What are some technologically advanced insurance companies

There are a variety of problems that insurers face in the insurance market. These include the increase in fraud at all stages of the insurance life cycle, cost rationalization and access to objective tools that insurers can use to set internal targets. The concept of exchanging information to combat fraud and reduce operating costs for insurers is also becoming increasingly important.

CRIF provides solutions for more accurate identity and anti-fraud management at key stages of the process - including bidding, underwriting, claims settlement and fraud investigation - to help insurance companies increase their profits, their knowledge of customers improve and efficiently manage the risk assessment.

Insurance Bureau Service

The solutions developed by CRIF for the insurance industry make it possible to assess the history of car insurance, household insurance, accident insurance, health insurance, transport insurance, travel and pet insurance. The services of the Insurance Bureau are continuously improved in order to meet the high requirements of insurers with regard to upstream processes of fraud prevention and risk assessment. In addition, they effectively support insurance companies throughout the entire customer lifecycle, from issuing the insurance policy to the claim. Thanks to a technologically advanced platform that is able to meet the volume demand required for supply screening, high performance with supporting service levels is guaranteed. Insurers have the flexibility to choose subsets of data services and information needed to build specific risk and fraud prevention models, leaving the insurer in complete control.

The Insurance Bureau's services can also work in combination with credit and payment transaction data, as a strict correlation has been demonstrated between poor payment behavior and the frequency of claims reports.

Insurance companies can decide whether they only provide claims or claims and payment transaction data related to their customers. The data query and the accounting group, the so-called ring fencing, are guaranteed by reciprocity rules.


  • Possibility of increasing premium income and profitability by calculating the premium rates taking into account the actual risk.
  • Qualitative improvement of the business portfolio and risk minimization through precise risk assessment.
  • Reduction of the manual effort and costs associated with confirming in the underwriting phase whether a person has fully disclosed their claims history and can benefit from a no-claims discount.
  • Reduction of claims expenses and faster processing through more efficient processing of claims.
  • Control total costs by identifying numerous and potentially fraudulent insurance claims.


Sherlock is a fraud prevention product that enables insurance companies to manage the identification and fraud prevention phases faster and more efficiently.

In multiple languages ​​and for multiple domains (it supports auto insurance, household insurance, accident insurance, health insurance, transportation insurance, travel and pet insurance), Sherlock can integrate any source of information inside and outside the insurance company and evaluate the results with innovative machine learning and analysis tools . A unique and intuitive user interface allows you to quickly classify insurance claims and policies based on actual risk of fraud, verify personal identity, report anomalies and conduct customer intelligence activities.

With tools that allow users to configure their own expert rules and multi-dimensional anomaly detection functions, it is no longer possible to fall back on the characteristics of a single claim and a limited number of variables. Instead, you can switch to analyzing a combination of several variables that are otherwise difficult to analyze manually and use conventional algorithms. Therefore, Sherlock enables the detection of potentially unknown or previously undetected fraud scenarios and the more detailed investigation of individual cases through additional checks on related persons, third parties and known addresses via the graphical representation of the network analysis with the identified links. A simple and at the same time comprehensive report makes it possible to quickly identify all anomalies and areas of risk that require further investigations, and - thanks to the traceability of all activities carried out on the individual examined subjects - to analyze the research carried out.

Finally, users can conduct real-time interactive research and view, on a single screen, the aggregated results of all the associations generated from a wide variety of variables related to people, companies, addresses, vehicles, email addresses, phone numbers, etc. and could be important across several different claims.


Elixir is used by major life insurance companies to screen, report, and notify users of the financial condition of brokers and Independent Financial Advisors (IFAs). Elixir members provide information on debt, lawsuits, litigation, amortization schedules and amounts written off through a web-based application. The system helps insurance companies to validate and monitor the entire life cycle of their business relationships with individual, independent financial advisors and ensures trust, transparency and efficiency with regard to commissions requested and owed.

The relevant data is eagerly shared among Elixir users in order to minimize the potential risks posed by unscrupulous independent financial advisers. Because it can happen that independent financial advisors systematically target several insurers in order to commit commission fraud. The system in turn protects insurers from reputational risks and supports mutually beneficial relationships with their IFA partners.

Big data analytics and scoring

CRIF provides both business analysts and data modelers with scoring models and know-how to support them from development and testing to management of forecast models.
Predictive analytics enables insurance companies to extract information from existing data to determine patterns and predict future outcomes and trends. In particular, this enables reliable forecasts of potentially future events, taking what-if scenarios and risks into account.

Today, new methods - including machine learning, genetic algorithms to solve problems and identify indicators or evolutionary neural networks, the availability of unstructured social networks and behavioral data - are used to optimize the relationship between information.

The application of knowledge discovery and data mining, using link analysis and neural networks, enables the determination, analysis and visualization of data patterns.

CRIF has invested in research and development to design advanced risk assessment techniques and develop our analytics DNA. The latter pulls information on human behavior, lifestyles and habits and calculates large amounts of structured and unstructured data (available through social media platforms and beyond), unfair behavior and information discrepancies to create a more powerful and accurate risk profile with predictive power.