According to a new study report there has been an increased focus of analytics in nearly all the spheres of the insurance industry, however the property-casualty insurers are facing a serious challenge in attaining their analytical goals. Information technology leaders especially the P&C insurers suffer with obsolete data infrastructure that inhibits the analytics and adds the average quarter percentile of the overall cost of the IT projects.
Poor data infrastructure is known to affect analytics; it also costs the industry more than billion dollars a year making the insurers realize the severity of the issue. Some of the major problems faced by them include the following:
- Incongruent Legacy systems.
- Composite data structure.
- Lack of development in master data management.
As the industry is trying to pace up, insurers have initiated to implement programs on the lines of enterprise data warehousing, operational data stores, big data technology, improved data governance as well as organisational changes.
Enterprise data improvements support the following:
- Predictive analytics programs for underwriting and claims
- Straight and thorough processing
- Cross selling and up selling
- Underwriting and “account –aware” services.
- Enterprise risk management.
- Customer profitability analysis
It has been observed that while the underwriting and claims are the natural elements for programs that explore quality enterprise data infrastructure, distribution and marketing are growing as significant users of data and systems. In order to cope up with the demands for forward movements in the data world, insurers are ensuring the initiation and application of changes and improvisations in the organisation.
The study also suggests that, integrated data systems brought into the wider business carrier models rather than being siloed as a standalone unit is part of the data maturation process. It has also been indicated by most chief information officers that they expect the organisation to develop analytics to manage the burgeoning volume of data across consumer lines nationwide. Staff training on analytics is also being emphasized for the use of data in decision making at the leadership level to implement enterprise data upgrades.
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