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. Illustrative of the pattern, not published client references.
An insurer models claimants, representatives, repair shops, and addresses as a connected knowledge graph. Fraud rings that stay invisible in tabular data show up as linked networks, ranked for an investigator to review rather than discovered claim by claim.
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 data you already hold, straight from the claims, policy, and investigation systems you already run. Nothing is replaced. Your teams keep working where they work today.
A scoped 8–12 week paid pilot structures your first decision context, say the network behind a suspected fraud ring, measured against success criteria you set. Your domain experts contribute the knowledge; we do the engineering. If it proves value, you expand from there. If it doesn’t, it doesn’t scale.
You query the connected layer in plain language, and answers come in seconds, accurate and traceable, with citations back to the source so you can check them yourself.
Curious what disconnected data may be costing your organization? The free Data Silo Cost Calculator puts a number on it in about two minutes, with no signup to see the result.
Try the Data Silo Cost Calculator →For a single document with a single answer, plain search or an LLM pointed at your PDFs does the job well, and a knowledge graph would be overkill. The difference shows up on the questions one document can’t answer: which claims connect through a shared address, vehicle, provider, or phone number that no single file reveals, so a fraud ring surfaces before it pays out. Those answers live in the relationships across your claims, policy, and investigation systems and your adjuster notes, medical reports, and third-party records, not inside any one of them. A knowledge graph connects the structured systems and the documents into one model, follows the chain across them, and returns each answer with a citation back to the record it came from. Something you can act on, and defend.
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. No deck, no pitch.
The same connected-data approach, applied across sectors. Explore another industry.