AI in Insurance Claims Operations: Where Automation Delivers Real ROI

Traditional insurance claims operations are under immense pressure to change. What has shifted now is the margin for delayed results. Today’s customers demand faster updates on claims, while insurers need more robust ways to detect sophisticated fraud patterns.

The problem is, simply adding more people isn't a sustainable solution when teams are already dealing with complex documentation. Where most insurers rely on legacy systems that involve endless manual handoffs and document-heavy processes, the modern pace requires a change.

According to Accenture, around 17% of claimants are unhappy when a claim is settled in under 48 hours. When that claim stretches to three months or longer, dissatisfaction scores climb to 39% or higher. EIOPA's Consumer Trends Report confirms that claims handling remains one of the most complaint-prone areas of insurance operations. That is where AI can make a significant difference.

Where the Power of AI Fits into the Insurance Claims Cycle

The use of artificial intelligence doesn’t have to mean laying off insurance team members. It isn’t a “fix-all” for the industry. What it does mean is inserting AI into specific stages of the claims process where manual effort often drags things down.

A good starting point would be using AI in the claim intake phase. That allows the trained system to extract and classify information from claim forms, photos, reports, invoices, and other incoming documents. When adjusters spend less time entering data, they can focus more on customer satisfaction.

Machine learning models can route cases to the best-suited handler based on their complexity, coverage type, and presence of early loss signals. That results in faster assignment over manual triage.

The same is true for damage assessment using computer vision and predictive modeling for fraud detection, continually scanning incoming files for red flags and unusual patterns. Even the settlement stage can be improved with AI automation, reserving human review for exceptions that need further oversight.

Where Insurance Automation has the Most ROI

Like any other business, insurers need measurable ROI from any AI integration. For automation, this usually involves taking high-volume, repeatable tasks that are often subject to human error and streamlining them into efficient workflows. It’s not about big promises of “AI-powered claims transformation.” It’s about removing friction and operational risk.

For example, strong ROI can come from intelligent document processing. The average claims team has to pore over massive volumes of structured and unstructured documents. Claims forms, repair estimates, medical records, and submitted invoices clog up regular processes. AI can take that siloed data and identify missing info or prepare summaries in a fraction of the time it typically takes humans. The result is faster intake, fewer manual touchpoints, and less rekeying.

As already mentioned, AI can also improve triage and claims routing. When you have adaptive ML systems that can learn how your specific team prefers to manage complex claims or verify coverage type and fraud risk, it cuts down on barriers. Better routing helps reduce reassignment, rework, and those expensive delays that drive up customer dissatisfaction scores.

For simpler insurance claims, like a minor fender bender, AI can fast-track processing. When there is clear coverage, easy-to-verify documentation, and a low fraud risk, automated checks can support faster settlements. Humans can quickly review those decisions and serve as an additional layer of protection against potential overpayments or missed recovery opportunities.

All that being said, there should always be human judgment. AI automation is a great benefit for insurance summaries, severity predictions, and alerts for comparable references or missing data, but the human mind should be the final decision-maker. Where an AI provides faster, more consistent decisions, accountability must still be with the claims professional.

These improvements are attractive to insurers because they are easy to confirm and measure. However, scope also matters. According to ScienceSoft's project experience, developing a custom claims automation solution typically costs around $200,000–$500,000+, depending on scope and complexity. The goal is to justify insurers’ investment by weighing how AI will address bottlenecks so real gains can be realized in document handling, routing, and fraud screening.

What Insurance Automation ROI Looks Like in Practice

Financial returns for a business of any niche don’t necessarily come from a single breakthrough. It’s more likely due to a combination of shorter cycle times, better routing, and better use of adjusters' time. AI provides these benefits and ensures accuracy and lower claims leakage.

An excellent example of this automation is with Aviva. The insurer has reduced the average liability assessment time for complex cases by up to 23 days through AI-supported claims integration. It also improves routing accuracy by 30%. All that value helps the entire organization operate more efficiently and supports more consistent decisions.

Compensa Poland, part of Vienna Insurance Group, is another clear example. The company uses a self-service claims handling system that automates most steps from first notice of loss through segmentation, assessment, settlement, and reserve adjustment. Accenture reports up to 73% increase in process efficiency.

Where AI really builds strong ROI is in fraud detection. Having a system that is free from human emotion, won’t get fatigued, and doesn't require time off ensures consistent protection against potential insurance fraud. Ping An reported RMB 6.44 billion in claims savings just from using an AI-powered smart fraud detection tool through the first half of 2025. While that won’t be the case for all insurers, the lesson is worth noting.

Where AI Can Fail to Deliver ROI

AI projects aimed at improving insurance claims efficiency can and do fail, and usually for practical reasons, not because the technology doesn't work. This is not due to impractical or unusual modeling. It’s because of measurement. Capgemini found that 42% of P&C insurers do not track AI-specific metrics. How can anyone know whether automation is reducing costs, improving accuracy, or speeding up settlement when there is nothing to compare it to?

Where AI may struggle is with imbalance. Many insurers spend heavily on new tech but underinvest in aligning the company with those integrations. Without complete “buy-in” from the intern to the CEO, the model isn’t trusted. That leads to expensive manual double-checking, erasing any savings.

Finally, there are legacy insurance systems that people simply don’t want to let go. Data silos or policy administration and CRM systems that cannot support adaptation and growth hinder process efficiency. When an AI is introduced, it remains disconnected, losing any potential ROI in the adoption.

Protect AI Insurance ROI with Governance and Human Oversight

Insurance companies of all sizes in any nation are subject to regulatory oversight. That adds complexity to the business but also provides reassurance to the customer. Governance and compliance also protect the economics of AI automation. It ensures the new integration is auditable and remains aligned with modern regulations. That helps build trust with clients about coverage, payouts, fraud review, or overall treatment.

Regulators are increasingly requiring that AI-supported decisions be explainable, auditable, and subject to human oversight.

All this confirmation of oversight is good. It is what ensures over-automation is culled, reducing erroneous decisions, customer complaints, regulatory scrutiny, rework, and loss of trust. Having humans stay in the loop ensures those high-value or complex claims receive the attention they need, while also improving systems based on proven, taught AI modeling.

Final Thoughts

Using AI in insurance claims processes will not suddenly make a business profitable. The real, measurable value comes when the tools are applied to the right areas. As long as the business is willing to measure KPIs and build governance into the process, simple claims can be automated, complex claims streamlined, and ROI realized. A little operational discipline can connect modern technological improvements to insurers willing to make a change.

Author Bio: Olga Vinichuk, Insurance IT Consultant and Lead Business Analyst, ScienceSoft

Olga has built a vibrant career at ScienceSoft as a business analyst and insurance IT consultant. She participated in ScienceSoft’s 11 major insurance projects, guiding 8 of them as a leading business analyst. As an insurance IT consultant, Olga shapes the unique solutions that digitally transform underwriting, claim settlement, policy management, and compliance monitoring workflows. Olga is also involved in ScienceSoft’s outsourced product development projects, where she helps SaaS insurance companies turn their high-level product concepts to fully-functional solutions.