For insurers investing in AI, understanding its return on investment (ROI) is key to optimising value and ensuring smarter, data-driven decision-making.
However, accurately measuring the ROI can be a challenge. Perhaps this is why you haven’t started using AI in your claims department yet.
By focusing on clear metrics such as cost savings, processing speed, customer satisfaction, error reduction, and strategic insights, insurers can build a strong business case for future AI investments.
Drawing on advice from interviews with insurance leaders, this blog takes you through the key metrics that provide clear insights into the value AI delivers in claims processing.
Cost savings
One of the leading drivers for AI investment in claims processing is cost reduction, which is achieved by streamlining manual tasks. AI can automate repetitive and labour-intensive processes, such as data entry, document analysis, and fraud detection, which take up hours of claim handlers’ time.
This automation reduces administrative overheads, allowing claims handlers to focus on more complex, value-driven tasks. Improving claims handlers’ job satisfaction can reduce employe turnover.
Additionally, AI can help prevent costly mistakes and minimise rework by improving accuracy, which in turn saves money on rectifying errors. AI also optimises resource reallocation by directing human efforts to strategic roles.
How to measure
- Track the reduction in operational costs by comparing pre- and post-AI expenses related to manual tasks, rework, and third-party services
- Monitor administrative overhead reductions and resource reallocation to higher-value tasks
Results with Sprout.ai
- 48% cost reduction in claims operations, equating to major savings across millions of claims processed annually
- £21 cost reduction per claim, delivered against high claim volumes
Processing speed
Over a fifth of insurance customers want claims settled in hours, but 43% wait over two weeks.
AI boosts the speed of claims processing by automating various stages of the claims lifecycle, such as first notice of loss (FNOL), data extraction, document analysis, and even decision-making.
This allows claims to move from submission to resolution much faster than manual methods, where claims handlers need to review documents, verify details, and process claims individually.
By automating these time-consuming steps, AI reduces the overall cycle time for claims. This quicker processing not only helps insurers handle a higher volume of claims but also ensures that customers receive their settlements much faster.
Faster claim resolutions lead to increased customer satisfaction, as policyholders no longer have to wait weeks or months to see their claims resolved. Instead, many claims can now be processed in real-time or within days.
This speed directly impacts customer retention. Research shows that customers who experience faster and smoother claims processes are more likely to stay with their insurers and recommend them to others. With expectations for quicker services growing, providing fast claims processing gives insurers a competitive edge.
As Martin Turner, Chief Claims Officer at AXA XL says: “AI can significantly reduce the number of manual touchpoints in the claims process… potentially saving months on claims handlers time and improving overall claim lifecycle performance. This will ultimately benefit the customer and could also improve operational efficiency.”
How to measure
- Track claim cycle times, from submission to resolution, before and after AI implementation
- Measure the percentage of claims processed in real-time, and monitor the total volume of claims handled within shorter timeframes
Results with Sprout.ai
- Sprout.ai enables real-time processing, which dramatically accelerates claims handling, ensuring faster resolutions
- Up to a 30% increase in processing capacity, making operations more scalable and responsive
- 22-day reduction in claim duration, with claims being processed and settled far quicker than traditional methods
Customer satisfaction
“In recent years, customer expectations have really changed,” says Laura Lazarus, Head of Personal Lines Home Claims at Aviva. “Using AI in claims can help speed up the process and allow our claims experts to concentrate on helping customers who may require more support or need more urgent assistance.”
Customer satisfaction is one of the strongest indicators of long-term success for insurers. It plays a key role in building customer loyalty and retention. In an industry where claims are often the only major touchpoint customers have with their insurers, providing a seamless and efficient claims process is vital.
AI enhances customer satisfaction by improving the speed, accuracy, and transparency of the claims process. When claims are processed faster and with fewer errors, customers experience less frustration and receive their settlements promptly.
This immediate benefit directly influences key customer satisfaction metrics like the Net Promoter Score (NPS), which measures a customer’s likelihood to recommend the insurer to others. A higher NPS indicates strong customer approval and is a positive sign for customer retention and growth.
Read more: Customer Experience: The Claim Handler’s Perspective
How to measure
- Use Net Promoter Score (NPS), customer retention rates, and feedback on the claims process to assess the impact of AI
- Regularly monitor these metrics to gauge improvements in customer satisfaction and adjust services accordingly
Results with Sprout.ai
- One insurer saw TNPS increase by +100 during a partnership with Sprout.ai
Read more: What is tNPS, and how can insurers boost it?
