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.
A claims and policy graph modeled in RDF, connecting claimants, providers, vehicles, and policies into one semantic web, so fraud rings and hidden exposure surface where table-based checks miss them.
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.
Modern AI can structure a great deal on its own. Where it stops is the meaning specific to your organization: the concepts, rules, and relationships that make your business yours, and where its real value lives. We structure that layer with you on open, world-standard semantics, not a proprietary schema, so the graph reflects how you operate and stays yours: no vendor lock-in, portable to whatever you run next. More on the structure beneath it →
You don't have to boil the ocean. Start with a single business context and prove it there. Once that foundation is laid properly, the same connected data tends to open opportunities in other departments, so the next team builds on the work already done rather than starting from zero.
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.
Pick one decision that matters, say the network behind a suspected fraud ring. We build the knowledge graph over the claims, policy, and investigation systems you already run. Nothing is replaced. Your teams keep working where they work today.
Your team's questions, reports, and AI run on that connected layer. Answers come in seconds, accurate and traceable, with citations back to the source so you can check them yourself.
A working result in a scoped 8–12 week paid pilot, measured against success criteria you set. If it proves value, you expand from there. If it doesn't, it doesn't scale.
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.
The same connected-data approach, applied across sectors. Explore another industry.