[AI]For effective AI, insurance needs to get its data house in order

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A report from Autorek, a provider of AI solutions to the insurance industry has produced a report that describes operational drag in companies’ internal processes that not only affect overall efficiency but cause an impediment to the effective implementation of AI in insurance concerns.

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draws from a survey of 250 managers in the sector from the *** and US. The survey’s responses paint a picture of connected bottlenecks that include slow settlement processes and data fragmentation. The report also covers the current state of AI deployment in the industry.

Companies surveyed in the sector report persistent structural inefficiencies:

14% of operational budgets are spent correcting manual errors, 22% of those questioned said reconciliation complexity is a significant cause of cost increases, Around 22% of respondents link inefficiencies to governance and audit risks, Nearly half of firms operate settlement cycles in excess of 60 days.

Transaction volumes are projected to rise by roughly 29% in the next two years means, the report claims, and OPEX burdens are likely to rise commensurately. The report attributes this to the combination of manual processing, disparate data systems, and the transactional complexity that’s the nature of modern insurance operations. The persistence of such processes, the authors state, is despite its previous publications’ findings being in the public domain for some time.

There is a gap between respondents’ expectations of what AI might deliver and implementation of the technology on the ground. The headline figure is that 82% of firms in the sector expect AI to dominate the industry, yet only 14% of companies have fully-integrated AI in their operations. Six percent of companies report no use of AI at all.

What are the barriers to AI in the insurance sector?

The report identifies legacy system integration, fragmented data, and limited internal expertise as the main issues companies need to address to implement AI. The issue of fragmented data affects data governance frameworks, making the latter similarly piecemeal. The report’s authors cite complex data estates in many companies as the main reason that AI deployments are constrained in the sector.

Firms surveyed managed an average of 17 data sources, and a majority cite this as an issue, one that’s compounded after mergers and acquisitions.

The report’s authors imply AI will affect costs and scalability positively and could address some of the issues firms experience around manual error correction and mistakes in reconciliation processes. The report suggests decision-makers could target reconciliation processes for an initial proving ground for AI, given it’s a boundary-ed, rules-based domain where automation can yield fast positive results.

Any form of automation, AI or deterministic, placed on a fragmented architecture and a fractured data layer may not scale well without a rise in costs. The report highlights the potential for AI in structuring fragmented data sources, and suggests cloud-based, as opposed to in-house AI platforms may be an answer in that respect.

Structural issues

The dichotomy between reconciliation processes (essentially structured workflows) and disparate data sources that need manual nurturing creates complexity that’s measurable in cost and cycle times. This is a situation that persists despite a broad awareness of the issues among those surveyed.

The report asserts that such firms successful in addressing the issues at a structural level will widen the performance gap. Data standardisation and governance precede scalable automation, and eventually, automation will reduce reconciliation costs. AI could address the complexity of fragmented data and software layers that rules-based automation such as RPA (robotic process automation) may not be able to address economically.

The rate at which firms can resolve the data fragmentation issue is dictated by legacy technology and the overheads of day-to-day operations. The extent to which AI deployment could translate into performance gains beyond cost reduction is unclear, but if cost reduction is positive outcome enough, then addressing the structural issues affecting the insurance sector would form a solid basis for AI-powered automation.

(Image source: “Scattered pieces” by Cle0patra is licensed under CC BY-NC-SA 2.0.)

 

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