Insurers have the data. The problem is it lives in silos - policy systems, claims platforms, and legacy warehouses that never talk to each other. Decision Foundry unifies it into a governed intelligence layer, so underwriters, claims teams, and executives can act on what is actually happening right now.
Policyholder data sits across multiple systems with no unified view of risk exposure or customer lifetime value
Underwriting decisions rely on manual data gathering that takes days and introduces inconsistency
Claims triage is reactive - high-severity cases are not identified until they are already expensive
Fraud detection is rules-based and catches only the patterns it was programmed to look for
Actuarial models are built on data that is weeks old by the time it reaches them
NAIC, IFRS 17, and other regulatory reporting is a quarterly manual scramble across disconnected systems
Legacy core systems resist modernization but the business cannot afford to stop while they are replaced
What We Deliver
We build the data foundation that makes underwriting faster, more consistent, and more accurate. Risk data from internal systems, third-party data providers, and external sources is unified and structured, so underwriters work from a complete, real-time risk picture — not a manually assembled snapshot.
We build claims analytics that surface high-severity cases early, predict likely outcomes, and recommend the right resolution path at intake. Claims teams spend less time on administration and more time on the cases that actually need human judgment.
Rules-based fraud detection catches known patterns. AI-powered detection learns new ones. We deploy machine learning models that identify anomalous behavior across claims, billing, and provider data flagging suspicious activity before payouts are made, not after.
We give actuarial teams a governed, unified data environment with the freshness and granularity they need to build reliable models. Actuaries spend less time sourcing and validating data and more time on the analysis that drives pricing, reserving, and capital decisions.
NAIC, IFRS 17, Solvency II, and state-specific compliance requirements involve significant manual data preparation. We automate the data pipelines and reporting workflows that feed these obligations — reducing preparation time from weeks to hours and eliminating the reconciliation errors that attract regulatory attention.
Policyholder retention, agent performance, cross-sell and upsell opportunities, customer lifetime value. We build the analytics layer that gives sales, marketing, and distribution teams the intelligence to act on.
Is your claims data giving you the visibility you need before costs escalate? We will assess your current data environment and show you where the biggest intelligence gaps are.
Book a Free Insurance Data AssessmentHow We Deliver It
We map your current data landscape - core systems, data sources, quality issues, and governance gaps. Insurance organizations typically have significant data in policy administration systems, claims platforms, and actuarial tools that has never been unified. We build a complete picture of what you have, what state it is in, and what needs to happen before intelligence can be built on top of it.
We design the target architecture - unified data model, governance framework, security model, and compliance controls. For insurance, this includes data lineage and auditability requirements that regulators increasingly expect.
We connect your policy, claims, actuarial, and third-party data sources into a unified platform. This is the foundational work that everything else depends on. We do not skip it.
We build the underwriting dashboards, claims triage models, fraud detection systems, and actuarial analytics that give your teams the intelligence they need. Each component is validated against your business requirements before go-live.
For insurers ready to go further, we deploy AI models for fraud detection, claims outcome prediction, risk scoring, and underwriting automation embedded into the workflows where decisions are made.
We automate the data pipelines and report generation for your compliance obligations - NAIC, IFRS 17, state filings, and internal actuarial reporting.
Post-launch, we monitor data quality, model performance, and system reliability and iterate continuously as your regulatory environment, product mix, and data landscape evolve.
AI-powered detection identifies anomalous patterns across claims and billing data before payouts are approved- catching fraud that rules-based systems miss entirely.
Predictive triage identifies high-severity and high-complexity claims at intake routing them to the right team immediately rather than days into the process.
Unified risk data and automated data gathering gives underwriters a complete picture in minutes rather than days accelerating quote-to-bind timelines without increasing risk.
Automated pipelines and governed reporting frameworks eliminate the manual effort and reconciliation risk from NAIC, IFRS 17, and state compliance obligations.
The AI Opportunity in Insurance
Insurance is one of the industries where AI delivers the highest measurable impact because decisions are made at volume, the cost of a wrong decision is quantifiable, and the data needed to improve those decisions already exists in most organizations.
The constraint is not the AI. It is the data foundation underneath it. Fraud detection models trained on inconsistent claims data give inconsistent results. Underwriting models built on incomplete risk data introduce new exposure rather than reducing it. Actuarial models fed with stale data produce reserves that do not reflect current reality.
Every AI engagement Decision Foundry delivers in insurance starts with the data foundation. We unify and govern the data first. Then we build the intelligence layer on top. This is not the fastest path to a demo. It is the only path to an AI deployment that delivers in production.
We understand the data shape of insurance - policy administration systems, claims platforms, actuarial models, and the regulatory reporting infrastructure that sits across all of them. We do not spend your project budget learning your industry.
Every engagement begins with unifying and governing your data before any analytics or AI is built on top of it. AI deployed on fragmented data does not deliver. We insist on getting this right.
NAIC, IFRS 17, Solvency II, state-specific filing requirements - we understand what regulators expect and build the data architecture and reporting infrastructure that meets those expectations from day one.
We have deployed fraud detection, claims triage, and underwriting models that operate reliably in production environments not just in demos. We know what it takes to move from pilot to production because we have done it.
Decision Foundry has been delivering data and analytics engagements for over two decades. In insurance, this means we have seen every version of the legacy system challenge, every iteration of the regulatory reporting problem, and every shape of the data fragmentation problem. We know what works.
Salesforce Select Partner, Snowflake Select Partner, Databricks Premier Partner, Tableau Premier Partner, Microsoft Solutions Partner for Data and AI, the platforms insurance organizations run on are platforms we have delivered at scale.
Whether you are starting with fraud detection, claims analytics, underwriting automation, or regulatory reporting, the right starting point is the same: a clear picture of your current data environment and the highest-impact opportunity to act on first.
Book a Free Insurance Data AssessmentQuestions, Answered
Yes. Most insurance organizations have legacy policy administration or claims systems that were not designed for modern integration. We have experience connecting to a wide range of legacy systems extracting data reliably without disrupting core operations and building the integration layer that allows modern analytics to run on top of legacy infrastructure.
Data governance, access controls, and security are foundational to every insurance engagement we deliver. We design the data architecture with field-level security, role-based access, and full audit trails from day one not as a retrofit. We also hold SOC 2 compliance and are experienced with GDPR, CCPA, and insurance-specific data residency requirements.
Yes. IFRS 17 requires granular contract-level data and sophisticated actuarial calculations that most insurers have had to rebuild their reporting infrastructure to support. We have delivered IFRS 17 data architecture and reporting automation engagements and understand the specific data model and calculation requirements the standard demands.
Rules-based systems detect the patterns they were explicitly programmed to detect. They are effective at catching known fraud schemes but blind to new ones. Machine learning models identify statistical anomalies across large datasets, flagging behavior that deviates from expected patterns even when that behavior does not match any known fraud scheme. The two approaches are complementary - rules-based for known patterns, ML for emerging ones.
No. We build the intelligence layer on top of your existing systems connecting them, unifying the data they contain, and delivering analytics and AI without requiring a core system replacement. Core system modernization is a separate decision that can happen independently of building your analytics and AI capability.
A focused engagement addressing one specific workload -fraud detection, claims triage, underwriting dashboards, or regulatory reporting automation typically takes 12 to 16 weeks. Enterprise-scale engagements covering multiple workstreams typically run 6 to 12 months.