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Redian Software
Insurance solution

Insurance pricing that actually responds to risk

Machine-learning powered insurance pricing and rating engine. GLM + gradient-boosted hybrid models, real-time scoring, A/B price testing, regulator-grade explainability (SHAP/LIME), drift monitoring. For carriers, MGAs and aggregators.

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CMMI Level 3 Appraised ISO Certified 200+ enterprises 5 regional hubs 9+ years of BFSI
Outcomes our customers see

The numbers we move.

Production benchmarks from real deployments — not vendor brochures.

  • <100ms

    p95 inference latency

    Real-time quote pricing

  • +4 pts

    Combined ratio improvement

    Insurance pricing engagement

  • Hybrid

    GLM + ML

    Regulator-defensible + accuracy lift

  • SHAP

    Explainability

    Per-quote reason codes

What's in the platform

Capabilities, end to end.

A complete module list — designed to remove the gaps where vendor platforms typically leave you in spreadsheets.

  • 01

    Hybrid GLM + ML modelling

    GLM as regulator-defensible base layer, gradient-boosted residual model for the lift. Best of both — interpretability and accuracy.

  • 02

    Real-time scoring

    Sub-100ms p95 inference at quote time. No batch pre-computation. Every quote is freshly priced against current model.

  • 03

    A/B price testing

    Production rate version control with stratified customer assignment. Statistical-significance-aware termination. Champion-challenger architecture.

  • 04

    Explainability

    SHAP / LIME reason codes per quote. Regulator-ready audit trail. Has held up under IRDAI and FCA review.

  • 05

    Drift monitoring

    Automated alerts when input distributions or model outputs drift. Quarterly retraining triggers. PSI tracking and rate distribution monitoring.

  • 06

    Model governance

    Versioning, evaluation pipelines, approval workflows. Models don't get to production without passing the gate.

Who deploys this

Built for the operating environments we know best.

We've shipped this platform across the most common patterns — find the closest fit to your operating model.

  • P&C carriers

    Motor, property, travel, liability carriers needing competitive pricing with regulator defensibility.

  • Insurtech startups

    Digital-first insurers building pricing as a competitive moat from day one.

  • Aggregators & marketplaces

    Insurance comparison platforms needing real-time pricing across multiple carrier products.

  • MGAs

    Managing General Agents operating binding authority programmes needing pricing engine within risk appetite.

  • Health & life

    Underwriting-heavy products where ML can refine risk assessment beyond traditional actuarial tables.

  • Micro-insurance

    High-volume low-value products where rate accuracy and speed both matter to unit economics.

Implementation

How a rollout unfolds.

Phased, milestone-driven, with parallel-run safety nets where regulators require them.

  1. 01Weeks 1–4

    Data audit & feature work

    Data quality assessment, feature engineering, baseline GLM. The 30% of effort that actually determines model success.

  2. 02Weeks 5–10

    Model development

    GLM + gradient-boosted hybrid. Iteration cycles with weekly evaluation. MLflow / SageMaker / Vertex AI infrastructure live.

  3. 03Weeks 11–12

    Explainability & governance

    SHAP integration, model documentation, governance workflows, regulator-aware audit trail.

  4. 04Weeks 13–14

    Integration & deployment

    API contracts with PAS or quote-and-buy front-end. Latency tuning. Shadow-mode period.

  5. 05Week 15

    Go-live

    Production deployment with champion-challenger setup. A/B testing infrastructure active from day one.

  6. 06Ongoing

    Monitor & retrain

    Drift monitoring, A/B testing, quarterly model retraining, regulator review absorption, bias audits.

Solution overview

In depth — how this platform runs.

The long-form view of capability, architecture and deployment model.

The next generation of insurance pricing

Traditional rate tables are blunt instruments — coarse-grained, slow to update, blind to most of the signal in your data. ML pricing engines fix that. Ours combines GLM (regulator-friendly) with gradient-boosted models (signal-capturing), runs in real time during quote, supports A/B price testing in production, and ships SHAP-based explainability that holds up under FCA or IRDAI review.

