Insurance companies

Identification and Anti-fraud Services

Within the insurance market, insurers face a wide variety of issues. These include an increase in fraud at all stages of the insurance lifecycle, cost rationalization, and access to impartial tools which insurers can use to set internal targets. Also, the concept of sharing information to combat fraud and reduce insurers’ operational costs has gained more and more importance.
CRIF offers solutions for more accurate identity verification and anti-fraud management in the key stages of the process, including quotation, underwriting, claims handling and fraud investigation, to help insurance companies increase their profits, improve their knowledge of customers, and efficiently manage risk assessment.

Insurance bureau services. Streamlining risk assessment and claims management

The insurance bureau services developed by CRIF allow the insurance industry to confidently assess the motor, home, personal injury, health, marine, travel and pet insurance history of customers. Constantly enhanced to meet the demanding requirements of insurers that are moving fraud prevention and risk assessment processes upstream, insurance bureau services effectively support insurance companies throughout the customer lifecycle, from policy quotation to the claims stage. Thanks to a technologically advanced platform able to handle the high volume of requests required for quote screening, high performance is guaranteed. Insurers have the flexibility to choose subsets of data services and information needed to build up specific risk and fraud prevention models, giving total control to the insurer.
The Insurance Bureau Service can also work in combination with credit and payment transaction data, as a close correlation has been proven between bad payment behavior and propensity to claim.
Insurance companies may decide to contribute data relating to claims only or claims and payment transactions related to their customers. Data enquiries and ring-fencing will be guaranteed by reciprocity rules.


  • Ability to increase premium revenue and profitability by charging rates linked to the true risk involved.
  • Improved quality of the business portfolio and reduced exposure by accurately identifying risks.
  • Reduction in the manual effort and costs involved in confirming at the underwriting stage whether an individual has consistently disclosed his or her claims history and can therefore benefit from a “no claims” discount.
  • Reduction in claims expenses and faster processing through more efficient claims handling.
  • Control of overall costs by identifying multiple and potentially fraudulent claims.

Sherlock. Optimizing insurance fraud detection and investigation

Sherlock is a fraud prevention service which allows insurance companies to manage the identification and fraud prevention stages faster and more efficiently.
Multilingual and multiline (supporting motor, home, personal injury, health, marine, travel and pet insurance lines), Sherlock can integrate any internal and external information source, and analyze the results using innovative machine learning and analytics tools. A unique and intuitive interface enables claims and policies to be quickly classified on the basis of the actual risk of fraud, checking personal identity, reporting anomalies, and performing customer intelligence activities.

Using tools which allow users to configure their own expert rules and multidimensional anomaly identification functionality, it is now possible to move from a verification approach based on the characteristics of the individual claim and with a limited number of variables, to the analysis of a combination of multiple variables which are difficult to analyze manually and using traditional algorithms. Therefore, Sherlock enables potentially unknown or not previously identified fraud scenarios to be discovered and individual cases to be examined in more depth, carrying out additional checks on related subjects, third parties and associated addresses through the network analysis function, showing the identified links graphically. Through a simple and comprehensive report, it is possible to quickly identify any anomalies and areas of risk, which require further investigation and to analyze the searches carried out thanks to the traceability of all the activities performed on each subject under investigation.
Users can carry out interactive searches in real time, and view on a single screen the aggregate results of all the links generated from a wide range of variables relating to individuals, companies, addresses, vehicles, e-mail addresses, telephone numbers, etc., which could also be relevant across different claims.
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  • Identifies hidden claims connections between parties of past and present claims.
  • Performs a complete claims risk evaluation in just a few seconds thanks to more than 200 expert rules developed by our fraud specialists. 
  • Evaluates the level of anomaly of each claim, based on proprietary machine learning techniques to selectively manage claims based on relevancy.
  • Provides a detailed output that allows you to start an investigation immediately.

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Elixir. Mitigating potential risks and information sharing

Elixir is used by major life insurance companies for screening, reporting and alerting users on the financial standing of brokers and independent financial advisers (IFAs). Elixir members supply information on debts, legal actions, repayment schedules and written-off amounts using a web-based application. The system helps insurance companies validate and monitor the entire lifecycle of their business relationships with individual IFAs, supporting confidence, transparency and efficiency in relation to commission credit and debt.
Relevant data is compliantly shared between Elixir insurer users to mitigate potential risks from IFAs who may systematically target multiple insurers in order to commit commission fraud and IFAs who may repeatedly require extensive and long-term commission credit due to financial instability or propensity to be subject to legal action. This in turn protects insurers from reputation risk, and supports mutually beneficial relationships with their IFA partners.

Predicting future trends with big data analytics and scoring

CRIF provides scoring models and expertise, empowering business analysts, from beginners to advanced modelers, allowing them to develop, build, test, deploy and manage predictive models.
Predictive analytics allow insurance companies to extract information from existing data in order to determine patterns and predict future outcomes and trends. It forecasts what might happen in the future with an acceptable level of reliability, and includes what-if scenarios and risk assessment.
Today, new methodologies, such as machine learning and Genetic Algorithms used to solve problems and find indicators, or Evolutionary Neural Networks, and the availability of unstructured, social media, and behavioral data, are used to optimize the relationship between information.

The application of knowledge discovery and data mining through the use of Link Analysis and Neural Networks enables identification, analysis and visualization of patterns in data; embracing intelligent technology to auto learn and inform processes.
CRIF has been investing in research and development to design advanced techniques in risk profiling and to develop our Analytics expertise, drawing on information about human behavior, lifestyles and habits and computing large quantities of both structured and unstructured data available via social media platforms and beyond, dishonest behavior and discrepancy information, to generate a more powerful and accurate predictive risk profile.