The insurance industry is becoming increasingly focused on the digitalization of its business processes. There are many factors driving digitalization, but it’s clear that a reliable and meaningful database is the basic prerequisite for a successful digitalization strategy. Insurance companies are increasingly prioritizing digitalization, not because this issue is currently
It's the age of big data and the internet of things (IoT), but how will that change things for insurance companies? Do insurers still need to consider classic data warehouse concepts based on a relational data model? Or will all relevant data be stored in big data structures and thus
Auditability and data quality are two of the most important demands on a data warehouse. Why? Because reliable data processes ensure the accuracy of your analytical applications and statistical reports. Using a standard data model enhances auditability and data quality of your data warehouse implementation for business analytics.
Insurers are embracing digital to meet the demands of modern consumers. And, of course, there are obvious benefits to them from less costly, more streamlined interactions with their customers. The trouble is that digitisation comes with a major health warning: Unless insurers put suitable measures in place, they're at risk
Using a standardized data model is an essential condition to achieve data governance in an enterprise. A standard data model supports data governance processes by implementing industry standards wherever possible: standards for contract and claims representation, mapping of data content with standard definitions (glossary function), use of code attributes
“All for one and one for all” is best known as the motto from “The Three Musketeers”, but this phrase could easily sum up the growing trend in social brokers. With advanced analytical techniques like generalized linear modeling insurance companies have created more granular pricing structures. But despite the assertions
Over the years I have written many blogs about insurance fraud including those on anti-money laundering, data quality in fraud, anti-fraud technology, life insurance fraud and even ghost broking. It’s clear that insurance fraud comes in many shapes and sizes and as losses continue to grow, detecting and preventing fraud
Insurance is a tough marketplace, but in many respects reinsurance is tougher! Today, the reinsurance industry is faced with an unprecedented number of challenges especially with what appears to be an increasing frequency and severity of man-made and natural catastrophes. To combat these challenges, reinsurers are turning to technology for
In my first blog article I explained that many insurance companies have implemented a standard data model as base for their business analytics data warehouse (DWH) solutions. But why should a standard data model be more appropriate than an individual one designed especially for a certain insurance company?
One of my colleagues often asks me “What’s new in insurance”. For an industry that is risk adverse, change does not come easily. In the past we have discussed innovations concerning telematics, drones, wearables devices and even weather data. However when he asked me last week and I responded that