Graph analytics for detecting fraud in Indian insurance claims
Using graph analytics to detect fraud in Indian insurance claims starts with a simple observation: suspicious events often become persuasive only when viewed as connections. A policy, claimant, agent, surveyor, repairer, medical provider or bank account may look ordinary alone; together, they can expose duplication, collusion and coordinated inflation.
For Australian insurers, the lesson is relevant but not a transplant unchanged. India combines large motor and health books, public and private carriers, agents, aggregators, third-party administrators and investigators. Australia has different market structures, laws and fraud incentives, so relationship data must be interpreted through local repair networks, broker practices, privacy duties and complaint pathways. Graph evidence should combine with sound claim controls and human judgement.
Fraud as a network problem
Indian general insurers process thousands of claims across motor, health, property and travel every day. Fraud can be a fabricated loss, an inflated invoice, a duplicate reimbursement or a staged network that redirects claims to favoured agents, workshops or hospitals. Because each event enters through a policy, customer, intermediary or service provider, claim data naturally forms a graph of people, organisations, assets, documents and money.
Claim-by-claim rules can flag an unusual amount, yet miss a moderate claim whose importance lies in who else appears nearby. In a graph, the same mobile number, address, payment account, surveyor or repairer creates weighted links. Shared attributes become more revealing when claims cluster in time, recur across branches or connect through several intermediaries. Link analysis can therefore surface a coordinated ring even when no individual record breaches a threshold.
Network evidence also changes how a fraud team reasons. Rather than starting with a suspicious customer and searching manually, analysts can begin with a provider and map its claimants, referrers and banking relationships. Case methods and implementation lessons are also shared through the analytics community forum maintained by Passionate in Analytics, which is valuable for India-focused analytics practitioners.
Building a claims knowledge graph
The first design choice is what counts as an entity. A useful schema separates the policyholder, insured person, claimant, agent, broker, employee, surveyor, third-party administrator, repairer, medical provider, vehicle, property and account holder. It also preserves the claim, policy, medical document, estimate, invoice and payment as events or business objects. This avoids collapsing roles that may be connected but not equivalent.
Records arrive through call centres, branches, agents, aggregators, portals, hospital networks and workshops, often with spelling variations and duplicate customer records. Entity resolution converts those fragments into stable identities using policy numbers, verified contact details and carefully governed attributes. Matching should retain a confidence score and source history; an uncertain link is a prompt for review, never an automatic accusation.
Edges need equally rich context. A link can represent referral, employment, ownership, shared contact, payment, document submission or prior claim, with effective dates and confidence attached. Temporal weighting prevents an old connection from dominating a present case. Document extraction can add document fingerprints, prescriber details and invoice relationships, while the source document remains available for human verification.
Graph features can be combined with conventional signals such as amount, loss type, prior losses, police-report status and treatment plausibility. A dense neighbourhood may be normal for a busy hospital, while a sparse customer record can still be fraudulent. Baselines by product, geography and provider maturity supply that context.
Signals that reveal coordinated activity
Useful patterns fall into several families. Duplicate networks reuse identity elements or bank accounts across claims; steering networks repeatedly route claimants to a common agent or service provider; inflation networks share addresses, device data or invoice templates; and collusion networks connect otherwise separate claims through a surveyor, broker or employee. Timing can be decisive when documents arrive in an unnaturally similar sequence.
Graph analytics supports several complementary techniques. Degree and centrality identify unusually connected actors, community detection exposes dense clusters, and shortest paths reveal how a claimant reaches a beneficiary. Temporal motifs can detect repeated interaction sequences, while link prediction suggests missing relationships for investigation. Supervised models can learn risk from labelled cases, and graph embeddings can summarise complex neighbourhoods into features for claim-scoring systems.
Anomaly scores should not be confused with evidence. A central hospital or high-volume workshop is not fraudulent merely because many claims pass through it. Investigators need a concise reason for each alert, such as a newly shared account followed by payments to one intermediary. Comparing the suspicious path with normal customer journeys and independent medical or repair evidence keeps the analysis defensible.
From graph model to production
A practical architecture begins with event capture from policy administration, claims, payments and service providers. Cleaned events flow through entity resolution into a graph store, where features and relationship scores are updated as new information arrives. Real-time scoring can prioritise referrals and high-value notifications, while a nightly batch recomputes deeper communities and historical patterns. Analysts should also retain links to source records for audit.
Case management is the bridge between detection and action. An alert should display the relevant relationship path, dates, contributing attributes and comparable legitimate cases, rather than present an unexplained risk number. Investigators can request documents, dismiss weak links and record why. Those outcomes feed monitored training data, but human corrections should be reviewed to prevent a previous investigative bias from becoming a permanent rule.
