Fraud, waste, and abuse in insurance claims cost the industry billions of dollars annually. The Insurance Information Institute estimates that property and casualty insurance fraud costs the industry over $300 billion every year in the United States alone. As a result, each customer pays an extra $900 per year, mostly due to increased premiums.

Due to the cost of manually reviewing every claim, some insurers auto-approve claims below a certain threshold checking them for fraud, waste and abuse – it’s cheaper to do so than manually check each claim. While efficient, this leaves room for fraudulent claims to slip through the net, and increases Control over Combined Ratio (CPR). An estimated 20% of insurance claims are fraudulent, according to Forbes.

At, we believe in a world where no insurer needs to factor fraud, waste or abuse into their accounts. enables insurers to check every claim swiftly and accurately, ensuring thorough scrutiny and significantly reducing the likelihood of fraud. It acts as the brains behind the scenes in insurance claims processing, handling time-consuming tasks like reading documents, analysing claims for inconsistencies, and cross-referencing data with external sources. 

Not only does this reduce fraud and indemnity spend , it ​​reduces the time and resources needed to investigate and process claims. This lowers the overall administrative costs associated with managing both fraudulent and valid claims. These savings can be passed on to policyholders, resulting in lower insurance premiums.

The challenge of insurance fraud 

First, here’s quick run through the differences between fraud, abuse, and waste:

Fraud: Intentional deception to gain benefits, such as submitting false claims for non-existent treatments.

Abuse: Improper but not illegal actions, like exaggerating claims for additional benefits.

Waste: Inefficient or unnecessary use of resources, such as redundant medical tests that inflate claim costs without medical necessity.

Insurance fraud comes in many forms, from exaggerated claims to completely fabricated incidents. 

It’s currently on the rise. Reported cases of opportunistic fraud from March 2022 to April 2023 rose by 61 per cent from the year before, according to the City of London Police’s Insurance Fraud Enforcement Department. As the cost of living crisis creates financial pressure, growing numbers of otherwise law-abiding citizens are turning to insurance fraud as a way to ease financial hardships. 

For many of these fraudsters, the perceived risk of getting caught is low compared to the potential financial rewards. Even when fraud is detected, not all cases are prosecuted due to the high costs and complexities. 

AI and digital tools have made it easy to create convincing fake documents. Fraudsters are quick to adopt these new technologies, but many insurers are slow to upgrade their systems. Traditional manual review processes are time-consuming and prone to errors, making it challenging for insurers to keep up with the volume of claims. This often results in smaller claims being auto-approved to streamline operations, a lag that fraudsters can exploit. 

Read more: How investing in automated claims processing reduces costs for insurers

How tackles fraud can analyse and verify claims data in seconds, compared to the hours or days required for manual reviews. It can check what’s written on documents as well as metadata in images. For example, it can check that a photograph of a crash was taken at the time and in the place the claimant says it was.

This quick turnaround reduces the overall claims processing time, allowing insurers to check every claim for fraud, usually in real time. uses Optical Character Recognition (OCR) and Natural Language Processing (NLP) to extract and analyse data from claims documents. 

Here’s how these technologies help reduce fraud and waste:

1. Data analysis

   – Optical Character Recognition (OCR) uses OCR to convert physical documents into digital data, allowing the AI to swiftly analyse and extract relevant information. For example, if a claim includes handwritten notes or scanned receipts, OCR technology ensures these documents are accurately digitised for further analysis.

   – Natural Language Processing (NLP)

NLP interprets textual data from claims to identify anomalies and patterns indicative of fraud. For instance, if a claim’s description contains inconsistencies or unusual terminology that doesn’t match typical claims, NLP flags these discrepancies for further review.

2. Real-time fraud detection

   – Immediate analysis processes claims in real-time, enabling insurers to evaluate the legitimacy of each claim as soon as it is submitted. This rapid analysis can identify red flags, such as repeated claims for similar incidents from the same individual or address, which might suggest fraudulent activity.

3. Advanced pattern recognition

   – Historical data comparison

The AI system compares new claims against a vast database of historical claims data to detect unusual patterns. For example, if an unusually high number of claims are filed from a specific region or under similar circumstances, the AI can flag these as potential fraud hotspots.

   – Machine learning 

These models continuously learn and improve from past claims data, becoming more adept at recognising fraud patterns over time. This ongoing learning ensures that the system adapts to new fraud tactics as they emerge.

4. Cross-referencing

   – Integration with external data sources integrates with external databases, such as fraud watchlists, government records, and industry-specific data sources. This integration allows for comprehensive cross-referencing, ensuring that claims are verified against a wide array of information. For instance, a claim for a stolen car can be cross-referenced with police reports to confirm the incident.

   – Dynamic verification

Real-time access to external data sources means that claims can be verified as new information becomes available.

Read more: How does use Large Language Models (LLMs)?

The types of fraud Sprout tackles

– Duplicate claims

If a customer submits multiple claims for the same incident across different insurers or within the same insurer using slightly altered details,’s system can detect these duplicates by comparing data points such as incident descriptions, dates, and involved parties.

– Exaggerated claims

NLP can identify exaggerations by analysing the language used in the claim descriptions. For example, a claim describing “total destruction” of a vehicle in a minor accident can be flagged for further investigation.

– Fake invoices and receipts

OCR technology can scan and analyse submitted invoices and receipts, comparing them against known templates and databases of legitimate service providers to spot forgeries or inflated amounts.

How reduces waste

– Reducing the administrative burden

By automating the document review process, reduces the administrative workload on claims handlers, allowing them to focus on the complex cases that require human judgement. This efficiency helps prevent waste associated with prolonged claim processing times.

– Minimising payout errors

Accurate data extraction and analysis ensure that only legitimate claims are paid out, reducing the financial waste from erroneous or inflated claims.

Conclusion addresses fraud and waste in insurance claims by providing comprehensive, real-time analysis with powerful data processing tools. With every claim thoroughly checked in seconds, it stops fraudulent claims from going under the radar. 

This helps insurers reduce costs, improve accuracy, and enhance overall operational efficiency. Not only does it combat fraud, it creates a more reliable and trustworthy insurance industry. 

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