AI development
for insurance.
Insurance has used statistical models for centuries, so the novelty here is not prediction but the ability to work with unstructured evidence: photographs, reports, medical records, and correspondence. That is where most of the untapped value sits, and it comes with real fairness and governance obligations.
Discuss your projectHigh-value use cases.
Claims triage
Routing claims by complexity and likely value at first notification, so straightforward claims settle fast and complex ones reach specialists immediately.
Damage assessment
Estimating repair cost from claimant photographs. A well-bounded computer vision problem with a directly measurable financial outcome.
Fraud detection
Network and behavioural analysis to surface organised fraud that per-claim scoring misses. A graph problem as much as a classification one.
Document extraction
Pulling structured facts from medical reports, police reports, and schedules — the volume work that consumes handler time.
Underwriting support
Risk assessment from submission documents, with explainability and fairness review built in from the start rather than added under pressure.
Reserving and severity
Predicting ultimate claim cost earlier and more accurately, which improves reserving and reduces development surprises.
Sector-specific constraints.
Generic AI advice fails here for specific, predictable reasons. These are the constraints that shape every design decision we make in insurance.
- Fairness is regulated — proxy discrimination through correlated variables is a live exposure in pricing and underwriting
- Actuarial governance — models face independent validation and documentation requirements
- Adverse decisions need reasons — declines and pricing loads must be explainable to the policyholder
- Long development tails — ultimate claim cost is unknown for years, delaying ground truth
- Catastrophe non-stationarity — climate-driven change makes historical frequency a weak guide to future risk
- Adversarial claimants — fraud adapts to detection, so patterns shift deliberately
AI in insurance.
How do we prove an underwriting model is not discriminatory?
Test for it directly rather than relying on the absence of protected attributes from the feature set. Measure outcome disparities across protected groups, check whether included variables act as proxies — postcode frequently proxies for ethnicity, for instance — and use explanation methods to see what actually drives decisions. Document the testing, the thresholds you deemed acceptable, and the mitigations applied. Excluding a protected attribute does not prevent a model from reconstructing it.
Can AI settle claims automatically?
For narrow, low-value, well-evidenced claim types, straight-through processing is achievable and already common. The realistic pattern is a confidence-banded one: automate the clear cases, route the uncertain and high-value ones to handlers with the model's assessment attached as a starting point. Keep the automation threshold conservative and monitor the automated band closely, since errors there are unreviewed by definition.
How do we handle the long delay before we know if a claim prediction was right?
Use interim signals rather than waiting for ultimate outcomes. Track prediction stability, agreement with handler assessments, and early development markers that correlate with final cost. Validate on historical cohorts that have fully developed, and re-validate as newer cohorts mature. This is a specific case of the labelling-delay problem described under model drift, and it argues for deliberate, well-monitored retraining rather than frequent automated cycles.
Other industries.
Healthcare
Custom AI and ML development for healthcare — clinical decision support, imaging, and documentation, built for HIPAA, audit, and c…
ReadFinancial Services
Custom AI and ML for banking, payments, and asset management — fraud detection, credit risk, and document automation built for mod…
ReadLogistics & Supply Chain
Custom AI and ML for logistics and supply chain — demand forecasting, ETA prediction, routing, and exception management built on m…
ReadLet’s scope it
properly.
Tell us the problem you are trying to solve in insurance and we will tell you honestly whether machine learning is the right tool.
Start the conversationor write to us at [email protected]