Introduction 

Industry fraud intelligence refers to the structured collection, analysis, and sharing of fraud-related information across multiple insurers within a defined framework. Unlike data sharing alone, intelligence implies value-added analysis: identifying patterns, networks, and emerging risks that no single insurer could see in isolation. 

Australia’s industry fraud intelligence framework is currently in a period of meaningful change, with the Insurance Council of Australia developing new capabilities to strengthen the industry’s collective defence. 

What Industry Fraud Intelligence Means (Plain English) 

Industry fraud intelligence is the work of turning raw fraud data into actionable insight at the industry level. Where data sharing simply allows insurers to look up records, intelligence adds analysis: spotting trends, identifying networks, and surfacing emerging threats. 

Typical activities include: 

• Analysing reported fraud data for cross-insurer patterns 

• Identifying organised networks that span multiple companies 

• Producing typology reports on emerging fraud methods 

• Supporting law enforcement with intelligence packages 

• Coordinating industry response to emerging threats 

The Current State in Australia 

Industry fraud intelligence in Australia is presently coordinated through the Insurance Fraud Bureau of Australia (IFBA), the working arm of the Insurance Council of Australia. The IFBA’s mandate explicitly covers information collection, sharing, and analysis of insurance fraud information that facilitates insurer action against fraud and supports law enforcement investigations.^² 

In addition to industry-level intelligence, state-based regulators provide their own fraud focus. SIRA in NSW analyses industry data and reports suspicious trends to CTP Green Slip insurers. MAIC in Queensland supports fraud prosecutions where insurers identify sufficient evidence of misconduct under the Motor Accident Insurance Act 1994.^⁴,⁵ 

The Industry-Wide Solution in Development 

In March 2024, the Insurance Council of Australia announced its intention to develop an industry-wide fraud detection and prevention solution. This followed the wind-up of the long-standing Insurance Reference Services (IRS) claims database, and reflects the ICA’s view that a modern and sophisticated fraud detection service is required for the current threat environment.^¹,³ 

The ICA has indicated that the new capability will draw on international experience, particularly the United Kingdom’s industry-funded Insurance Fraud Register, which is operated by the UK Insurance Fraud Bureau and provides industry-wide visibility of confirmed insurance fraudsters. 

How Insurers Use Industry Intelligence 

Insurers integrate industry fraud intelligence into their operations in several ways: 

• As part of automated screening during application or claim, with elevated risk cases routed for additional review 

• Within investigations into suspected fraud, to identify prior connections or known patterns 

• As input to detection model design, ensuring detection logic reflects current fraud methods 

• To inform internal training and awareness for frontline claims and underwriting staff 

Why Industry Intelligence Matters 

Organised fraudsters typically operate across multiple insurers, relying on the limited view of any single company. Industry intelligence closes this gap by allowing patterns to be identified across the wider market. This is particularly important for: 

• Organised crash for cash and staged accident networks 

• Identity-based fraud spanning multiple policies 

• Ghost broking and unauthorised intermediation 

• Supplier-led fraud touching multiple insurers 

The Importance of Quality Data 

Industry intelligence is only as good as the data feeding into it. Insurers that invest in strong entity resolution, robust internal fraud detection, and disciplined case management contribute substantially more value to industry intelligence than those operating less mature programmes. 

This creates a virtuous circle: better internal capability produces better contributions to industry intelligence, which in turn produces better intelligence for every participating insurer. 

Role in a Layered Detection Strategy 

Industry fraud intelligence is one component of a layered detection strategy. It is most effective when combined with internal detection analytics, entity resolution, and operational frameworks aligned to the General Insurance Code of Practice. No single source provides a complete view, and the strongest detection programmes integrate multiple inputs. 

As Australia’s industry-wide capability continues to develop, insurers that build strong internal foundations now will be best positioned to benefit from emerging industry intelligence as it becomes available. 

Related Topics 

Insurance Fraud Bureau of Australia (IFBA) 

Industry fraud data sharing in Australia 

Organised fraud in insurance 

Application fraud 

Sources & further reading 

^¹ Insurance Council of Australia — public statements on industry fraud detection strategy 

^² Insurance Fraud Bureau of Australia — mandate documentation 

^³ Insurance News (insuranceNEWS.com.au) — March 2024 reporting on industry-wide solution development 

^⁴ NSW State Insurance Regulatory Authority — CTP fraud guidance 

^⁵ Motor Accident Insurance Commission (Qld) — CTP fraud framework 

Introduction 

Industry-level data sharing is a foundational capability in the fight against organised insurance fraud. By allowing insurers to identify when the same individuals, vehicles, or patterns appear across multiple companies, shared data exposes fraud that would otherwise remain invisible to any single insurer. 

