Aligning Investments

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You don’t have to look far to read something about AI in the insurance industry. It’s being used to underwrite and price, to automate claims processing, to detect fraud, and more. But what if AI could help you determine if your core processing system is adequate? What if it could give you objective, data-driven insights into your legacy infrastructure’s condition, performance gaps, risks, costs, and future viability? What if it could align your technical investments with your operational needs?

That would change a game or two, wouldn’t it?

Older policy admin systems are likely to be COBOL or mainframe based. If so, they’re rigid, hard to maintain, and unable to accommodate capabilities like real-time processing, AI-driven underwriting, personalized products, or regulatory compliance. AI can accelerate the discovery phase of legacy review projects, reduce risks, and facilitate decisions to keep and patch, to incrementally modernize, or to fully replace with a modern core processing platform.

More specifically, AI can:

  • Scan legacy code and extract business rules, workflows, data flows, and undocumented dependencies
  • Generate process maps, documentation, and visualizations
  • Reveal hidden technical debt, complexity, and maintainability issues
  • Analyze operational data (processing times, error rates, downtime, peak-load handling) from legacy systems; predict performance and scalability issues based on trends in claims volume, policy growth, or regulatory change and simulate what-if scenarios
  • Build cost-benefit and ROI models comparing maintenance costs for legacy systems (including obsolete skills), migration costs, one-time expenses, savings, and revenue gains from modern systems.

 

Risk Management

AI can also help manage the risks associated with legacy or replacements. Aside from mitigating migration risks, AI can identify outdated security protocols and regulatory compliance gaps. It can perform operational benchmarks to show competitive disadvantages. It can analyze service tickets, audit logs, or survey feedback to identify and quantify pain points. And it can gather requirements and simulate architecture and process options to determine if full replacements are justified.

Are these applications of AI foolproof? No. Can AI make final yes/no decisions in isolation? Without human evaluations of strategy, budget, culture, and vendor options, no. But AI can eliminate guesswork and bias from system evaluations and can cut assessment time dramatically while improving accuracy.

So, if you’re wondering whether your core system is a strategic asset or a growing liability, AI-powered assessment tools can provide a clear, fast path to an informed answer. Many carriers are already using them in their digital transformations.

When it comes to your technology infrastructure, AI seems like a sound way of aligning your investments.