12d → 2d
Motor-claim cycle
−34%
Handling cost
+28
Customer NPS
+52%
Fraud detection rate
Client
Leading APAC General Insurer
Sector
Insurance
Duration
10 months
Team
30 specialists
01 · The challenge

Problem

Motor-claim handling time was 12 days on average. Fraud was leaking, costing the insurer USD 22M annually. Customers were leaving for digital-native competitors.

02 · How we delivered

Solution

Claims AI platform: automated FNOL, document intelligence, fraud-graph engine, and integrated repairer dispatch. Live-parallel against existing flow for 6 weeks.

03 · Outcome

Impact

Motor-claim cycle from 12 days to 2 days. Handling cost down 34%. Customer NPS +28. Fraud detection rate up 52%. 1.2M claims processed in year one.

How we delivered

Programme phases.

Five phases. One accountable team. Every phase had a named decision point and a measurable outcome.

Discovery & alignment

2–3 weeks

Workshops with the Leading APAC General Insurer executive team, baseline metrics, target outcome tree, programme governance set up.

Design & architecture

4–6 weeks

Reference architecture, security blueprint, joint squad model agreed. Data model and integration contracts published.

Build & live-parallel

Q2 onwards

Vertical slice built and run live-parallel against the existing system. Continuous integration, daily deploys, weekly business demos.

Cutover & scale

Mid-programme

Phased cutover, audit-aligned reconciliation, scaling out of squads, capability transfer to Leading APAC General Insurer teams.

Run & continuous improve

Steady state

Managed run with named SLOs, quarterly value reviews, and a 15% optimisation budget reserved for improvement work.

Engineering view

Architecture overview.

Foundations

Cloud landing zone, identity, network, security baseline. Data fabric with lineage-by-default. Audit-grade observability stack from day one.

Application & integration

Domain-aligned microservices behind a published API surface. Event-driven core with CDC into the data fabric. Live-parallel capability built in, not bolted on.

Trust & governance

RBAC, audit logs, lineage, policy-as-code. Model risk records for every production model. Compliance posture on the executive dashboard, not in a quarterly slide.

Built on

Technology stack.

Production-grade choices, defended by track record. The stack is one engineering decision among many — but a load-bearing one.

Azure Cognitive Services Pega SQL Server PowerBI Pega Decision Hub
Trust by design

Governance & assurance.

01

Programme assurance

Independent assurance reviews at each phase gate. Findings tracked in a single risk register with named owners and remediation deadlines.

02

Security & data

ISO 27001, SOC 2 Type II controls applied throughout. Data lineage captured by default; sensitive data tokenised at the edge.

03

Model risk management

SR 11-7-aligned model risk record per production model. Audit-trail evidencing model behaviour against benchmarks at the decision level.

04

Regulator engagement

Quarterly briefings to the regulator with reproducible explainability artefacts. First-attempt acceptance is the default expectation.

A claim used to be a phone call and a wait. Now it is an app and a notification.

C Chief Claims Officer · Leading APAC general insurer

What we learnt

Three things we would do again.

  1. 01

    10 months from kickoff to first regulated outcome — squad density and decision velocity matter more than headcount.

  2. 02

    Joint squads with Leading APAC General Insurer engineers stayed in place after go-live. Ownership did not transfer in a hand-off — it grew in place.

  3. 03

    Live-parallel for a meaningful window before cutover bought us trust. The cutover itself was a flag flip, not a war room.

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