Model complexity needs a measured design. Training several models at graph scale can be costly; computational cost of ensembles is a useful reminder to benchmark simpler models before adding complexity. Subgraph sampling, approximate community detection and tiered scoring can keep latency and infrastructure under control. The right design depends on alert volume and graph size, not on the novelty of an algorithm.
Monitoring should cover data and model drift. A new aggregator can create thousands of unfamiliar relationships, while changed identifiers may break old links. Track match rates, missing attributes, alert concentration, investigator agreement and adverse outcomes by product and location. Model owners, fraud operations, data engineering, privacy teams and investigators need shared service levels.
Indian market realities
India's claims ecosystem is fragmented by design and history. Motor policies may be sold through agents, brokers, bancassurance channels, aggregators and digital marketplaces, while health claims pass through insurers, third-party administrators, hospitals and cashless networks. Surveyors, investigators, workshops and adjusters may work across many insurers. Data-sharing agreements and consistent identifiers are therefore as important as the graph algorithm.
Geography and seasonality add context. Flood, monsoon and cyclone claims can concentrate activity in specific districts; urban medical networks can create legitimate clusters; and fraud may migrate between states. A model trained on national behaviour must distinguish unusual volume from abnormal relationships. Hindi, Tamil, Bengali and other local-language documents also require extraction and review processes that preserve their evidentiary value.
Regulatory design must cover governance of claims records, third-party access, customer notice and security. Insurers should align their practices with applicable IRDAI requirements and the evolving implementation of India's Digital Personal Data Protection Act. Access logs, purpose limitation, retention rules and auditable links reduce misuse of sensitive health, identity and financial information.
False positives carry a human cost. A legitimate claimant can be delayed by a network built from a shared workplace address, family plan or emergency repairer. High-risk decisions should support human review, provide an explanation and preserve an appeal path. Tiered alerts can focus specialist investigation on major cases.
Australian signals and controls
Australian insurers operate a different market. Sydney and Melbourne contain large insurer, broker, repair and claims ecosystems, while Brisbane's flood and storm exposure creates distinct event patterns. National insurers work with local assessors, tow operators and workshops; regional distances make inspection and independent evidence harder to obtain. These conditions can create referral incentives and repeated partner networks, but they should not be presumed fraudulent.
Market details matter when translating Indian practices. Australian general insurance moves through insurers, captives, brokers and aggregators, with referral relationships in motor claims handled under state and industry complaint arrangements, including the Australian Financial Complaints Authority. GST generally does not apply to most Australian general insurance premiums, unlike motor policies in India, so premium signals cannot transfer directly. Australia's Privacy Act and state regimes also govern customer-link collection and use.
Graph analytics should sit within Australian anti-fraud rules, medical and repair evidence checks, and AFCA review where an issue escalates. Transparent matching, access limits and contestability are essential as automated decision scrutiny increases.
Signals worth monitoring
- Claims that reuse payment, phone, address or device identifiers
- Synchronised document patterns across unrelated loss types
- Repeated referral paths through agents, assessors or repairers
- Medical or repair outcomes inconsistent with independent evidence
Controls to prioritise
- Explainable entity resolution with confidence thresholds
- Relationship-path alerts attached to investigation records
- Targeted monitoring of high-concentration service networks
- Fairness and appeal tests across regions and customer groups
Measuring value and choosing the right method
Pilot the approach in shadow mode against known cases before it affects customer outcomes. Measure precision, recall, avoided loss, investigation time, time to detection, referral rates, appeals and false-positive effects. Compare the graph signal with the existing score on the same claims. A modest lift can still be valuable if it directs investigators toward coherent cases rather than isolated anomalies.
Graph analytics, rules and conventional machine learning answer different questions. The comparison below clarifies where each belongs in a layered control environment; no single method covers every pattern.
| Approach |
Strength |
Common blind spot |
Best role |
| Rules |
Fast and explainable for known patterns |
Miss unfamiliar network behaviour |
First-line claim controls |
| Standalone machine learning |
Scores individual claim risk |
Can miss context split across entities |
Prioritise individual claim triage |
| Graph analytics |
Reveals relationships, clusters and paths |
Depends on data quality and entity resolution |
Network-level risk ranking and investigation |
| Manual investigation |
Applies context and challenge |
Slow, costly and inconsistent |
Validate alerts and resolve complex cases |
A focused pilot can isolate motor repair referrals or duplicate health reimbursements before wider use. The durable advantage is not a dramatic standalone score. It is the ability to connect evidence, expose hidden relationships and give fraud teams a practical path from suspicion to substantiation.
