Generative AI is improving claims processing by reducing fraud and accelerating settlements, addressing two of the industry’s biggest challenges.
Fraudulent claims cost insurers billions annually, while delays in settlements often lead to poor customer satisfaction.
Generative AI tackles these issues by automating processes, analysing complex datasets, and identifying inconsistencies in real-time.
Platforms like Sprout.ai are empowering insurers to process claims faster, more accurately, and with enhanced fraud detection.
This blog explores how generative AI in claims is reshaping the industry, from its ability to detect fraud to speeding up claims settlements, and why it’s an essential tool for modern insurers.
Read the report: Generative AI in the Insurance Industry
What is generative AI in claims?
Generative AI is a branch of artificial intelligence that creates, analyses, and processes data in innovative ways. In insurance claims, it works by automating complex workflows, analysing large datasets, and identifying patterns and anomalies that streamline claims handling.
Unlike traditional automation tools, generative AI adapts to both structured and unstructured data, such as policy documents, emails, and images. This makes it an ideal solution for claims processing, where accuracy, speed, and fraud detection make a huge difference.
Platforms like Sprout.ai are using generative AI to improve claims management, enabling insurers to automate time-consuming tasks and focus on delivering better outcomes for customers.
Read more: 6 practical ways insurers can implement generative AI
How Generative AI reduces fraud in claims
Fraud prevention is an area where generative AI excels, thanks to its ability to process data in real-time and identify inconsistencies.
Advanced fraud detection
Generative AI analyses historical and real-time claims data to detect unusual patterns that may indicate fraud. This includes flagging claims with similarities to previously identified fraudulent cases.
For example, an insurance provider discovers a surge in claims for stolen vehicles with remarkably similar descriptions and incident circumstances. generative AI flags these claims due to their alignment with historical fraudulent patterns. Upon investigation, it is revealed that a coordinated fraud ring was submitting identical claims across multiple providers, leading to their arrest.
Document verification
Through advanced image recognition and metadata analysis, generative AI can identify forged or altered documents. It detects inconsistencies, such as mismatched timestamps or tampered images, far more accurately than traditional methods.
If a property insurance claim submitted for flood damage includes a repair invoice with altered dates. generative AI can identify metadata discrepancies in the document, such as mismatched creation and modification timestamps. It flags the claim for further investigation, saving the insurer from a potential payout for a falsified repair.
Data enrichment
Generative AI integrates external data sources, such as social media, public records, and geolocation data, to cross-check claims information. This holistic view helps identify discrepancies that might not be visible within internal systems alone.
A travel insurance claim for stolen luggage might be flagged by generative AI when the claimant’s social media posts showed them using the supposedly lost items days after the reported incident. By cross-referencing public records and social activity, the AI helps identify fraudulent intent.
Speeding up claims settlements with generative AI
Time-consuming claims processing leads to customer dissatisfaction and lower tNPS. generative AI addresses this by automating workflows and accelerating decision-making.
Automating documentation
Generative AI generates summaries of lengthy claims documents, extracts key information, and pre-populates forms. This reduces manual input and eliminates bottlenecks.
Imagine a home insurance provider uses generative AI to process claims for storm damage. When a policyholder submitted a 50-page contractor report detailing repairs, the AI generates a concise summary, extracted key repair costs, and pre-filled the claim form. This allows the handler to review and approve the claim in hours instead of days.
Real-time decision-making
By instantly analysing claims data against policy terms, generative AI enables adjusters to approve or escalate claims in minutes, rather than days.
For example, an auto insurer integrates generative AI to handle claims for minor accidents. A claimant submits photos of their vehicle damage via a mobile app, and the AI cross-referenced the images with policy coverage terms and repair cost estimates. Within 10 minutes, the system approves the claim, enabling immediate payout. This reduces processing times from days to under an hour.
Reducing administrative tasks
With generative AI handling certain repetitive tasks, claims handlers can focus on resolving complex cases and improving customer communication, further speeding up settlements.
A health insurer uses generative AI to automate the verification of treatment codes and policy coverage for reimbursement claims. This eliminates hours of manual checks for each claim. For instance, an adjuster handling a complex dental claim could focus on clarifying coverage limits with the claimant, leading to faster resolution and improved customer satisfaction.
Benefits of generative AI in claims
Faster processing
Generative AI accelerates claims handling, enabling insurers to settle claims in hours instead of weeks, significantly enhancing service delivery.
Enhanced fraud prevention
AI-powered insights identify anomalies and flag potential fraud, safeguarding insurers’ financial stability while maintaining fairness for genuine claimants.
Improved customer satisfaction
Accurate, speedy claim resolutions foster customer trust and loyalty, boosting satisfaction and retention rates.
Operational efficiency
By automating routine tasks like data extraction and document generation, generative AI reduces administrative burdens, allowing claims teams to focus on strategic decision-making and complex cases.
Challenges of generative AI in claims
While generative AI offers transformative benefits, integrating it into existing workflows comes with challenges.
Legacy systems
Many insurers operate on outdated systems that can’t handle modern AI applications. Sprout.ai addresses this by offering configurable solutions that integrate seamlessly with legacy platforms.
Data security and compliance
With increasing scrutiny over data privacy, insurers must ensure their systems comply with regulations like GDPR. Sprout.ai’s platform prioritises secure data handling and adheres to strict compliance standards.
Change management
Adopting generative AI requires organisational buy-in and training. Sprout.ai provides support to help teams adapt to AI-driven workflows effectively.
Read more: Debunking 9 myths about generative AI and insurance
Generative AI claims with Sprout.ai
With multilingual and multi-sector adaptability, Sprout.ai supports a wide range of business lines, enabling insurers to deliver faster resolutions and superior customer outcomes. The platform combines cutting-edge technology with robust security measures, offering insurers a trusted solution for modern claims management. Clear, explainable AI outputs, supported by detailed documentation, help insurers understand and justify automated decisions.
Sprout.ai uses generative AI in a number of ways to automate claims processing. From the creation of synthetic data to policy checking, generative AI has helped create a product that enables insurers to better serve their customers.
In one case, an insurer partnered with Sprout.ai to automate claims handling. Within three months, average settlement times dropped from two weeks to less than 24 hours, while fraud detection rates improved by 40%.
To learn more about how we have used generative AI to help insurers process claims faster and better serve their customers, book a call with one of the team.
