Insurance fraud is not new. But its scale and sophistication are accelerating at a pace that traditional defences were never designed to handle.
In 2024, UK insurers detected £1.16 billion worth of fraudulent general insurance claims – a 2% increase on the previous year – with over 98,400 fraud-related claims uncovered, representing a 12% rise from 2023[1]. Motor insurance remains the most heavily targeted area, accounting for 53% of all detected fraud claims[2]. Organised fraud networks now operate across multiple policies, identities, and claims channels. Patterns are hidden across thousands of transactions, multiple systems, and years of historical data.
For many insurers, the question is no longer whether to invest in AI-driven fraud detection, but how quickly they can build the data foundations to support it.
Why traditional fraud detection is reaching its limits
Historically, insurers relied on claims investigators and predefined business rules to identify suspicious activity. Claims exceeding a certain value might trigger an investigation. Unusual claim frequency might flag a case for review. Suspicious patterns were often discovered through labour-intensive manual analysis.
While these methods still play a role, they struggle against the complexity of modern fraud schemes. Research from Adyen’s 2025 Insurance Report found that 53% of UK insurers acknowledge their legacy payment systems limit fraud detection capabilities, while 52% still rely heavily on manual claims processing[3]. Modern fraud networks exploit precisely this kind of fragmentation – claims data, policy information, customer records, and third-party data often exist across multiple systems that do not easily communicate with one another, making it extremely difficult for human investigators to identify patterns that span multiple datasets.
The AI advantage in fraud detection
Artificial intelligence changes this equation fundamentally.
AI systems can analyse vast volumes of claims data, customer records, and behavioural patterns simultaneously. Rather than reviewing claims individually, machine learning models can detect unusual claim behaviour, identify hidden relationships between claimants, repair providers, and policies, and highlight anomalies that may indicate coordinated fraud activity. Techniques such as text mining, anomaly detection, and network link analysis can score millions of claims in real time[4].
The results are already visible across the industry. Aviva detected 14% more fraudulent claims in 2024, uncovering more than 12,700 suspect claims worth £127 million[5]. Allianz UK prevented £92.6 million in fraudulent activity in the first half of 2025 alone – a 34% increase year-on-year[6]. These numbers reflect the growing capability of AI-enhanced detection systems to catch what traditional methods miss.
This allows insurers to identify high-risk cases far earlier in the claims lifecycle. Rather than investigating fraud after it has occurred, insurers can begin to prevent losses before they escalate.
The commercial impact of AI-driven fraud detection
The financial implications are significant. The global insurance fraud detection market was valued at approximately $5.3 billion in 2024 and is expected to reach over $22 billion by 2030, growing at a compound annual growth rate of around 26%[7]. This rapid growth reflects the insurance industry’s recognition that AI-powered analytics is no longer optional.
AI also improves operational efficiency. By automatically screening large volumes of claims, insurers can focus their investigative resources on the cases most likely to involve fraud. This reduces investigation workloads while maintaining faster claims processing for legitimate customers – a critical advantage at a time when 58% of insurers acknowledge that delayed payouts contribute significantly to customer churn[3].
The result is a more efficient claims operation, a stronger underwriting position, and a better experience for honest policyholders.
The data challenge behind AI adoption
However, AI cannot operate effectively without the right data foundation.
Many insurers still operate across legacy systems where claims data, policy information, and operational records exist in separate environments. When data is fragmented or poorly structured, AI models cannot access the full picture required to detect complex fraud patterns. Before AI can deliver its full value, insurers must first ensure their data is integrated, structured, and accessible across the organisation.
This is not a trivial undertaking. Years of accumulated records across multiple platforms, inconsistent data formats between departments, and incomplete or duplicated customer records all present obstacles. Data preparation – including cleansing, standardisation, and integration – is the essential groundwork that determines whether AI-driven analytics delivers meaningful results or simply automates existing problems.
Where Dajon fits
This is where Dajon Data Management supports insurance organisations.
Dajon helps insurers prepare and structure their data so it can be used effectively by advanced analytics and AI technologies. Through data integration, migration, and structured data preparation, Dajon enables insurers to bring together information from multiple systems into unified data environments – creating the kind of clean, connected data foundation that AI-powered fraud detection requires.
Whether an insurer is consolidating legacy claims systems, preparing data for migration to a new platform, or structuring historical records for advanced analytics, Dajon provides the specialist data expertise that ensures the underlying information is accurate, consistent, and ready to deliver insight.
Preparing insurance for a smarter future
Fraud detection is becoming a data challenge as much as a claims challenge.
Insurers that invest in strong data foundations and AI-driven analytics will be far better equipped to identify suspicious behaviour, uncover hidden relationships, and protect their profitability. Those that continue to rely solely on manual review and rule-based systems risk falling further behind increasingly sophisticated fraud networks.
With the right data infrastructure and the support of experienced partners such as Dajon, fraud detection can move from reactive investigation to intelligent prevention – protecting both the bottom line and the premiums paid by honest policyholders.
References
- Fraudulent insurance claims continue to top £1 billion ABI[↩]
- Insurance fraud in UK climbs to record £1.16 billion Insurance Business UK[↩]
- Revealed – how much fraudulent claims cost UK insurers Insurance Business UK[↩][↩]
- Using AI to fight insurance fraud Deloitte Insights[↩]
- Aviva detected 14% more claims fraud in 2024 Aviva[↩]
- Allianz UK prevents record level of fraud Allianz UK[↩]
- Insurance Fraud Detection Market Size Mordor Intelligence[↩]