Insurance pricing is where profit is made or lost — and where most insurers still rely on aging factor tables and gut feel. Redian Software builds ML-augmented pricing and rating engines that sit inside the quote flow, are governed for regulator conversations, and are re-trained on a cadence your actuaries and audit function accept. CMMI Level 3 Appraised, ISO 27001 and 9001 certified, with nine years of insurance and AI/ML delivery across 200+ enterprises from Noida, Nairobi, Dubai, London and New York.
What we deliver
- Rating engine — rule-driven rating for motor, health, property, commercial and bespoke lines, configurable by underwriter.
- ML pricing models — GBM, GLM and neural models where the loss history supports it, wired into the quote flow with millisecond latency.
- Actuarial audit trail — model cards, dataset provenance, drift monitoring, back-testing and versioning ready for the regulator.
- Explainability — SHAP, LIME and business-language reason codes on every quote.
- Bias and fairness testing — protected-attribute audits and evidence packs.
- Underwriter workbench — override, referral and constraint capture with reason codes flowing back into training data.
- Retraining pipelines — versioned datasets, evaluation harness, human sign-off before promotion, rollback on drift.
- Integration — PAS, broker portals, aggregator surfaces and internal rating tools.
Who we build this for
- Mid-market carriers with enough loss history to train a model
- MGAs and MGUs needing bespoke pricing logic not supported by generic broker software
- Aggregators and comparison sites needing sub-second quote generation
- Reinsurers piloting portfolio-level pricing models
- Digital-first insurers launching with a data-first pricing strategy
- Insurance groups consolidating pricing logic across multiple entities under one platform
Our approach
Discovery — problem framing, data audit, loss history review, actuarial policy interviews, feasibility spike. Baseline model — reproducible GLM or GBM baseline against real data in a secure enclave, benchmarked against your current rating. Governance framework — model card, dataset provenance, drift monitoring, bias-testing framework agreed with actuarial and regulator-facing teams. Engineering — production pipelines, MLOps, deployment inside your VPC, latency and SLO targets. Integration and UAT — model in the actual quote flow, underwriter workbench for overrides. Go-live and monitoring — evaluation dashboards, drift alerts, quarterly re-training. Timeline committed at end of Discovery, in writing.
Why Redian for ML pricing
- Domain-embedded data scientists — insurance engineers, not lab hires learning your business on your dime.
- Regulator-aware — SHAP, LIME, model cards and bias testing built for IRDAI, FCA, NAIC, IRA Kenya, NAICOM conversations.
- MLOps in the base plan — versioned datasets, drift monitoring, rollback.
- CMMI Level 3 Appraised, ISO 27001 and 9001 — governed process auditors accept.
- Model-neutral — GLM, GBM, neural and hybrid picked per line, not per vendor.
- 9+ years, 200+ enterprises — production ML in supervised environments, not POCs.
- Multi-region delivery — Noida, Nairobi, Dubai, London and New York.
Where we have delivered
Pricing and rating programmes for carriers, MGAs and aggregators across India, Kenya, UAE, UK, USA, Canada and Australia, with additional programmes in Nigeria, Ghana, Tanzania, Uganda, Rwanda, Ethiopia, South Africa and Saudi Arabia. Reference deliveries include pricing behind InsureMe — an East African insurance aggregator.
Working with Redian
Send us the lines of business, the loss history you have and the KPI you need to move. A senior data scientist responds inside one business day with a written first read and a feasibility position. NDAs signed on request. Start at /contact or read our AI/ML expertise and insurance practice.
