Fraud data from other sectors can help insurers identify and tackle policy application and claims fraud, reducing the financial impact of insurance fraud on the industry and genuine customers. That’s the considered view encompassed within a new report from Cifas and Hill Dickinson.
The report explains the results of a cross-sectoral data match between motor insurance fraud cases and fraud cases from other sectors, such as retail, banking and telecommunications. It reveals “complex networks” of criminal activity and strong links between false claims on motor insurance and other fraudulent activity, such as money laundering. Data relating to the chronology of frauds has also been uncovered, providing a more detailed insight into the behaviours of fraudsters.
The data matching exercise* was between a sample set of confirmed fraud cases from Hill Dickinson’s Netfoil ACE database and confirmed fraud cases held on the Cifas National Fraud Database. In total, 196 of the Hill Dickinson cases matched on details within 347 Cifas National Fraud Database cases.
Key findings are as follows:
*32% of false claims on motor insurance involved individuals who were also found to be involved in other types of fraudulent activity. The majority (82%) of matches were against cases in other sectors, showing the extent to which the same fraudsters are operating across different industries
*31% of the individuals involved in both insurance claim fraud and other forms of fraud committed the other fraud first, showing that the sharing of cross-sector data could have alerted insurance fraud investigators to the fraud risk at an early stage
*42% of matches were on activity indicative of money laundering, suggesting strong links between insurance fraud and serious organised crime
Fraudsters operating across regions
The study results also suggest that insurance fraudsters are operating across regions. Not only are some individuals involved in frauds at multiple addresses in the same region, but many are also linked to frauds in other cities. London, Manchester, Birmingham, Nottingham and Sheffield exhibit the highest numbers of linked fraud networks.
These findings highlight the crucial role of intelligent data matching in fraud prevention. In some cases, fraudsters had deliberately used different names and addresses to evade detection, but were identified purely through common contact details such as mobile numbers or e-mails.
Mike Haley, deputy CEO at Cifas, said: “These findings set insurance fraud into a much wider criminal context. The Case Studies unearthed by our research reveal vast networks of fraudulent activity. Intelligent use of cross-sector data can help insurers combat fraud and reduce the cost of insurance for honest customers.”
Haley added: “Fraudsters are some of the best collaborators out there. To combat them, fraud prevention professionals need to work even more closely together and share intelligence wherever possible.”
Peter Oakes, head of fraud at Hill Dickinson, commented: “This research demonstrates that financial services fraud is a volume-based enterprise. We know the fraudsters targeting the insurance industry are determined and prolific. We’re even beginning to see potential evidence of the franchising of fraud ring models.”
Oakes concluded: “Cross-sector data sharing unquestionably assists organisations to prevent fraud at application and better manage any existing fraud risk. Hill Dickinson is committed to assisting insurers in their use of cross-sector data and intelligence to strengthen counter fraud controls.”
Given the number of matches found in this pilot study and the fact that the average loss from a single organised motor insurance claim case is approximately £20,000, it’s evident that the insurance industry has the potential to save millions through increased cross-sector sharing.
*The data matching exercise was between a sample set of 618 confirmed fraud cases related to 2,307 individuals from Hill Dickinson’s Netfoil ACE database, and more than two million confirmed fraud cases held on the Cifas National Fraud Database