Australia’s industry fraud data sharing arrangements have undergone significant change in recent years and are continuing to evolve as the Insurance Council of Australia develops new capabilities for the industry. 

What Industry Fraud Data Sharing Means (Plain English) 

Industry fraud data sharing is the practice of allowing insurers to exchange information about suspicious or confirmed fraud activity, so that fraudsters cannot simply move between insurers committing the same fraud repeatedly. Without it, each insurer sees only their own fragment of the picture. 

Data sharing typically covers: 

• Confirmed fraud cases referred by member insurers 

• Suspicious activity warranting cross-industry awareness 

• Historical claims information used to identify patterns 

• Intelligence supporting law enforcement investigations 

The Historical Context: Insurance Reference Services (IRS) 

From 1991 until early 2024, the Insurance Reference Services (IRS) operated as Australia’s primary industry-level claims database. IRS allowed insurers to share motor, home, and travel claims information for the purposes of supporting claims management, claims investigation, loss assessment, fraud detection, and risk underwriting.^¹ 

At its peak, the IRS database held more than 22 million de-duplicated claims, with approximately 600,000 claims updates received monthly from member insurers. However, as participation declined over the years, the remaining IRS members made the decision to wind up the operation as the most appropriate course of action.^¹,⁴ 

The Current Industry Framework 

Following the wind-up of the IRS, industry fraud data sharing in Australia is currently coordinated primarily through the Insurance Fraud Bureau of Australia (IFBA), a working element of the Insurance Council of Australia. The IFBA’s mandate explicitly includes information collection, sharing, and analysis of insurance fraud information that facilitates insurer action against fraud.^³ 

The IFBA also coordinates information exchange between insurers where insurance fraud or a criminal act is reasonably believed to have occurred, and supports law enforcement investigations where insurance fraud may be a factor.^³ 

The Emerging Industry-Wide Solution 

In 2024, the ICA announced its intention to develop an industry-wide fraud detection and prevention solution. According to ICA statements, this will involve the establishment of a dedicated entity focused on the detection of systemic and organised insurance fraud across the Australian landscape.^²,⁴ 

The ICA has indicated that the new capability will draw on overseas experience — including the United Kingdom’s Insurance Fraud Register operated by the UK Insurance Fraud Bureau — to inform the design. The objective is a modern, sophisticated, industry-wide fraud detection service appropriate for the growing risk environment. 

Why Data Sharing Matters 

Organised fraud relies on the limited view of any single insurer. The same network may submit claims across multiple insurers using slightly different identities, vehicles, or addresses. Without industry-level data sharing: 

• Repeat fraud is much harder to identify 

• Investigations rely on incomplete information 

• Networks remain invisible until significant losses have accumulated 

• Honest customers bear the cost through higher premiums 

Privacy and Fairness Considerations 

Industry data sharing operates within Australia’s privacy framework, including the Privacy Act 1988 and the Australian Privacy Principles. Any information sharing must have a clear legal basis, support a legitimate purpose, and respect the rights of individuals.^⁵ 

This is particularly important given the consequences for individuals where they are identified within fraud data. Records must be based on sufficient evidence, processes must allow for challenge and correction, and access must be controlled appropriately. 

Role of Analytics and Entity Resolution 

Data sharing is only as valuable as the quality of the data being shared and the ability to resolve entities accurately across insurers. Strong entity resolution — the ability to identify when different records refer to the same real-world individual or business — is essential to making industry data work. 

Where insurers invest in entity resolution and detection analytics at their own level, the value of industry data sharing increases substantially. 

Related Topics 

Insurance Fraud Bureau of Australia (IFBA) 

Organised fraud in insurance 

Application fraud 

Entity resolution and risk visibility 

Sources & further reading 

^¹ Insurance Reference Services Limited (legacy) — insurancereferenceservices.com.au 

^² Insurance Council of Australia — statements on IRS wind-up and new fraud detection initiative 

^³ Insurance Fraud Bureau of Australia — insurancecouncil.com.au/consumers/insurance-fraud 

^⁴ Insurance News (insuranceNEWS.com.au) — March 2024 reporting on the industry-wide solution 

^⁵ Privacy Act 1988 (Cth) and Australian Privacy Principles 

Introduction 

The Insurance Fraud Bureau of Australia, commonly known as the IFBA, is the working element of the Insurance Council of Australia (ICA) dedicated to combating insurance fraud in all its forms. Established in December 2010 by ICA members, the IFBA exists to coordinate information sharing, support investigations, and reduce the impact of insurance fraud on honest policyholders.^¹,² 

The IFBA plays a central role in the Australian insurance industry’s collective response to fraud, working closely with police, regulators, and individual insurers. 

