Visualizing Fraud Patterns
Increase in fraud - a sign of the times?
In economically challenging times, we see a marked increase in the incidence of corporate and personal fraud. The challenge facing many companies and entities is to find affordable anti-fraud solutions which are adaptable enough to keep pace with advances in technology and the increasingly sophisticated methods deployed by fraudsters. The missing link in fraud detection has been the ability to predict future behaviour of fraudsters, utilizing past behaviour patterns. KeyLines enables visualization of fraud patterns and networks, offering an invaluable tool in the armoury of the fraud prevention officer in predicting and combating this highly lucrative crime.
What do we mean by fraud?
Despite the definition of fraud in legal terms being fairly broad and falling into a number of categories, its main purpose is usually some kind of material gain. The line between fraud and other crimes can be blurred as fraud can stray into other areas such as identity impersonation and falsifying of scientific results, but at the end of the day, financial gain or damaging another person are usually the motivating factors. If fraud can be detected and proved – and therein lies the difficulty - it is categorized as a criminal offence. However, increasingly the focus is on proactive fraud prevention. There are many types of fraud:
- Identity fraud (50%)
- Use of another living person’s identity to apply for an account or service.
- Use of identity and current address of victim (“Current Address Fraud”) still accounts for the highest proportion of identity fraud.
- Misuse of facility (20%)
- Obtaining a facility with the express purpose of using it for a fraudulent purpose.
- Such as setting up of a bank account by a “money mule” and voluntarily allowing it to be used on behalf of third parties to launder money.
- Facility takeover (15%)
- Hi-jacking of a bona fide facility such as a bank account or credit card by the fraudster.
- Often unauthorised transactions are made or account details changed.
- Application fraud (15%)
- The applicant opening a facility in his or her own name, but the application contains a falsehood, for instance, an inflated income or a falsified document such as a false bank statement.
- For example, obtaining a much higher mortgage than would be allowed by the applicant’s real income by fraudulently misrepresenting income.
- Asset conversion fraud and false insurance claims are additional examples.
An increasing proportion of fraud is occurring online, encompassing all the above categories. In the UK, for instance, almost 2% of online revenues are lost to fraud (source: Cybersource). As e-commerce increases, so does the overall revenue lost to fraud. Many online merchants expect their revenue to increase by up to 20% in 2012, and therefore the amount lost to fraud will increase correspondingly unless anti-fraud methods improve.
Fraud itself has not changed because of new technology: it is the fraudsters who have changed their approach. They are more organized and are able to exchange information on new purchasing processes and platforms they can target. Fraud prevention therefore requires skill and strategic planning in conjunction with new tools to keep pace with the fraudster.
KeyLines – an indispensable aid to fraud detection
Fraud detection tools such as online card validation number (CVN) or SecureCode, electronic fingerprint matching and customer behaviour pattern analysis are all increasingly being deployed to detect fraud. However, more sophisticated means are required in order to combat the increasingly collaborative approach used by fraudsters.
An indispensable method employed by investigators in the fight against fraud is the analysis of fraud cases successfully solved, and extrapolation of the results to prevent future fraud. This is achieved by building up a visual picture of behaviour patterns and fraudster collusion which can be easily shared for fraud prevention purposes. This approach is particularly effective in the Identity and Application Fraud situations and Facility Misuse, in which fraudsters tend to exhibit repetitive and predictable behaviours which lend themselves to visual highlighting.
KeyLines is a tool which allows networks of fraudsters to be easily visualized and presented. Compatible with all major web browsers and platforms, KeyLines draws a picture of fraudster networks using raw data consisting of personal identifiers. Links between individuals in the group can then be rapidly identified, providing vital clues on their role in the network and their behaviour patterns.
Organizations who provide investigation services already offer sophisticated systems which incorporate tools such as facial recognition and identity matching. The integration of network visualization tools greatly enhances their value to investigators dedicated to combating and preventing fraud in the 21st century.