How AI Is Helping Insurers Tackle Fraud

Insurance fraud is a persistent challenge for the industry, costing UK insurers over £1.1 billion annually, according to the Association of British Insurers (ABI). From exaggerated claims to entirely fictitious accidents, fraud not only affects insurance companies but also drives up premiums for honest policyholders.

Traditionally, detecting fraud has been a time-consuming and manual process, reliant on investigators sifting through data, interviewing claimants, and spotting inconsistencies by eye. But in an increasingly digital and data-driven world, these methods are no longer sufficient.

This is where AI is helping insurers in the battle to beat fraudulent claims.

What Types of Insurance Fraud are Impacted?

Insurance fraud isn't limited to criminal gangs or elaborate schemes; it spans a wide spectrum, from opportunistic exaggerations to organised crime. Here are some of the most common types:

Exaggerated Claims

One of the most frequent forms of fraud. A genuine claim is made, but the value is inflated. For example, a claimant involved in a minor car accident might report exaggerated injuries or include pre-existing damage in the repair claim.

Fictitious Claims

These are claims made for events that never actually happened. A person might report a burglary that didn't occur, or claim a mobile phone was lost when it wasn't. The goal is to receive a payout for something that doesn't exist.

Staged Accidents

Typically seen in motor insurance fraud, these are incidents that are deliberately set up to look like accidents. For instance, a fraudster might slam on the brakes in front of an unsuspecting driver to cause a crash, then file a claim for vehicle damage and personal injury.

Ghost Broking

This is a growing issue in motor insurance. Fraudsters pose as brokers and sell fake or invalid insurance policies, often targeting vulnerable or inexperienced drivers. Victims often only find out they're uninsured when they try to make a claim.

Identity Theft and Synthetic Identities

Fraudsters may steal personal details to open policies in someone else's name or use AI to create convincing synthetic identities. These are then used to claim payouts or launder money through the insurance system.

Application Fraud

This involves lying on an insurance application to obtain a lower premium. Common examples include misrepresenting your address, hiding previous claims or convictions, or stating a car is kept in a garage when it isn't.

Multiple Claims for the Same Incident

Also known as "double-dipping," this occurs when an individual submits the same claim to multiple insurers or seeks reimbursement for the same damage from both an insurer and a third party, such as a credit card company.

Organised Crime Rings

Some fraudulent activity is carried out by well-organised networks that exploit loopholes across multiple insurers. These rings can involve fake companies, bribed insiders, and large-scale claim operations, often linked to other forms of financial crime.

How AI Detects Insurance Fraud

AI technologies, particularly machine learning, natural language processing, and computer vision, are being deployed by insurers to automate and improve fraud detection.Here's how:

Machine Learning Algorithms

AI models can be trained on vast datasets of past claims to recognise patterns associated with fraudulent behaviour. These models can then scan new claims in real time, flagging anything that deviates from the norm.

Anomaly Detection

Rather than relying on pre-programmed rules, AI can identify outliers or unusual combinations of data points that might signal a problem. For instance, a high-value claim submitted unusually soon after a policy is purchased might raise red flags.

Natural Language Processing (NLP)

AI can analyse unstructured text from emails, claim descriptions, or adjuster notes. It can pick up on inconsistencies, detect sentiment shifts, or identify language patterns commonly associated with dishonesty.

Computer Vision

When claims involve images, such as car damage or property loss, AI can analyse these for signs of tampering or reuse. It can also compare submitted photos to stock images or those from previous claims to spot fraud.

Artificial intelligence is becoming a critical tool in the insurance industry's fight against fraud. When implemented effectively, AI can deliver a range of benefits that improve operational efficiency, customer experience, and bottom-line results.

Speed and Real-Time Detection

AI can assess claims and detect anomalies in real time, significantly reducing the time it takes to identify suspicious activity. This is especially valuable in high-volume environments like motor or travel insurance, where thousands of claims can be submitted daily.

