Insurance App Development

Insurance engineering sits at the intersection of actuarial logic, regulatory obligation, and legacy system integration. Every InsurTech, MGA, and carrier operating in this space faces the same structural challenge: a modern digital layer that must remain permanently synchronised with a Policy Administration System (PAS) built a decade or more ago.

The engineering complexity is not just in building quoting engines or claims portals. It is in mapping your domain model to a PAS schema you did not design, handling state machine transitions that live partly in your application and partly in a system you cannot fully control, and ensuring that a bind event in your API results in a valid policy record without manual reconciliation.

Insurance software development at this level requires teams who understand Guidewire, Duck Creek, Majesco, and Sapiens integration patterns alongside the product and rate filing obligations that govern what you can quote, bind, and sell by jurisdiction. Scrums.com builds dedicated engineering teams for InsurTechs, MGAs, and carriers that need to move faster than their PAS allows.

Insurance Platform Architecture

PAS Integration

The most common source of scope growth in insurance software development is underestimating PAS integration complexity. Guidewire PolicyCenter, Duck Creek Policy, Majesco Policy, and Sapiens ALIS each expose different API surfaces, event models, and data schemas. Integration architecture must establish a canonical domain model in your application layer rather than exposing PAS data structures directly to the rest of the system.

This decoupling allows your quoting, claims, and portal layers to evolve independently of PAS version upgrades. Contract tests at the PAS boundary catch breaking changes before they reach production. File-based fallback patterns handle PAS downtime without halting bind operations.

Real-Time Quoting Engine

A quoting engine for personal or commercial lines must return indicative premiums in under two seconds while orchestrating calls to rating services, data enrichment providers, and eligibility rules. Save-and-resume state management across sessions reduces abandonment on longer journeys.

Data enrichment at quote time typically includes telematics scores, geodata flood and subsidence risk, credit reference signals, and MVR records for motor lines. Each enrichment call adds latency and introduces failure modes. The rating rules service must be versioned independently so actuarial changes can be tested against historical quote data before deployment.

Claims Infrastructure: FNOL to Resolution

Claims infrastructure spans first notification of loss intake, triage and assignment, reserve setting, payment, and closure. Multi-channel FNOL intake (web, mobile, telephony integration) must capture fraud signals at the point of notification rather than during investigation. Structured intake forms guide claimants through the information required for automated triage without adding friction that suppresses reporting.

Triage routing logic assigns claims to adjusters or automated settlement tracks based on claim type, reserve estimate, and fraud score. PAS state machine synchronisation ensures that adjuster actions in your platform produce the correct policy record states without dual-entry.

Our insurance app development teams build across the full stack of InsurTech and carrier technology platforms:

Policy Administration Systems (PAS)

We build or extend PAS platforms handling the full policy lifecycle: quoting, binding, endorsements, renewals, and cancellations. Our teams work with both legacy mainframe-era systems requiring modernisation and greenfield cloud-native builds. Integration points include rating engines, document generation services, and agent portals.

Claims Management Platforms

Claims systems require careful state management across intake, assignment, investigation, settlement, and closure. We build FNOL intake flows, adjuster workflow tools, reserve tracking, and payment disbursement integrations. Scrums.com teams have built claims platforms for personal lines, commercial lines, and specialty insurance carriers.

Quoting and Rating Engines

Rating logic is complex and heavily regulated. We build configurable rating engines that allow product teams to adjust factors, tiers, and rules without engineering deployments. Integrations include third-party data providers (credit bureaus, telematics, property data) and distribution channels (agent portals, comparison aggregators, direct-to-consumer).

Technology Stack for Insurance Platforms

Backend and API Layer

Java or Kotlin Spring Boot for core policy and claims service layers, where strong typing and robust ORM tooling match the complexity of insurance domain models. Python for data-intensive workstreams: rating model integration, actuarial calculation APIs, and ML-based fraud scoring. REST or GraphQL for frontend-consumed APIs; gRPC for internal microservice communication where latency is a constraint. Domain-Driven Design patterns (aggregates, bounded contexts) are well-suited to insurance systems where policy, claims, and billing are distinct domains with different consistency requirements.

Rules Engine and Product Configuration

Drools (a Java-based rules engine) or custom DSL implementations for rating logic and eligibility rules that must be configurable by product teams without engineering deployments. A properly versioned rules service with rollback and test harness capability is a significant competitive advantage in MGA and InsurTech contexts where product iteration speed and regulatory auditability both matter.

Document Generation

Templated policy documents, renewal notices, and certificates of insurance are generated using iText or Apache FOP for PDF output with precise layout control. ACORD standard XML schemas are required for certain broker distribution channel integrations and carrier data exchange scenarios. Document generation must be triggered reliably as part of the policy bind event sequence, with storage and retrieval integrated into the document management layer.

Data and Analytics

PostgreSQL as the primary operational database for policy and claims state. Apache Kafka for event streaming between services and into the data warehouse. Redshift or BigQuery for analytical workloads: portfolio reporting, loss ratio analysis, and regulatory reporting. Warehouse schemas must be designed from the outset to support actuarial reserve calculations rather than added as a later data engineering workstream.

Fraud Detection

Real-time fraud scoring on FNOL using ML models trained on historical claims patterns. Identity verification at application stage (Jumio, Onfido, Veriff) for high-risk product lines. Network analysis for organised fraud rings requires graph database capabilities where portfolio scale justifies the infrastructure investment. Device fingerprinting and behavioural analytics are effective complementary signals for application-stage fraud on digital distribution channels.

