Let’s start with a contentious statement, rugby union is the ultimate team sport.
That is not to say that other teams sports are less athletic or don’t require excellent teamwork. Anyone who remembers the way Pep Guardiola’s Barcelona team created football poetry knows that’s not the case. The lens through which we’re looking at this is the way different types of athletes are woven together to make a team.
It’s said that there is a position for everyone in rugby. If you’re tall, you can go in the second row. Immensely strong and don’t mind mangling your ears you can be a prop. Good at darts, a hooker. Fast and strong, number 8. Missing a sense of fear and self-preservation, flanker. Finally, if you’re fast and have good hair, you can play in the backs. Each different type of player has a specific roll to play.
Fraud control in insurance is built the same way: success doesn’t come from a single superstar system or an inspired analyst working alone, it’s the orchestration of diverse capabilities, coordinated in real time, towards a common goal. Modern fraudsters iterate quickly, mix digital and physical tactics, and exploit seams between functions - underwriting vs. claims, first notification of loss (FNOL) vs. settlement, call centre vs. portal. To keep pace, insurers need a unified, continuously learning counter‑fraud “team” where each capability does its job and passes the ball at the right moment.
Below is some practical best practice for building that team, mapping rugby roles to counter fraud capabilities - and how to orchestrate them into one cohesive defence without slowing down honest customers.
The front row: Rules, scenarios, and governance (Solid base to secure possession)
Your front row anchors the line, set stability, and win clean ball. In fraud terms, these are your business rules and fraud scenarios - codified patterns for known modus operandi), regulatory must checks, and high confidence disqualifiers.
- High signal rules at underwriting and claims FNOL (e.g. ghost broking indicators, mismatched risk/quote details, duplicate datum across customers).
- Scenario bundles that chain conditions across events (e.g. same repairer + same device + new bank details + weekend claim submission).
Best practices:
- Keep this layer precise and interpretable - avoid bloated rule sets.
- Instrument with false positive cost and uplift metrics (e.g., additional fraud detected per 1,000 referrals).
- Be prepared to change at 60 minutes when new threats emerge and the scenarios need to adapt.
The locks: AI & machine learning (Power in tight channels)
AI provides the push where rules can’t. It excels at subtle interactions and nonlinear signals that might indicate first party opportunistic fraud, soft fraud inflation, or organised rings morphing tactics.
- Supervised models to highlight the propensity for fraud at key stages of the process, FNOL, adjustment and settlement.
- Natural Language Processing AI models that highlight the patterns of language associated with previous fraudulent activity
- Contributory AI models that collaborate and vote on possible fraudulent activity sharing their learning across insurers without sharing personal information
Make AI a good teammate
- Avoid the black box perception. Keep models explainable at the point of decision (reason codes, top features) to support handlers, Quality Assurance, and regulators.
- Promote model/rule interplay: models identify candidates for new rules; rules capture hard won insights that keep model complexity and drift in check.
- Set guardrails: bias checks, stability monitoring, champion/–challenger frameworks, and human in the loop review for adverse decisions.
The back row: Real time network analytics (Coverage across the park)
Fraud is social. Rings coordinate mule accounts, assets, addresses, phone numbers, repairers, and legal representatives. Graph/network analytics turns isolated alerts into case-level intelligence:
- Build an entity resolution layer (people, addresses, devices, emails, bank accounts, vehicles, properties, businesses etc) with deterministic and probabilistic matching.
- Maintain a real time knowledge graph of relationships and events; compute features like centrality, community membership, shared attributes, temporal proximity, and suspicious subgraphs (e.g., many claims funnelled to a small cluster of providers).
Key outcome: Fewer isolated alerts, more coherent case referrals that speed investigation and improve hit rates.
The half backs: Real time orchestration & decisioning (Game management)
Like scrum halves and fly halves dictate the tempo of the game, an orchestration layer routes each interaction through the right checks without slowing play:
- Event driven architecture: at FNOL/quote, call out to device, document, voice, and third-party services concurrently; fuse results in real-time.
- Risk Segmentation to Policy driven routing: low risk → straight through; medium risk → light friction (e.g., selfie liveness); high risk → refer/hard stop.
- Feedback loops: closed loop learning from investigation outcomes feeds rules, models, and network features.
- Design for customer experience: Apply graduated friction - honest customers see minimal hurdles, while risk weighted controls increase only when signals justify them.
The centres: Image & document fraud (Break the line)
Document and image manipulation remains a staple of opportunistic and organised fraud:
- Document forensics: detect tampering, edits, synthetic PDFs, metadata anomalies, and font/package inconsistencies.
- Image analytics: detect splices, re used images (perceptual hashing), Exchangeable image file format (EXIF) inconsistencies, synthetic content cues, and physical impossibilities (shadows, reflections).
- Cross document/entity checks: verify consistency with third party data (e.g., vehicle condition, property attributes, weather).
The differing techniques used to detect manipulation tend to have differing true positive rates, therefore should be deployed with other detection methods into one overall score.
The back three: Voice & contact centre intelligence (Last line of defence)
Your phones and chat lines are a goldmine:
- Voice analytics: detect synthetic/AI voice, playback patterns, and voice stress indicators (used judiciously).
- Caller/device reputation: correlate device ID, call velocity across accounts, and Interactive Voice Response behaviour.
This ensures alignment between contact centre operations with digital signals, closing a common gap exploited by social engineers and claim mills.
However, this will become increasingly challenging, as consumer communication preferences evolve.. Forcing consumers down specific channels could lead to genuine customer drop off.
The bench: Third party data (Specialists you bring on when it matters)
No team wins the game without impact subs. Blending multiple external data sources - with clear legal bases and robust vendor management - to sharpen signals and reduce friction:
Identity & Reputation
- Credit reference & identity verification data (e.g., name/date of birth/address match, thin file flags), to verify trust
- Email/phone intelligence: tenure, breach exposure, disposable domains, SIM swap, call risk scores.
- Device & IP intelligence: emulator/VM signals, TOR/proxy/VPN, device age
- Merchant & payments data: IBAN/merchant reputation, account age, mule clustering.
- Sanctions/Politically Exposed Person & adverse media screening
Assets & Context
- Vehicle: Vehicle Identification Number/number plate history, prior damage/salvage, mileage anomalies, market values.
- Property: ownership/tenure, valuation bands, construction type, renovation permits.
- Geospatial & weather: peril verification at time of loss (hail, flood, wind), catastrophe footprints.
- Business & professional registries: company status, beneficial ownership, licensure (e.g., repairers, medical providers).
Bringing your team together
In rugby, the magic isn’t just raw power or pace, it’s the timing of the pass, the shape of the run, the trust in the system. Countering insurance fraud is the same. You’ll need the stability of well governed rules, the power of AI, the field coverage of real time network analytics, the finishing quality of document and voice forensics, and a bench of specialist third party data. But the win comes from orchestration: a single playbook, shared metrics, and constant learning.
Get that right, and you’ll not only reduce fraud loss and Special Investigation Unit (SIU) backlog - you’ll improve honest customer experience, improve straight through decisions, and build an adaptive defence that keeps playing at test match level, no matter how the opposition changes its tactics.