Regulators and partners increasingly expect insurers and their distribution partners to identify complaints quickly and track them to resolution. At high volume, the hard part is not reading every complaint but recognising the few that are critical: a vulnerable customer, a potential conduct issue, a pattern that points to a systemic failure.
In a financial-services partnership programme within our work for a Fortune 500 specialty insurer, more than 4,000 complaint records arrive every day1. Before the triage engine, critical issues were buried in routine ones, classification was too slow for compliance timelines and prioritisation differed from analyst to analyst.
Triage by impact, not by arrival
Impact-based prioritisation
Levels assigned by the triage engine and the response each one triggers
| Level | Meaning | Response |
|---|---|---|
| L0 | Critical | Immediate escalation |
| L1 | High | Same-day review |
| L2 | Medium | Standard queue |
| L3 | Low | Batch processing |
Note: From the specialty-insurer case study (scale of the build).
Source: DaasLabs, “Case study: GenAI customer intelligence for a Fortune 500 specialty insurer” (2026)
The engine detects frustration intensity, decides whether a complaint is in scope and assigns a level. L0 and L1 complaints are flagged and routed for immediate analyst review, so the critical case is seen the same day rather than when it reaches the top of a queue1.
Keep the analyst in the loop
Every critical classification is validated by an analyst through a review interface, and overrides feed back into the model. This is not a concession to caution; it is how the system earns trust and gets better. It also gives compliance a record of who decided what, and why.
- Scope first: separate in-scope complaints from noise before prioritising.
- Explain the level: show the phrases and signals behind each classification.
- Measure the overrides: a rising override rate is an early warning that the model or the business has changed.