What the IFBA Does (Plain English) 

The IFBA is the industry body that helps connect the dots between fraud activity at different Australian insurers. Where a single insurer may see only one suspicious claim, the IFBA can support intelligence and information exchange across the wider industry. 

Its core activities include:^¹ 

• Operating a hotline for community members to report suspected insurance fraud (1800 600 444) 

• Providing a law enforcement inquiry service to facilitate police investigations where insurance fraud may be a factor 

• Coordinating information exchange between insurers where insurance fraud or a criminal act is reasonably believed to have occurred 

• Participating in community and government forums focused on crime prevention 

• Supporting prosecutions in partnership with state and federal police 

Why the IFBA Matters 

Organised insurance fraud is, by definition, rarely confined to a single insurer. The same network may submit claims across many different insurers, using slightly different identities, vehicles, or addresses to avoid detection. Without a coordinating body, each insurer would see only a fragment of the picture. 

The IFBA provides industry-level coordination that makes effective disruption possible. The IFBA has estimated that insurance fraud in Australia costs more than $2 billion annually — a cost ultimately reflected in premiums paid by honest policyholders.^¹ 

How the IFBA Fits with State Regulators 

The IFBA operates alongside state-based regulators that have their own fraud focus. For example, the NSW State Insurance Regulatory Authority (SIRA) operates a multi-agency taskforce dedicated to investigating CTP fraud in NSW, with a CTP Insurance Fraud hotline established jointly with the IFBA for public reports.^³ 

Similar relationships exist with other state regulators, including the Motor Accident Insurance Commission (MAIC) in Queensland. These partnerships allow the IFBA to support both industry-led and scheme-specific fraud response.^⁴ 

The Evolving Industry Landscape 

In 2024, the ICA announced that it is expanding the capability of the IFBA as part of the development of an industry-wide solution, with the establishment of a dedicated entity focused on the detection of systemic and organised insurance fraud across the Australian landscape.^⁵ 

This followed the wind-up of the Insurance Reference Services (IRS) claims database in early 2024. The ICA has indicated that the new industry-wide fraud detection capability will draw on overseas experience, including the United Kingdom’s Insurance Fraud Register, to strengthen collective defence against organised fraud.^⁵ 

Working with the IFBA 

Insurers contribute to and benefit from IFBA intelligence in several ways. Confirmed fraud cases can be referred to IFBA intelligence teams. Suspicious patterns can be shared for cross-insurer analysis. And in cases of confirmed organised fraud, the IFBA supports investigation and prosecution through its relationships with state and federal police. 

Effective participation requires clear internal processes for identifying, validating, and referring cases — processes that themselves benefit from strong fraud detection capability at individual insurer level. 

Role of Analytics and Industry Collaboration 

The IFBA’s effectiveness depends on the quality of intelligence it receives from its members. Where insurers operate sophisticated detection and entity resolution, the data they share with the IFBA is more valuable, and the industry-level intelligence in return is correspondingly stronger. 

This creates a positive feedback loop in which investment in detection at individual insurer level strengthens collective defence across the industry. 

Related Topics 

Insurance Council of Australia and the General Insurance Code of Practice 

Organised fraud in insurance 

Network analysis in insurance fraud 

Insurance fraud investigation 

Sources & further reading 

^¹ Insurance Fraud Bureau of Australia — insurancecouncil.com.au/consumers/insurance-fraud 

^² Insurance Council of Australia — IFBA establishment and mandate documentation 

^³ NSW State Insurance Regulatory Authority — CTP Insurance Fraud collaboration 

^⁴ Motor Accident Insurance Commission (Qld) — fraud reporting framework 

^⁵ Insurance News (insuranceNEWS.com.au) — March 2024 reporting on IFBA expansion and IRS wind-up 

Introduction 

A Special Investigations Unit, almost always referred to as an SIU, is the specialist team within an insurer responsible for investigating suspected fraud. SIUs sit at the heart of an insurer’s counter-fraud capability, combining specialist skills, dedicated tooling, and close working relationships with claims, underwriting, and external partners. 