What the engine does

  • Hybrid modelling — GLM as the regulator-defensible base layer, gradient-boosted residual model for the lift. Explainable both ways.
  • Real-time scoring — sub-100ms inference at quote time. No batch pre-computation; every quote is freshly priced.
  • A/B price testing — production rate version-control with random or stratified customer assignment. Statistical-significance-aware termination.
  • Explainability — SHAP / LIME reason codes per quote. Regulator-ready audit trail showing why this customer got this rate.
  • Drift monitoring — automated alerts when input distributions or model output drift from training. Quarterly retraining triggers.
  • A/B + champion-challenger — compare new model versions against the production champion before full rollout.

Where it fits

Motor, health, travel, property, life, micro-insurance. Standalone or integrated with our Policy Administration System. Open APIs let you plug into any PAS or quote-and-buy front-end.

Why Redian

What makes this platform different.

Independent reasons clients pick us over incumbents and over generic global platforms.

  • Regulator-defensible

    GLM base layer + SHAP explainability. Has held up under IRDAI and FCA scrutiny — not just demo-stage ML.

  • Production-grade MLOps

    Versioning, evaluation, drift monitoring, rollback live from sprint 1. Not bolted on for Phase 2.

  • Champion-challenger by default

    New models earn their production rollout through A/B testing — they don't just get deployed and hoped.

  • Insurance ML depth

    We've shipped pricing engines for general insurance, health and life — across the UK, India and Africa.

Tech & integrations

What the platform talks to.

Open APIs, standard integrations, configurable from day one.

  • Python
  • PyTorch
  • TensorFlow
  • scikit-learn
  • XGBoost
  • LightGBM
  • statsmodels
  • GLM
  • MLflow
  • SageMaker
  • Vertex AI
  • SHAP
  • LIME
  • Optuna
  • Airflow
  • dbt
  • Snowflake
  • BigQuery
  • Spark
  • REST APIs
  • Kafka
  • Redis
  • Kubernetes
  • AWS
  • Azure
Proof from production

A deployment that mirrors your use-case.

Real customer · real numbers · real go-live. Most of our work is under NDA — this is one we can share publicly.

InsuranceKenya

Insurance Distribution Platform for Kenya-based InsureMe

Client · InsureMe

  • Live

    Multi-insurer aggregator

  • M-Pesa

    Secure payment + Lipa PolePole

  • Real-time

    Policy generation

InsureMe — Kenya's digital insurance aggregator — runs on a Redian-built platform comparing policies across insurers, processing M-Pesa payments and issuing policies in real time with NTSA/KRA verification.

Tech stack

AngularNode.jsMySQL
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Frequently asked questions

Everything you wanted to ask before the demo.

Don't see your question? Ask us directly →

What's the difference between GLM and ML-based pricing?

GLM (Generalised Linear Models) is the actuarial standard — interpretable, regulator-friendly, but limited to linear relationships. ML (gradient boosting, neural nets) captures non-linear patterns and interactions but is harder to explain. Our engine combines both — GLM base layer for regulator defensibility, ML residual for accuracy lift, SHAP explainability across both.

How explainable are the model decisions?

Every quote ships with SHAP reason codes — top features pulling the rate up, top features pulling it down. Audit-trail per quote, retained for regulator review. We've deployed this under IRDAI and FCA scrutiny.

Can we A/B test price changes in production?

Yes — production rate version control, customer assignment (random or stratified), statistical-significance-aware termination. Champion-challenger setup lets new model versions earn their rollout, not just be deployed and hoped.

What latency do you guarantee at quote time?

Sub-100ms p95 inference latency for typical pricing scenarios. Sub-50ms for streamlined motor and travel pricing. Designed for real-time aggregator and direct-to-consumer quote flows.

How is drift handled in production?

Automated drift monitoring on input distributions (population stability index) and output distributions (rate distribution shift). Alerts at configurable thresholds trigger retraining workflows. Quarterly model review and retraining is standard.

Can the engine work with our existing PAS?

Yes — open REST APIs. We've integrated with BancsBranch, Genelco, Synergetics, Eurobase and several in-house PAS. Also integrates natively with our own PAS for tightest data flow.

Still figuring it out? Tell us your operating environment and we'll send a tailored architecture and pricing within one business day.

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See it live

Ready for a tailored ML Pricing & Rating Engine walkthrough?

Tell us your regulator, your incumbent system and the outcome — we'll send a demo plan and pricing within one business day.

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