Fraud detection
Fraud prevention is a valuable use case for AI in the insurance industry. As the Head of Claims Operations at a leading insurer points out, “The use case for fraud prevention is being able to pick out patterns which humans can’t do as effectively as an AI solution.”
Traditional methods of fraud detection, which often rely on manual reviews and human intuition, are no longer sufficient in today’s technology-driven environment.
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. One in five claims handlers believe that up to a quarter of claims now involve fake supporting documents created or altered using AI and digital tools.
AI’s ability to detect fraudulent claims lies in its advanced pattern recognition and data analysis capabilities.
Unlike humans, AI can analyse vast amounts of data in seconds, cross-referencing details across multiple claims and identifying inconsistencies or anomalies that would be hard for a human reviewer to catch.
For instance, AI can detect discrepancies in metadata from submitted photos, such as checking whether the date and location of an image align with the claim details. It can also flag unusual patterns, such as repeated claims for the same type of damage, or receipts from unverified service providers, helping insurers to stop fraudulent claims before they result in financial losses.
With AI-driven fraud detection, insurers not only reduce the financial burden of fraud but also protect honest policyholders from the cost of inflated premiums. Itallows insurers to stay ahead of increasingly sophisticated fraud tactics and reduces the manual effort of claims review.
In addition, by quickly flagging suspicious claims for further investigation, AI enables claims handlers to focus on resolving legitimate claims, improving efficiency and customer satisfaction.
How to measure
- Measure the number of fraudulent claims detected and compare them with historical data
- Track reductions in fraudulent payouts and monitor the time saved on claim verification by analysing AI-flagged suspicious claims versus manual reviews
Results with Sprout.ai
- Sprout.ai 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
- It can analyse submitted invoices and receipts, comparing them against known templates and databases of legitimate service providers to spot forgeries or inflated amounts
Read more: Friend or foe: Is AI the only solution to rising insurance fraud?
Error reduction
Errors in claims processing can be costly and lead to rework, inefficiencies, and regulatory risks.
When a claim is processed incorrectly, it often has to be reviewed and reworked, which delays settlement times and increases operational costs. More importantly, errors can lead to compliance failures, particularly in highly regulated markets like insurance. Each mistake that slips through the cracks is a risk to an insurer’s reputation and bottom line.
AI reduces human error by automating the more repetitive and detail-oriented aspects of claims processing. It can quickly gather and cross-reference data from multiple sources, ensuring information processed is accurate and consistent.
For example, AI can extract key data points from policy documents and compare them against the submitted claims, ensuring that everything aligns with the customer’s coverage.
As a Senior Claims Operations Lead points out, “AI is going to assist in terms of assessing claim accuracy, by faster information gathering and data so that the claims handler can analyse and process the claim in a quicker and more efficient manner, meaning ultimately that customers are happier.”
How to measure
- Compare the error rates before and after AI adoption by tracking the percentage of claims requiring rework
- Assess the time spent correcting errors and monitor reductions in compliance-related issues due to AI-enhanced accuracy
Results with Sprout.ai
- 99% accuracy in data processing, minimisng errors and rework
Insights and predictive analytics
Beyond automation, AI empowers insurers with advanced insights through predictive analytics. This enables insurers to analyse large datasets, identify emerging trends, and make more informed, data-driven decisions.
AI’s predictive models allow insurers to forecast claims patterns, such as anticipating spikes in claims due to seasonal events or specific types of incidents.
For example, AI can help improve consistency in claim outcomes by providing more uniform assessments. As Paul Blyth, Head of Underwriting & Claims Proposition at SCOR, says: “You could give the same claim to 20 assessors and it is likely that you won’t get exactly the same outcome. Adopting AI improves consistency, which benefits future analysis and decision-making.”
How to measure
- Track how often AI-generated insights lead to improved decision-making by comparing the consistency and accuracy of claim assessments before and after AI adoption
- Monitor the quality of predictions in detecting emerging trends or spikes in claim volume
About Sprout.ai
Sprout.ai is an AI-powered platform designed to improve claims processing by delivering measurable results. With a 48% reduction in claims operations costs, £21 savings per claim, real-time processing, and 99% accuracy, Sprout.ai helps insurers maximise the value of their AI investments while enhancing capacity and operational efficiency.
