Your claims, policy, and investigation systems each hold fragments of the truth. A coordinated fraud ring only appears when you connect claimants to their addresses, their legal representatives, their repair shops, and their prior claims — in a single graph. LangOptima builds that graph.
Your investigators are sharp. Your systems are sound. The problem is that fraud rings exist in the relationships between records — and relational systems were never designed to surface relationships.
Each claim is reviewed on its own merits. Connected-party analysis happens only when a suspicious pattern has already tripped a rule — and rule-based systems miss novel ring structures.
Investigating a suspicious claim means days of jumping between claims, policy, investigation, and external data systems to build a picture of the connected parties. Most investigators never get the full picture.
47 unlinked claims connected through shared addresses, representatives, and repair shops will never trigger a single-claim rule. Only a network view reveals them — and only if you have one.
LangOptima models your claimants, policies, legal representatives, repair shops, medical assessors, addresses, and claims as connected entities in a single knowledge graph. Your existing claims and investigation systems keep running. The graph is the layer that makes the hidden network visible.
Claims, policy, investigation, and external data unified in a semantic layer. Entity resolution bridges the inconsistent identifiers that let the same claimant appear as three different people.
Surface coordinated fraud rings, shared-address clusters, and representative patterns that no single-claim rule would ever catch. The network view is the insight.
Investigators open a suspicious claim and see the complete network on the first click. Hours of detective work collapse to seconds.
Your investigators stop building context and start making judgments. The knowledge graph does not replace their expertise — it compounds it.
Representative scenarios for insurers with your profile — ask us what applies to yours.
An insurer increased fraud detection rate by 40% in the first quarter by modeling claimants, representatives, repair shops, and addresses as a connected knowledge graph. A 47-claim fraud ring surfaced in week one — previously invisible in tabular data.
Industry estimates put rule-based AML screening at 90–95% false positives. Graph-based pattern detection complements existing rules by surfacing the relationships rules cannot see — while leaving the investigator in control.
However complex the data landscape underneath, the engagement itself stays simple.
We ingest the documents and data you already hold, straight from the systems you already run. Nothing is replaced — your teams keep working where they work today.
A scoped 8–12 week pilot structures your first decision context. Your domain experts contribute the knowledge; we do the engineering.
You start asking the questions — and every answer carries a citation back to the source, so you can check it yourself.
Start with a 30-minute conversation. Tell us about the line of business, the claim pattern, or the investigation that is eating the most time today — and we’ll show you how a knowledge graph would surface the network underneath.