Successful insurer data and analytics projects allow insights to be more easily, and more broadly, available across the organization. Data initiatives also allow insurers to manage risk better by using analytics and to sell more by enabling automated decisioning and straight-through processing.
Aite-Novarica Group’s 11th annual Insurance Technology Impact Awards on data initiatives include 17 case studies. Each case study details business goals, project sponsors, team structure, project timeline, tools and technology used, challenges and success factors, and business impact delivered.
Data warehouse, reporting, and analytics capabilities remain top priorities for all insurers.
Data and analytics capabilities are near the top of insurers’ business priorities. Roughly half of all carriers continue to invest in business intelligence and data repository features in 2022, often in conjunction with core system and digital experience transformation efforts.
Mosaic Insurance, a midsize property/casualty Impact Award winner, is an example of an insurer taking a data-first approach to build a new underwriter experience from the ground up. Mosaic wanted to provide real-time data and analytics for its specialty lines underwriters, with data drawn from a large range of third-party sources, so it implemented an API-based analytics platform. Stood up in six months, the platform increased efficiency by 5% and generated US$6.4 million of new business.
Data initiatives are about making data useful and analyzing it.
Data and analytics projects are fundamentally focused on accessing and using information. Challenges around insurer data and analytics are often not about the lack of data; for many insurers, especially in the large property/casualty sector, data that’s siloed or trapped in legacy systems can pose its own unique challenges: There’s plenty of data, but it can be difficult to make that data useful.
Large property/casualty Impact Award winner EMC Insurance Companies implemented an enterprise data modernization project to service its operational, regulatory, and analytical data needs. The 30-month project delivered a modern enterprise data warehouse in the cloud, migrated 380,000 policies and billing data from 11 lines of business, and transformed legacy data at a 99% success rate.
Artificial intelligence (AI) and machine learning (ML) remain areas of active investment.
Insurers continue to actively invest in AI/ML, with roughly half of this year’s data projects including AI or ML elements. Applications spanned fraud analytics, claim severity estimation, automated decisioning, and more effective sales team targeting.
This year’s life/annuity/benefits winner, Penn Mutual, developed autonomous and semi-autonomous ML-enabled predictive models to improve automation and application throughput. The model enabled immediate or next-day decisions on 50% of applications and supported 28% sales growth without hiring new underwriting staff.
Data initiatives often deliver the highest value.
Data and analytics projects are some of lengthiest and most complicated projects insurers undertake, especially if AI or ML components play a role. But data initiatives are often among the efforts with the highest value. Modernizing data environments, creating new reporting tools, and training algorithms or ML models can all create substantial impact. Understanding this impact can be a critical path toward defining and measuring the value new data technologies deliver.
Insurance Technology Impact Case Study Compendium 2022: Data Initiatives provides insurance business and IT executives with examples of ways they can leverage technology to create business value. Solution providers can also use these case studies to frame the ways their products can create value for their insurer clients. Access to the report can be found here.