From Manual to Autonomous: Rethinking Insurance Automation in the GenAI Era

Insurers have long understood the benefits of automation: streamlining workflows, improving customer service, and freeing up agents from menial tasks.

While some digital-first insurers are pushing towards full automation, most insurers remain only partially automated, stuck with tools that can’t meet modern demands. These traditional carriers are still working through foundational challenges such as data silos, outdated workflows, and limited AI literacy, making it difficult to scale automation without ballooning complexity and costs.

GenAI is redefining what it means to automate, enabling decision intelligence across areas like underwriting, claims, servicing, and more.

To realize the full promise of automation, insurers must adopt a phased approach to AI adoption in order to scale responsibly, track progress, prioritize investment, and manage risk. As the role of GenAI in insurance continues to expand, here’s what insurers need to know.

Traditional Automation Falls Short

Historically, automation in insurance was synonymous with rule-based systems and robotic process automation (RPA), both tools that are effective for repetitive tasks but fall short when deviations or nuanced decision-making arise. But with claims costs rising, regulatory scrutiny intensifying, and customers now expecting swift, hyper-personalized experiences, today’s market demands more.

AI-driven automation is helping prioritize such demands.

GenAI has the potential to enhance underwriting, predictive risk assessment, and personalization across the insurance value chain. But adoption is only the beginning – without a clear strategy for implementation, insurers risk automating inefficiently, unknowingly triggering compliance risks, and losing out on the full benefits of GenAI.

The Five Levels of Insurance Automation

Inspired by the five-tiered classification system for autonomous driving capabilities, insurers are using their own automation maturity model to better assess the progress of their automation.

• Level 0 (Manual): Common in legacy environments and among small mutual providers, level 0 insurers still do everything by hand – manual data entry, spreadsheets, and paper forms dominate operations.
• Level 1 (Basic): At the most basic automation level, tasks like quote generation or STP (straight-through processing) for simple policies are partially automated, but humans still control the main flow of operations.
• Level 2 (Emerging): Here, automation drives most workflows, but there is still an expectation that humans will intervene for edge cases, where unusual claims conditions or other uncommon situations are at play.
• Level 3 (Advanced): At level 3, the full policy lifecycle can be automated for standard lines like auto or home insurance, with human input only required for more anomalous insurance situations. Automated claims payout and renewal triggers are hallmarks of this level.
• Level 4 (Full Automation): Level 4 insurers use GenAI tools and Machine Learning (ML) models to manage the entire end-to-end lifecycle, from initial customer interactions to the final payout, with humans only providing strategic oversight. Lemonade, for instance, can process renters’ claims in under two seconds without human review, exemplifying successful total automation.

Any level is a good starting point, but for modern insurers with competitive aspirations, full automation should be the goal.

To achieve this, insurers need keenly organized data, AI governance and compliance frameworks, and auditable decisioning processes to address ethical concerns, AI hallucinations, or bias. Training staff to collaborate with AI – prompting correctly, reviewing outputs, and guiding edge cases – is just as important as the tech itself.

Automation Driving Value

So, what does reaching AI-driven automation practically mean for insurers?

In claims processing, GenAI is speeding up triage and first notice of loss (FNOL) assessment – so much so that 76% of insurers have already implemented or are planning to embed GenAI capabilities in their claims workflows.

This capability is particularly strategic in the case of fraud detection, due to AI’s unique ability to identify abnormal patterns that might be missed by traditional models. Mastercard, for example, has already successfully implemented GenAI driven fraud detection measures, doubling the speed and accuracy with which they can alert merchants to fraud risks and reducing false positives of fraudulent transactions by up to 200%. Insurtechs are similarly layering GenAI on top of fraud databases to cross-reference claims in real time.

Underwriting is also improved by AI-driven decision-making support tools, which can surface submission risks in real-time and promptly recommend next steps. Many companies are piloting GenAI tools that analyze submission data and generate preliminary assessments, reducing underwriter time on low-value tasks.

Finally, GenAI improves customer service throughout every single insurance touchpoint, bolstering service quality and speed with AI-powered virtual agents and GenAI chat bots.

The AI Roadmap: Start Small, Scale Strategically

Automation in insurance is not a binary switch, nor will it yield the “quick wins” that many insurers may expect. GenAI is the engine, but insurers with their sights set on automation must drive the journey with intent – draw a levelled roadmap, scale strategically, and track progress over time. By benchmarking maturity and pairing AI with human judgment, insurers can automate with confidence, positioning themselves to drive the future of intelligent insurance.

It’s not just a matter of convenience. It’s about making the often-challenging moments when people turn to their insurers easier to navigate than ever before.