The structure, scope, and reporting lines of SIUs vary between insurers, but their purpose is consistent: to investigate suspicious activity, gather evidence, and support decision-making on cases where fraud is suspected. 

What an SIU Does (Plain English) 

An SIU is the team that picks up cases where standard claims handling identifies potential fraud. Where a claims handler can flag a concern, the SIU investigates it in depth. 

Typical SIU responsibilities include: 

• Reviewing claims flagged for potential fraud 

• Conducting interviews with claimants, witnesses, and suppliers 

• Gathering evidence including documentation, telematics, and external data 

• Working with external investigators, forensic specialists, and law enforcement where appropriate 

• Supporting recovery action and prosecution where evidence warrants 

How SIUs Operate Under the Code of Practice 

Part 15 of the General Insurance Code of Practice 2020 establishes specific standards for claims investigations in Australia. These include requirements for:^¹ 

• Investigation activity being proportionate to the risk 

• Investigators being properly trained and qualified 

• Customers being kept informed during investigation 

• Surveillance only being used where alternatives have been considered 

• Investigators not making threats, promises, or inducements 

• Authority being obtained before fraud is alleged 

These standards shape how Australian SIUs operate, both internally and through external investigators they appoint. The Code Governance Committee oversees compliance with Code obligations.^⁴ 

How SIUs Are Structured 

SIU structures vary depending on the size and complexity of the insurer. Larger Australian insurers may have separate teams for motor, CTP, casualty, property, and organised fraud. Smaller insurers may operate a single team that covers all lines of business. 

Effective SIUs typically include a mix of skills, including former police officers, claims specialists, analysts, and digital forensics expertise. The Australian and New Zealand Institute of Insurance and Finance (ANZIIF) offers specialist training on claims investigation standards under the Code.^² 

How SIUs Interact with Other Teams 

SIUs are most effective when they are embedded in the wider claims operation rather than treated as a separate function. Claims handlers refer cases in, the SIU investigates, and the outcome feeds back into both individual claim decisions and broader detection improvements. 

Strong relationships with the IFBA, state regulators (such as SIRA in NSW), and law enforcement are also important. Fraud rarely sits in a single business area, and effective response often requires coordinated action.^³ 

Challenges Facing SIUs 

Modern SIUs face several recurring challenges: 

• Volume of referrals can exceed investigation capacity, requiring careful prioritisation 

• Fraud tactics evolve faster than traditional investigation methods alone can keep pace with 

• Evidence gathering is increasingly digital, requiring specialist tools and skills 

• Cross-border and organised fraud requires collaboration beyond a single insurer 

Role of Analytics and Workflow 

Modern SIUs rely heavily on analytics and structured workflow tools. Analytics surface cases that warrant attention and provide investigators with the context they need to act quickly. Workflow tools ensure that cases move efficiently through investigation, decision, and outcome. 

Where SIUs operate with integrated detection, investigation, and case management, productivity and accuracy both improve significantly. 

Related Topics 

Claims fraud: detection, investigation, and prevention 

Insurance fraud investigation 

Workflow management in fraud and compliance 

Entity resolution and risk visibility 

Sources & further reading 

^¹ Insurance Council of Australia — General Insurance Code of Practice 2020, Part 15 (Claims investigation standards) 

^² Australian and New Zealand Institute of Insurance and Finance (ANZIIF) — Claims Investigation Short Course 

^³ Insurance Fraud Bureau of Australia — insurancecouncil.com.au/consumers/insurance-fraud 

^⁴ Code Governance Committee — insurancecode.org.au 

Introduction 

Premium leakage is the underwriting equivalent of claims leakage. It refers to premium income lost because the risk being insured is not accurately reflected in the price charged. Where claims leakage represents money paid that should not have been, premium leakage represents money that should have been collected but was not. 

In a competitive market, premium leakage can have a significant impact on combined ratios and overall portfolio performance. 

What Premium Leakage Means (Plain English) 

Premium leakage happens when the rating factors used to price a policy do not match the true risk being insured. The customer pays less than the risk warrants, leaving the insurer exposed. 

Common causes include: 

• Inaccurate or incomplete declarations by customers 

• Misstated rating factors such as address, occupation, or vehicle use 

• Undeclared additional drivers or household members 

• Outdated information that has not been refreshed at renewal 

• Deliberate misrepresentation, including fronting and application fraud 

Why Premium Leakage Matters 

Premium leakage affects insurers in two distinct ways. First, individual policies are underpriced, leading to inadequate premium collection for the risk taken on. Second, the wider rating book is distorted, making accurate pricing harder across the portfolio. 

