a wide variety of dimensions such as personal names, companies, organization, job titles, sentiments, emotions, but also higher level patterns describing aspects of the modus operandi.
The importance of data analytics
Showing such analyses in facets using, for example, a WordWheel, hierarchies or other visualization tools, really helps fraud investigators carry out early case assessments and get quick insight into the relevant dimensions of a particular incident.
Listening to the speakers, it became very clear to me that data analytics will continue to play a crucial role in fraud investigations. This is especially so when it is necessary to automatically and quickly organize and align relevant extracted information from electronic evidence and present it in the most effective way to the investigator according to his or her particular work-related needs and requirements.