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

Finance

Insurance

Life Science

Logistics

Manufacturing

Public Service

Retail

Telco

Utilities, Oil, Gas

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

Helvetia

ÖKK

Sanitas

Suva

Allianz

AXA

Basler Versicherung

Die Continentale

Ergo

Generali

Hannover Rück

HDI

Helsana

ITERGO

LVM Versicherungen

Provinzial

Talanx

VHV Versicherungen

Mobiliar

Success

Insurance company (Switzerland)

Conceptualization and implementation of the entire data management for a group-wide IFRS 17 implementation for a leading Swiss insurance company. In this process, more than 200 information systems of different group companies were connected.

General Insurance company (Germany)

Knowledge transfer in the field of data science, implementation of various advanced analytics use cases such as next-best-offer, next-best-customer, potential analysis and document verification. Establishment of a central advanced analytics environment. Automation of the document review leads to a saving of costs by an external reviewer and speeds up the processes.

General Insurance company (Switzerland)

Analysis, design and implementation of a planning system for cost centre planning including cost allocation and target/actual comparison. Survey of the cross-departmental technical requirements for the company-wide DWH system. Design and implementation of a company-wide data warehouse.

International Re-Insurance (Germany)

The Investment Analysis project is designed to enable the specialist department to evaluate the portfolio, risk, performance, stress test and order data of its investments in a structured manner. The application is used to monitor and adjust the customers investment strategy.