Over time, sustained premium leakage erodes underwriting performance even when claims handling is well managed. APRA’s prudential standards require regulated insurers to maintain adequate pricing discipline as part of their broader risk management obligations.^¹ 

The Link Between Premium Leakage and Fraud 

Not all premium leakage is fraudulent. Many cases involve customers providing inaccurate information without intent to deceive. However, deliberate misrepresentation — including organised application fraud — is a major contributor to premium leakage in some lines of business. 

This is why detection at policy inception, combined with periodic data validation, plays an important role in closing the leakage gap. Under the Insurance Contracts Act 1984, applicants have a duty to take reasonable care not to make a misrepresentation when applying for cover.^² 

Detection Signals to Consider 

Indicators of potential premium leakage include: 

• Mismatches between declared and externally validated information 

• Patterns of policies sharing identifiers or addresses 

• Inconsistencies between rating factors and historical claims activity 

• Unusual application patterns suggesting rating arbitrage 

• Discrepancies between policy details and the actual use of the insured asset 

Role of Real-Time Validation 

Effective premium leakage reduction relies on validating customer-provided information against external data sources at the point of application or renewal. Where discrepancies are identified, insurers can either correct the rating factors with the customer’s agreement or decline the application where the risk is materially different from that declared. 

Embedding validation into the application workflow ensures that controls operate at scale without unduly slowing genuine applications. 

Related Topics 

Application fraud 

Fronting in motor insurance 

Policy fraud detection 

Risk scoring and prioritisation 

Sources & further reading 

^¹ APRA — general insurance prudential framework 

^² Insurance Contracts Act 1984 (Cth) — duty of disclosure provisions 

^³ KPMG Australia — General Insurance Insights 2025 

^⁴ Insurance Council of Australia — General Insurance Code of Practice 2020 

Introduction 

Claims leakage describes the gap between what an insurer pays on a claim and what should have been paid under the policy. Unlike fraud, claims leakage is not necessarily the result of deception. It can arise from process inefficiencies, missed opportunities, or inconsistent decisions, and it represents one of the largest controllable cost areas in claims operations. 

Reducing claims leakage is a priority for most insurers, but the work requires a clear view of where leakage originates. 

What Claims Leakage Means (Plain English) 

Claims leakage is money paid out on a claim that did not need to be paid, or that could reasonably have been avoided through better information, controls, or processes. 

Common causes include: 

• Failure to identify fraud or exaggeration 

• Missed recovery opportunities, such as subrogation 

• Inconsistent application of policy terms or excesses 

• Overpayment of supplier or repair costs 

• Incomplete validation of claimed losses 

Why Claims Leakage Is Hard to Measure 

Claims leakage is rarely visible in a single transaction. Each individual decision may look reasonable, but small overpayments and missed opportunities accumulate across thousands of claims, adding up to material losses across the portfolio. 

Measuring leakage typically involves sampling claims, applying a structured review framework, and comparing what was paid against what should have been paid under a robust handling standard. 

The Relationship Between Leakage and Fraud 

Fraud is one source of claims leakage, but it is not the only source. A claim may be entirely genuine and still leak value through poor handling. Equally, leakage controls that focus solely on fraud will miss the wider operational issues that contribute to losses. 

This is why mature claims operations treat leakage and fraud as related but distinct disciplines, supported by overlapping data and analytics. 

Common Leakage Drivers 

Typical drivers of claims leakage include: 

• Inconsistent decisions across handlers or offices 

• Insufficient time spent on validation of higher-value claims 

• Limited use of data to challenge supplier pricing 

• Weak processes for identifying subrogation, salvage, or recovery opportunities 

• Late identification of fraud, after costs have already been incurred 

Reducing Claims Leakage 

Effective leakage reduction relies on a combination of data, process, and culture. Insurers that materially reduce leakage typically invest in: 

• Consistent handling standards supported by clear workflows 

• Data-driven validation of claimed values and supplier pricing 

• Early fraud detection at FNOL and through the claim lifecycle 

• Feedback loops from leakage reviews back into training and process design 

Role of Analytics and Workflow 

Analytics support leakage reduction by surfacing patterns that human review alone would miss. Comparisons across similar claims, suppliers, and handlers help identify where standards are slipping and where additional controls are warranted. 