Regulatory Compliance in Insurance Platform Development

US State Regulation

Insurance in the United States is regulated at the state level. Product forms, rates, and policy language must be filed with and approved by each state's Department of Insurance before use. This has direct implications for product configuration architecture: state-specific rating rules, policy language, and coverage options must be manageable at a state level without full code deployments. SERFF (System for Electronic Rate and Form Filing) is the standard electronic filing mechanism in most US states. The platform must be able to generate SERFF-compatible form filings and maintain a record of approved forms by state and effective date.

UK: FCA, PRA, and Lloyd's

In the UK, insurers are dual-regulated by the FCA and PRA. Lloyd's of London operates under its own governance framework for syndicates and coverholders. Distribution requires FCA authorisation as an insurance intermediary or underwriter. The FCA's Consumer Duty (effective July 2023) requires insurers and distributors to demonstrate product and service value to customers, which has implications for product design, pricing architecture, and the outcomes monitoring data that the platform must produce. UK GDPR applies to all policyholder data processing.

GDPR and Sensitive Personal Data

Insurance platforms process sensitive personal data including health information (for life and health products), financial data, and motor vehicle records. The data processing architecture must document lawful bases for each processing activity, implement right-to-erasure workflows (complicated by policy record retention obligations under insurance regulations), and apply Article 9 protections for health data with explicit consent mechanisms integrated into the application journey.

Solvency II and IFRS 17

For platforms supporting carrier reporting obligations: Solvency II in the EU and its UK equivalent require specific actuarial and Pillar 3 reporting data to be available in structured form. IFRS 17 (effective January 2023) changed insurance contract accounting standards globally; platforms generating data for IFRS 17 calculations must produce the correct event granularity to support contract boundary determination, coverage unit calculations, and the contractual service margin allocation that actuaries require.

Why Work With Scrums.com for Insurance Software Development

Insurance technology sits at the intersection of actuarial complexity, regulatory compliance, and legacy infrastructure. Scrums.com provides dedicated engineering teams with hands-on experience across PAS modernisation, claims automation, and distribution platform builds.

Our teams understand the constraints carrier and MGA engineering leaders face: lengthy vendor procurement cycles, compliance sign-off requirements, and the challenge of shipping new capability while maintaining legacy system stability. We work within those constraints rather than around them.

Engagements start with a focused discovery phase to map your existing systems, identify integration dependencies, and agree on a delivery roadmap your stakeholders can present to the board. From there, dedicated squads take ownership of their workstreams with full transparency into progress and blockers.

Clients across P&C, life, health, and specialty lines have used Scrums.com teams to ship quoting platforms, claims portals, agent management systems, and telematics data pipelines on timelines their internal teams could not match alone.

Ready to discuss your insurance platform build or modernisation? Start a conversation and we'll scope it with you.

Frequently Asked Questions

How do you integrate with a legacy policy administration system?

We start by mapping the PAS API surface: what events the PAS emits, what operations it supports via API vs. file-based integration, and what the latency and reliability characteristics of each are. From that mapping, we design a canonical domain model and integration layer in the new platform that isolates the PAS-specific implementation details. This means the digital experience is not tightly coupled to PAS version upgrades, and the integration layer can be tested independently with contract tests against the PAS API. For platforms where the PAS has limited API capability, we have built event-based integration using database change streams and batch file exchange as reliable intermediate approaches.

Can you build a real-time quoting engine for a new MGA?

Yes. A quoting engine for a new MGA typically requires: a configurable rating rule service, integration with third-party data enrichment APIs (MVR, credit, geodata), a product configuration layer that business users can operate without code deployments, and an API that the distribution channel (direct web, broker portal, comparison aggregator) can call with sub-second response time expectations. We design the rating engine architecture before beginning development to confirm that the data enrichment latency budget is compatible with the response time requirement, as third-party API latency is often the binding constraint.

How do you handle US state-by-state regulatory requirements in the platform architecture?

State-specific requirements (rates, forms, coverage options, exclusions) are implemented as configuration data rather than code, managed through a product configuration layer with version control and approval workflows. This means adding a new state or updating an existing one is a configuration change with audit trail, not a code deployment. The platform maintains a record of the approved form version effective in each state, which is the evidence base for regulatory examinations. SERFF filing export is built into the form management workflow.

What is your approach to straight-through processing for claims?

Straight-through processing (STP) requires a scoring model that assesses each FNOL against a set of complexity and fraud signals and routes qualifying claims to automated settlement without adjuster review. The STP model must be designed with the compliance and fraud team, not just engineering, because the settlement authority rules and fraud tolerance thresholds are business and regulatory decisions. We build the FNOL intake and triage routing infrastructure, integrate the fraud scoring model, and implement the payment disbursement flow for settled claims, with a manual review fallback for claims outside the STP criteria.

What engagement model do you use for insurance platform projects?

We deploy dedicated teams assigned exclusively to your project for its duration: typically a technical lead, two or three senior engineers, and a QA engineer. For insurance platform work, we recommend a discovery phase before the first build sprint to map the PAS integration surface, define the canonical domain model, and agree the regulatory compliance architecture. This prevents the most common failure mode in insurance platform projects, which is discovering PAS integration constraints mid-build that require architectural rework. Engagements start within 21 days of contract signature. See our dedicated team model for details.