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From Core System Replacement To Data Modernization, A New Imperative For Insurers

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8 February 2024
Jaspaul Saini

Just as COVID-19 provided the “aha” moment for accelerating digital transformation in the insurance industry, generative AI (Gen AI) is the “aha” moment for data modernization. There is a sudden realization that the game has changed, and many in the industry are woefully unprepared.

As companies look to modernize their existing data capabilities, they need to focus on two areas that will better prepare them for the future:

1.Clean, Unified Data

In the Gen AI era, the saying “garbage in, garbage out” is even more relevant. The complexities of obtaining, cleansing, and unifying data across multiple systems in an insurance organization have always been challenging. However, with the proliferation of third-party data sources and the ingestion of unstructured data sources like voice and video, this has become even more difficult. Additionally, the evolving regulatory landscape for data privacy and the use of data for AI decision-making demand a renewed focus on strong data management and data governance capabilities.

2.Modern Cloud Data Platform

Modern cloud-based data platforms, comprising data lakes, cloud data warehouses, and data lakehouse architectures, provide a single managed and scalable environment to meet an organization’s end-to-end data and analytics needs. This includes ingestion, storage, transformation, and governance of vast quantities of structured and unstructured data as well tools for business intelligence, AI, ML (machine learning) and now, Gen AI. These platforms provide a cloud-based framework where compute and storage requirements can be rapidly scaled as needed, usually on a usage-based pricing model. A huge benefit of these platforms is that they enable a cost-effective path for organizations to take advantage of more powerful technologies as their own needs evolve. For example, an initial goal might be to develop a reporting data warehouse for analyzing core system data, followed by development of AI and ML apps that need to combine internal data with real-time external data feeds for customer data insights. A longer-term goal might be to utilize Gen AI technology that is augmented with internal company data.

While many insurance carriers have invested heavily in modernizing their core systems (policy, claims, billing), far fewer have modernized their data and analytics capabilities. The introduction of modern core systems presents both a data challenge and an opportunity for organizations.

The power of core systems is in the richness and quality of the data they hold. The challenge is how to get the data out of these systems, combine it with other data sources, and generate meaningful insights.

The opportunity lies in rethinking the enterprise data strategy and modernizing the data ecosystem to not only satisfy immediate reporting needs but also position the company for the future of advanced analytics, machine learning, and Gen AI.

The goal should be to phase out dependence on legacy, on-premises data technologies and to begin the path to data modernization utilizing modern cloud data platform technologies that serve as the “source of truth” for all the data needs of the organization.

Various vendors offer accelerators and expertise in integrating with common core system platforms, simplifying the process of extracting, transforming, and loading policy, claims, and billing data into proven insurance data models.

Whether an organization chooses to build a cloud data platform itself or leverage end-to-end SaaS insurance reporting vendor solutions, there are many options available to help modernize, dramatically improve data capabilities, and prepare for the future.

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