Under APRA’s CPS 230 operational risk framework (effective 1 July 2025), regulated insurers are required to identify, assess, and manage material operational risks — a category that explicitly includes risks from process failures contributing to losses. Effective leakage management aligns directly with this expectation.^¹ 

Related Topics 

Claims fraud: detection, investigation, and prevention 

Exaggerated claims 

Risk scoring and prioritisation 

Workflow management in fraud and compliance 

Sources & further reading 

^¹ APRA — Prudential Standard CPS 230 Operational Risk Management (effective 1 July 2025) 

^² KPMG Australia — General Insurance Insights 2025 

^³ Insurance Council of Australia — General Insurance Code of Practice 2020 

^⁴ Australian Prudential Regulation Authority — general insurance institution-level statistics 

Introduction 

First Notification of Loss, almost always abbreviated to FNOL, is the moment at which a customer first reports a claim to their insurer. It is the entry point of the claims lifecycle, and it shapes every subsequent decision about how that claim is handled. 

FNOL has become a critical focal point for fraud detection. Decisions made at FNOL — including which handling pathway a claim follows and which controls are applied — influence the cost, speed, and accuracy of the entire claims journey. 

What FNOL Means (Plain English) 

FNOL is simply the first time a claim is reported. The customer makes contact — by phone, online, or through a digital channel — and provides the initial details of what has happened. 

At this point, the insurer captures: 

• The nature of the incident 

• Date, time, and location 

• Parties involved 

• Initial estimate of loss or injury 

• Any supporting evidence already available 

Why FNOL Matters for Fraud Detection 

FNOL is one of the most valuable points in the claims lifecycle for fraud detection because it is the earliest opportunity to assess risk before significant costs are incurred. 

Risk assessment at FNOL allows insurers to: 

• Route higher-risk claims to specialist handlers or investigators 

• Apply additional validation steps before payments are made 

• Avoid unnecessary friction for low-risk claims that can be progressed quickly 

• Capture supporting evidence while it is still fresh 

Balancing Speed and Scrutiny 

FNOL is also a critical moment for customer experience. Genuine claimants are often distressed, particularly following an injury or significant loss. Excessive scrutiny at FNOL risks alienating honest customers and slowing legitimate claims. 

The General Insurance Code of Practice 2020 sets out specific timeframes for insurers to respond to claims and progress them through defined stages. Effective FNOL processes therefore aim to apply proportionate controls within Code expectations.^¹ 

Detection Signals at FNOL 

Signals available at FNOL that may indicate elevated fraud risk include: 

• Inconsistencies between reported events and external data 

• Links to entities of interest, including previously flagged individuals or networks 

• Geographic or temporal clustering with other recent claims 

• Unusual reporting patterns, such as delayed notification combined with detailed claim heads 

• Mismatches between policy details and the reported circumstances 

Role of Analytics at FNOL 

Modern FNOL detection combines real-time scoring with structured rule sets and entity resolution. By assessing each new claim against historical data and known networks within seconds, insurers can make better-informed decisions at the point of first contact. 

Where FNOL detection feeds directly into the wider case management workflow, the benefits compound: investigators receive earlier, richer context, and routine claims progress without unnecessary delay. Under APRA’s CPS 230 operational risk framework, regulated insurers are required to manage operational risks affecting critical business services such as claims handling.^² 

Related Topics 

Claims fraud: detection, investigation, and prevention 

Risk scoring and prioritisation 

Anomaly detection in insurance fraud 

Workflow management in fraud and compliance 

Sources & further reading 

^¹ Insurance Council of Australia — General Insurance Code of Practice 2020, Part 8 (Making a claim) 

^² APRA — Prudential Standard CPS 230 Operational Risk Management 

^³ Australian Financial Complaints Authority (AFCA) — guidance on claim handling timeframes 

^⁴ Insurance Fraud Bureau of Australia — industry fraud awareness materials 

Introduction 

Total loss fraud refers to a group of fraudulent activities that take advantage of the way insurers handle vehicles deemed beyond economical repair. While total loss claims are an everyday part of motor insurance, the points at which valuation, settlement, and salvage occur create specific opportunities for both opportunistic and organised fraud. 

Because total loss claims can involve significant sums, even small percentages of fraudulent activity can translate into material losses across a portfolio. 

What Total Loss Fraud Means (Plain English) 

Total loss fraud occurs when a customer, supplier, or organised network manipulates aspects of a total loss claim to obtain a financial benefit that is not justified by the genuine facts. 

