According to All-Russian Insurance Association, in 2014 the insurers paid to fraudsters more than 50 billion Rubles. It is worth noting that corporate fraud with participation of insurance company staff accounts for up to fifty percent of the above damage. In the coming years we anticipate a further increase in fraud activities, especially involving organized crime groups.

The foregoing once again proves that fraud management cannot and must not be limited to cleaning the insurance portfolio because the best measurable result with comparatively low expenses will be possible to obtain through implementation of the action plan designed to detection and control of corporate fraud by making use of forensic instruments.

Key Forensic Services for Insurance Companies

• Fraud Detection & Prevention System (FDPS) – development and subsequent implementation of a specific add-in in the insurance company ERP for the automatic detection of losses and the insured risks that may be very likely caused by fraud.
• Closed File Review (CFR) – stratified sampling of closed files for the mass insurance (CASCO, MTPL, VMI, etc.) and the file documentary review plus analysis of data from insurance company electronic systems for finding the ways for loss reduction.
• Claims Settlement Assistance (CSA) – independent evaluation of above-limit and abnormal losses, especially those carrying reputation consequences for insurers: (1) special investigative activities; (2) elaboration and implementation of measures aimed to reduce insurance claim payments; (3) legal support and lawyers’ services in judicial proceedings.
• Computer Forensics – (1) extraction and analysis of data from corporation mobile devices and work computers; (2) analysis of integration and access rights for various databases.
• Compliance and Fraud/Corruption Management Services in accrodance with Russian and international laws on prevention of corruption/bribery (FCPA, UKBA).

Sample Fraud Schemes Detected Through Forensic Diagnostics in Insurance Companies

• Manipulation of the insurance reserves calculation data (RAS/IFRS statements) and other key analytical results with a view to misinform the management and/or the owners of insurance company.
• Insurance tariff reduction and/or budgeting, which will consciously deteriorate the estimate indicators as compared with planned indicators through presenting Loss ratio and other management specified rates as aggregated values (without adequate segmentation by regions, business lines, sales channels, products, etc.).

• Adjustment of fictitious losses, as well as intentional overstatement of insurance claim payments (including double payments) through fraudulent actions of insureds and loss payees, including collusion with the insurance company employees or vendors (vehicle service station and medical prevention institution, etc.)
• Intentional recognition of a “total loss” followed by understatement of the cost of salvage materials with their sales to parties affiliated with the insurance company employees.
• Manipulation with RBNS (Reported but not settled claims) data as of reporting date or during the reporting period to hide the actual losses and other key performance indicators from the insurance company owners (in particular, through data input delay, artificial lowering of average insurance claim payments, LAE overstatement, etc.)

• Intentional high-risk portfolio underwriting at understated rates in collusion between the potential customer and the insurance company employees, including through unauthorized data input.
• Intentional transferring “direct” (associated with zero commission) customers with low-risk portfolio into sales channels that allow paying agent’s commission to intermediaries affiliated with the insurance company employees.
• Conclusion of (obligatory or facultative) reinsurance agreements intentionally discriminating against the interests of the insurance company, including reinsurance premiums not adequate to reinsurance risks.

• Prejudiced selection of vendors and partners (vehicle service station, medical prevention institution, assessors, etc.) under terms intentionally discriminating against the owner, in particular through concluding contracts with the companies affiliated with responsible employees, as well as through various manipulations by holding the tenders.
• Unfounded bonus and premium payments, in particular, through distorting actual key performance indicators (KPIs);
• Salary payments to ghost employees and ghost contractors;
• Investment activities that intentionally discriminate against the owner’s interests, including lending and securities trading;
• Conclusion of leasing contracts, sponsorship and charity agreements, etc. under terms discriminating against the owner’s interests in the presence of more profitable market analogues.