AI Agents

Context

Built for insurance within the Renewals & Upsell stack and used by product, renewal, and retention teams, this agent focuses on fit: whether the customer is still in the right product and coverage configuration, given how their risk and behaviour have evolved. It’s triggered around renewal or periodic review, asking “is this still the most suitable product setup for this customer, or is there a better match in our portfolio?”.

What it does

The agent looks at the customer’s current policy (covers, limits, deductibles, add-ons), their recent behaviour and claims history, and any usage/telematics signals where available. It then compares that profile against the insurer’s product catalogue and suitability rules to find better-fitting options: for example, suggesting a different product tier, adjusted limits or deductibles, removal of clearly unused add-ons, or migration to a usage-based/packaged product that aligns with how the customer actually lives or drives. The output is not an automatic switch, but a small, ranked set of “suitability re-match” options that can be surfaced to agents, brokers, or directly to the customer as renewal alternatives.

Core AI functions

The core capability is profile-to-product matching. The agent encodes product suitability criteria and constraints (eligibility, target profile, risk appetite, regulatory restrictions), derives a compact risk/usage profile per customer, and scores how well each available product configuration fits that profile. It then turns those scores into a few concrete, explainable options with simple rationales (“lower annual mileage and no recent claims → suitable for product X with higher deductible and lower base premium”).

Problem solved

Many portfolios are full of customers sitting in the “wrong” product configuration: over-insured on some dimensions, under-insured on others, paying for add-ons they never use, or stuck in a traditional product when their behaviour would suit a usage-based one. Today, suitability reviews tend to be manual, campaign-driven, or triggered only by complaints and major events. That leads to churn (“I found a product that fits me better elsewhere”) and regulatory questions about whether products remain suitable over time. This agent systematises the “are they still in the right product?” question at scale, instead of leaving it to chance.

Business impact

The main impact is better retention and healthier portfolio quality. Customers who are proactively offered a configuration that fits their life and budget are more likely to stay, complain less about value for money, and engage in conversations about trade-offs (limits, deductibles, optional covers) rather than silent lapses. For the insurer, re-matching improves overall product suitability, supports Fair Value and “good customer outcome” expectations from regulators, and can steer business toward products with better risk/return profiles.

Integration and adjacent use cases

Integration complexity is medium: the agent needs access to policy data, basic customer/behavioural signals (including, where relevant, telematics/usage outputs), product catalogue and suitability rules, and renewal/CRM workflows where suggestions are presented; it then writes recommended alternatives and rationales back into those journeys.

Common combinations in this stack:

• Lapse Risk Predictor to prioritise suitability reviews for customers most likely to churn;

• Premium Change Impact Explainer to pair a clear “why your premium changed” story with concrete alternative configurations the customer can choose from;

• Usage-Based Data Interpreter (Telematics) to feed interpreted usage patterns into the suitability logic and help explain why a telematics or non-telematics product might now be a better fit; and

• Cross-Sell / Upsell Trigger Agent to propose complementary covers or bundles after the core product has been re-matched, so that upsell feels like a natural extension of a well-fitting base policy rather than an aggressive add-on push.