Common forms include: 

• Inflating the pre-incident value of a vehicle in order to obtain a higher settlement 

• Submitting claims for vehicles that were already damaged or non-functional before the reported incident 

• Misrepresenting the condition, specification, or modifications of a vehicle 

• Manipulating salvage value or repurchase processes for financial gain 

How Total Loss Fraud Operates 

Opportunistic total loss fraud tends to involve individual customers inflating the value of a vehicle that has been genuinely written off. The vehicle may have had less generous service history, fewer modifications, or more pre-existing damage than declared. 

Organised total loss fraud typically involves networks of suppliers, often spanning recovery, storage, salvage, and repair providers. Where the same supplier relationships appear repeatedly across high-value total loss claims, investigation is warranted. 

Why Total Loss Fraud Is Difficult to Detect 

The detection challenge with total loss claims is twofold. First, the vehicle in question is often no longer available for inspection in its pre-incident state. Second, valuation involves judgement, and reasonable people can disagree about the value of a specific vehicle. 

This creates space for fraud to operate at the margins, particularly where supporting documentation is incomplete or where pre-incident condition is hard to verify. Australian valuation disputes are frequently reviewed by AFCA, which provides published guidance on its approach.^¹ 

Detection Signals to Consider 

Signals that may indicate potential total loss fraud include: 

• Pre-incident valuations significantly above market norms for the make, model, and age 

• Inconsistencies between declared modifications and historical records 

• Repeated involvement of the same suppliers across high-value claims 

• Patterns of total loss claims linked to particular customers, addresses, or networks 

• Discrepancies between claimed and verifiable service or ownership history 

Role of Supplier Oversight 

Because organised total loss fraud often involves supplier collusion, supplier management is a core part of effective detection. Monitoring supplier behaviour across claims — including pricing, declared work, and outcomes — helps identify relationships that warrant closer scrutiny. 

Where insurers can connect supplier patterns with claim-level signals and entity relationships, organised activity becomes substantially easier to surface. 

Related Topics 

Claims fraud: detection, investigation, and prevention 

Exaggerated claims 

Organised fraud in insurance 

Supplier management 

Sources & further reading 

^¹ Australian Financial Complaints Authority (AFCA) — guidance on motor vehicle valuation disputes 

^² Insurance Council of Australia — General Insurance Code of Practice 2020 

^³ Insurance Fraud Bureau of Australia — industry fraud trends 

^⁴ Insurance News (insuranceNEWS.com.au) — coverage of organised motor fraud prosecutions 

Introduction 

Exaggerated claims are among the most common forms of opportunistic insurance fraud. They occur when a genuine loss is inflated — in value, scope, or impact — beyond what actually happened. Unlike fabricated or staged claims, an exaggerated claim begins with a real event. 

Because the underlying incident is genuine, exaggerated claims sit in a particularly difficult area of fraud detection. The line between honest recollection, optimistic valuation, and deliberate inflation is not always clear-cut. 

What Exaggerated Claims Mean (Plain English) 

An exaggerated claim is a real claim that has been inflated. The customer experienced a genuine loss, but the claim they submit goes beyond what they actually lost. 

Examples include: 

• Adding items to a household theft claim that were not actually stolen 

• Overstating the value of damaged property 

• Inflating the severity or duration of an injury 

• Claiming for losses that are unrelated to the underlying event 

Why Exaggerated Claims Are Common 

Exaggerated claims are more common than fabricated ones for a simple reason: the underlying event provides a plausible basis for a claim. Customers may rationalise inflation as recovering past premiums, compensating for inconvenience, or making up for items they believe should have been covered. 

This makes exaggerated claims an everyday challenge for Australian claims teams, rather than an exceptional one. In NSW, exaggerated CTP claims are explicitly identified by SIRA as one of the most common forms of CTP fraud.^² 

The Detection Challenge 

Detecting exaggerated claims is more nuanced than detecting fabricated ones. Because the underlying loss is real, many of the traditional signals of fraud — such as missing documentation or unverifiable circumstances — do not apply. 

Effective detection typically relies on: 

• Validating claimed values against external market data 

• Comparing claim composition against similar historical claims 

• Identifying inconsistencies between the reported event and the claimed loss 

• Reviewing supplier relationships and pricing patterns 

Balancing Fairness and Control 

Exaggerated claims sit at the heart of the balance between fraud control and customer experience. Honest customers may legitimately overestimate the value of their possessions, or make small errors in good faith. Treating every inflated figure as fraud risks alienating genuine customers and damaging trust. 