Rather than waiting days for a manual review, a machine learning model can instantly flag a claim submitted minutes after a policy was taken out, giving insurers the chance to pause payment before funds are released.

Improved Accuracy and Reduced False Positives

Traditional rule-based systems often trigger false positives, flagging legitimate claims as suspicious and slowing down processing. AI learns from data over time, refining its predictions and minimising unnecessary investigations. This helps insurers focus their resources where it truly matters.

AI can distinguish between unusual but valid claims (like a policyholder travelling abroad) and genuinely suspicious behaviour (such as repeated claims for lost items just below the policy limit).

Greater Efficiency and Cost Savings

By automating the early stages of fraud detection, insurers can reduce the burden on human fraud teams. This doesn't eliminate the need for investigators, but it allows them to focus on more complex and high-value cases, improving team productivity and reducing investigation costs.

For example, an insurer might use AI to sift through all small claims under £1,000 and only escalate the most suspicious 5% to the fraud team, rather than manually reviewing every case.

Scalability

AI systems can handle vast volumes of data across multiple lines of business without needing to scale headcount. Whether an insurer processes 1,000 or 100,000 claims a month, AI tools can maintain consistent performance and scrutiny. During natural disasters or peak periods, AI can help maintain service levels without increasing staffing or sacrificing fraud oversight.

How Insurers are using AI

Many insurance companies are already leading the way in using AI to combat fraud.

Aviva

In 2024, Aviva detected over 12,700 fraudulent claims worth £127 million, marking a 14% increase from the previous year. The insurer also identified more than 98,000 fraudulent policy applications, nearly double the number from 2023. This improvement is attributed to investments in advanced analytics, machine learning models, and continuous training for their teams. ​

AXA

AXA employs AI technologies , including i mage recognition and natural language processing, to streamline claims processing. These tools have reduced the time to settle customer claims from five days to two in some instances. Additionally, AI assists in categorising unstructured data from emails and documents, enhancing fraud detection capabilities.

Considerations for Insurers

While AI offers huge potential in the fight against insurance fraud, it also introduces new challenges and responsibilities. Insurers must ensure that their use of AI is not only effective but also ethical, transparent, and legally compliant.

Bias and Fairness

AI systems are only as good as the data they're trained on. If the training data contains historical biases. For example, if certain groups are overrepresented in fraud investigations, the AI may unintentionally perpetuate or amplify those biases. This can lead to unfair targeting of certain demographics, postcode areas, or socio-economic groups.

Unfair treatment can lead to reputational damage, regulatory penalties, and a loss of customer trust. To counteract this, insurers must audit AI systems for bias and utilise diverse, representative datasets during training.

Transparency and Explainability

AI models, especially those using deep learning, are often seen as "black boxes", making decisions in ways that are difficult to explain to humans. This is problematic when a customer's claim is flagged or denied by AI, and they want to understand why.

Customers and regulators alike are demanding greater transparency. Insurers must be able to explain, in clear terms, why a decision was made. This requires building explainability into the model or using hybrid systems that combine automated detection with human oversight.

Privacy and Data Protection

AI systems rely on large volumes of personal data, which may include sensitive information such as financial details, medical history, or behavioural patterns.

Insurers operating in the UK and EU must comply with GDPR, which sets strict rules around data collection, processing, storage, and the right to explanation when decisions are automated. Insurers must ensure that their AI tools meet data privacy standards and that customers are properly informed about how their data is used.

In addition, Under GDPR (Article 22), individuals have the right not to be subject to a decision based solely on automated processing if it significantly affects them, for example, a rejected claim or flagged fraud case. If AI is being used to make or heavily influence these decisions, insurers must provide meaningful human review and allow customers to challenge the decision. Failing to do so could breach GDPR and lead to regulatory action.

Overall, AI is starting to transform the way that insurers tackle fraud, making detection faster, more accurate and scalable. While it's not a magic wand, it can offer significant advantages in staying ahead of increasingly sophisticated fraudsters.