Insurance Technology
That Reduces Risk & Accelerates Growth.

Straight-through processing means a claim is automatically assessed, validated, and settled without human intervention. STP is appropriate for a subset of claims — typically simple, low-value, high-frequency claims where the facts are clear (e.g., minor property claims, standard vehicle glass claims, certain health insurance claims). The implementation involves: a rules engine that classifies inbound claims against STP eligibility criteria, data enrichment and validation steps (fraud scoring, policy verification, coverage check), automated reserve setting within defined limits, payment instruction generation, and policyholder notification. Most carriers can achieve 40-70% STP rates for appropriate claim types. We design the classification model and rules based on your historical claims data before building the automation.

We implement ML-based fraud detection rather than rule-based systems. The distinction matters: rule-based systems are easy for fraudsters to game once the rules are known, generate high false positive rates on innocent customers, and cannot detect novel fraud patterns. ML-based detection trains models on your historical claims data — identifying the features and combinations of features that characterise fraudulent claims — and scores new claims in real time. The score is used to route claims to automated settlement, standard adjustment, or SIU referral. We also implement application fraud detection for new business, catching misrepresentation at quote and bind stage rather than at claims. Our typical implementation achieves 85%+ fraud detection rates with false positive rates below 5%, compared to 40-50% detection and 15-25% false positives for rule-based systems.

Yes. For Solvency II, we typically work on the data infrastructure that feeds SCR/MCR calculation systems and generates Quantitative Reporting Templates (QRTs) for Pillar 3 submission. We build the data pipeline from source systems (policy, claims, investments) through the calculation layer to automated QRT generation and National Competent Authority submission. For IFRS 17, we work on the integration between actuarial calculation systems, general ledgers, and IFRS 17 engines — typically automating the period-end process that converts actuarial outputs into IFRS 17 journal entries and disclosures. Both programmes require deep understanding of the business data and calculation logic, not just the technology — which is why we work with your actuarial and finance teams throughout.

Yes. InsurTech and established carrier engagements require different approaches. For InsurTech companies, we typically work on platform architecture design (API-first, cloud-native, microservices), regulatory sandbox navigation, core system selection (build vs buy vs configure), and the data and analytics infrastructure needed to prove risk models to reinsurance partners. For established carriers, we more commonly work on modernisation — legacy system integration, digital channel build, claims automation, and analytics. The insurance domain expertise and the technology delivery standards are the same; the starting point and the pace are different.

Insurance data migrations — from legacy PAS to Guidewire, from old claims systems to modern platforms — are among the riskiest technology changes in financial services. Policy and claims data goes back decades, contains complex relationships, and must be accurate for regulatory, legal, and operational reasons. Our approach: complete data audit before migration begins (what exists, what is needed, what the quality problems are), data cleansing and transformation specification written and approved before any migration code is written, parallel running period where source and target systems both process data (reconciliation reports daily), and a cut-over plan with a tested rollback procedure before any production migration happens. We have completed migrations for insurers ranging from 50,000 to 8 million policies in force.