The General Insurance Code of Practice 2020 sets minimum standards for fair claims handling, including requirements around investigation, communication, and customer treatment. Insurers that exceed these standards in their handling of suspected exaggeration tend to achieve better outcomes for both fraud control and customer experience.^¹ 

Role of Analytics and Explainability 

Modern claims fraud platforms combine value validation, network analysis, and behavioural signals to identify probable exaggeration without unnecessarily delaying genuine claims. Explainability is essential. Investigators, claims handlers, and customers should all be able to understand why a claim has been flagged for further review. 

Over time, feedback from confirmed and dismissed cases improves model accuracy, reducing both missed fraud and unnecessary friction for honest customers. 

Related Topics 

Claims fraud: detection, investigation, and prevention 

False positives and false negatives 

Risk scoring and prioritisation 

Judgement and explainability in insurance decisions 

Sources & further reading 

^¹ Insurance Council of Australia — General Insurance Code of Practice 2020, Parts 8 and 15 

^² NSW State Insurance Regulatory Authority — CTP insurance fraud guidance 

^³ Australian Financial Complaints Authority (AFCA) — approach to fraud allegations 

^⁴ Insurance Fraud Bureau of Australia — industry fraud trends 

Introduction 

Fronting is a specific form of application fraud that is particularly common in motor insurance. It occurs when a more experienced driver is named as the main driver of a vehicle when, in reality, the vehicle is principally driven by someone else — typically a younger, less experienced, or higher-risk driver. 

The purpose of fronting is to obtain a lower premium than the actual risk would justify. While it can seem like a minor misrepresentation, fronting is a recognised form of insurance fraud with significant consequences under Australian law. 

What Fronting Means (Plain English) 

Fronting happens when the person named on the policy as the main driver is not the person who actually drives the vehicle most of the time. This is often done within families to keep premiums affordable, but it remains a form of fraud regardless of intent. 

Typical scenarios include: 

• A parent named as main driver on a policy primarily used by their teenage child 

• An experienced driver named on a policy used mostly by a newly qualified driver 

• A vehicle’s main driver being misrepresented in order to access lower premiums for higher-risk groups 

Why Fronting Is Treated Seriously 

Fronting may appear to be a small misstatement, but the consequences are significant: 

• The policy is based on inaccurate rating information, meaning the premium does not reflect the true risk^¹ 

• In the event of a claim, the insurer may decline cover or void the policy under the Insurance Contracts Act 1984^¹ 

• The actual driver may be considered uninsured, with all the legal consequences that follow 

• Honest customers absorb the cost where fronting goes undetected 

Under the Insurance Contracts Act 1984, applicants have a duty to take reasonable care not to make a misrepresentation. Where fronting amounts to a fraudulent misrepresentation, the insurer may have grounds to avoid the policy entirely.^¹ 

Detection Signals to Consider 

Fronting can be identified through a range of signals, particularly where multiple data sources are combined. Indicators include: 

• Mismatch between the named main driver’s profile and the typical use of the vehicle 

• Vehicle type, value, or modifications that align poorly with the named main driver 

• Telematics or usage data suggesting the named driver is not the principal user 

• Claims patterns inconsistent with the stated main driver 

• Household relationships that suggest a higher-risk driver may be the actual user 

How Common Is Fronting? 

Direct Australian data on fronting is limited, but international research provides useful context. UK research from Aviva in late 2024 found that one in six young drivers openly admitted to being on a fronted policy, with the true figure likely higher. Australian insurers have flagged similar concerns, particularly as motor premiums have risen and cost-of-living pressures have intensified.^⁴ 

Some Australian insurers offer telematics-based products as an alternative, allowing newer drivers to access more affordable cover based on actual driving behaviour rather than risk proxies. 

Role of Analytics and Real-Time Detection 

Detecting fronting at scale relies on combining policy-level data, household data, and behavioural signals. Machine learning models trained on confirmed fronting cases can highlight high-probability applications for review without delaying low-risk applications. 

Where suspected fronting is identified, clear and explainable decision-making is essential. Customers should be able to understand the basis of any additional checks, and investigators should be able to demonstrate why a decision was reached — consistent with obligations under the General Insurance Code of Practice.^² 

Related Topics 

Application fraud 

Policy fraud detection 

Claims fraud: detection, investigation, and prevention 

Ghost broking 

Sources & further reading 

^¹ Insurance Contracts Act 1984 (Cth) — duty of disclosure provisions 

^² General Insurance Code of Practice 2020 — Insurance Council of Australia 

^³ Australian Securities and Investments Commission (ASIC) — motor insurance guidance 

^⁴ Aviva (UK) — young driver fronting research, December 2024, for international comparison