Insur­ance
- Improved performance through powerful analytics

Why

Insurance Gains from Data and Analytics




In the insurance industry, it is essential for both the insurer and the insured to derive mutual benefits from their partnership. This win-win relationship can be further strengthened through data analytics, providing numerous advantages to the insurance world.

Big data and analytics can be leveraged to efficiently handle low-volume, high-demand claims, while more granular data can be processed to optimise analytics techniques for risk assessment, and to enable the development of new insurance services. AI is used to detect claims at an early stage. The use cases for big data and analytics in the insurance industry are diverse, and include optimising the customer journey, detecting fraud, managing risks, improving business processes through process mining, performing image recognition for motor vehicle damage, and evaluating damage based on images.

Well prepared

Expert knowledge in Insurance





D&A Use Cases


Here are a few examples of use cases we've implemented from a pool of over 100 projects:

 

  • Standard use cases like customer segmentation, customer value assessment, campaign management, new customer acquisition, customer retention, churn prevention, cross-selling, complaint management.
  • Finding new potential commercial customers.
  • Optimising business processes through process mining.
  • Improving the customer journey.
  • Fraud detection and risk management.
  • Call centre reporting.
  • Image recognition procedures for motor vehicle damage.
  • Optimising sales through affinity ranking of customers for a specific product, such as motor insurance.
  • Self-service BI.
  • Establishing governance structures (CC-DWH).


Preparation and Tools


For example, we made use of:

 

  • Hortonworks, Apache Spark, Hive, Python, R.
  • Python libraries: Scikit-learn, Seaborn, Pandas, Matplotlib, FuzzyWuzzy.
  • Random Forest and Gradient Boost Classifier.
  • Oracle, Informatica, IBM Cognos, SAS, SAP Business Objects.
  • Oracle, Microstrategy, DWinsurance.
  • IBM DB2, DataStage, DWinsurance.
  • Teradata, SAS, DWinsurance.
  • Hortonworks Data Platform (HDP), Apache Ambari, Apache HBase, CentOS.
  • IBM DB2, Business Glossary, WebSphere Info Server, Cognos, Rational Data Architect.
  • Hortonworks Data Platform, Cloudera CDH, Hadoop Core Framework, Apache Hive, Sqoop, Spark, Kafka.

Over the years we have developed a standard insurance data model template for core data warehousing and multiple business marts, DWinsurance.




Standards and Regulation


We are guided by the following standards and regulations:

 

  • Knowledge of IFRS 17 implementation.

 

  • Automated submission of mandatory statistics to public authorities.

 

  • Preparation for the third pillar (reporting) of the Solvency II requirements - Documentation to trace the processes.

Customers

Our customers in Insur­ance


Success



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