Why leading insurers rely on data analytics for their claims

Imagine your insurance company receives 10,000 claims in a single month. Previously, your team spent weeks reviewing them manually, accumulating angry customer calls, late payments, and escalating administrative costs.
 

Today, thanks to data analytics in insurance claims, 60% of those claims are processed in minutes without human intervention. AI classifies cases, detects fraud, and automatically approves simple claims. 
 

The result: operational savings of millions of dollars per year, happier customers, and adjusters focused solely on the most complex cases. 
 

This is the power of predictive analytics in insurance claims: transforming a costly and time-consuming process into a driver of efficiency and profitability. From fraud detection to resource optimization, insurers that adopt AI are leading the market and leaving the competition behind.

How insurers are using AI in the claims process

Insurers are moving away from manual and fragmented processes. Today, a claim can go through an intelligent system that:

- Automatically classify and prioritize cases, sending the lowest-risk cases for immediate approval. “Claims classification is another area where AI can save time and effort. It can prioritize different requests based on their complexity and urgency, as well as assign simple claims to automated systems and complex cases to human adjusters,” Salesforce noted in an article.

- Cross historical data to detect suspicious patterns that may indicate fraud.

- Approve or deny claims in seconds based on business rules and machine learning.

- Predict resolution timeto keep the customer informed at every stage.

For example, if a customer files a claim for a damaged appliance, the system can instantly verify if the item was covered, compare its market price, and approve payment without human intervention. This type of predictive claims analytics not only saves time but also improves the user experience, generating greater brand loyalty. 
 

British insurer Aviva adopted more than 80 artificial intelligence models to optimize its claims management, McKinsey reported in an article. 
 

Thanks to this implementation, it was able to accelerate the liability assessment process in complex cases by 23 days, increase the accuracy of assigning claims to the correct teams by 30%, and reduce customer complaints by 65%. 
 

In addition, this modernization of its auto claims department generated savings of over $82 million in 2024.

Cases where AI can automatically deny claims

Automation is also used to identify and deny claims that don't meet policy conditions. Here's how insurance companies use AI to legitimately and efficiently deny claims: 
 

Duplicate claims: If the same claim has already been paid, the system detects it and prevents unnecessary payments.

Non-covered events: The AI ​​compares the incident description with the policy exclusions.

Insufficient documentation: The system alerts the customer and requests additional files to process the case.

This level of automation represents a competitive advantage.It's beneficial for insurers, as it reduces fraud and losses, optimizing their operating costs. However, these companies also recognize the importance of maintaining their policyholders' trust. 
 

Therefore, they implement clear and accessible appeals processes that allow customers to review and challenge decisions made by AI. Furthermore, they continuously monitor their predictive models to minimize errors and ensure that no legitimate claim is unfairly rejected. 
 

In this way, they combine technological efficiency with a commitment to offering a fair and transparent experience for all policyholders.

Types of data analyzed in insurance claims

Data analytics in insurance claims combines structured and unstructured information to obtain a 360° view of each case: 
 

• Policy data: Current coverage, compensation limits, and exclusions.
• Claim information: Location, time, descriptions, and evidence submitted.
• Previous claims history: Frequency and severity of a client's claims.
• Third-party data: Police reports, market prices, weather information.
• Multimedia content: Photos and videos analyzed by computer vision to validate damage.

This wealth of data feeds machine learning models, which learn over time and make each prediction more accurate.

AI insights that generate value for insurers

The true power of predictive claims analytics lies in the insights it generates for decision-making: 
 

• Average costs by claim type, to adjust policy prices.
• Claim frequency by region, to detect high-risk areas.
• Loss severity, identifying claims with high financial impact.
• Fraud probability to allocate investigative resources where they are most needed.
• Operational efficiency, revealing bottlenecks in claims handling.

These insights not only improve internal operations but also drive more competitive product design and precise customer segmentation.

What insurers are spending unnecessarily without AI

Insurers that still manage claims traditionally are losing money on:
 

• Slow manual processes, which require more administrative staff.
• Improper payments due to failure to detect fraudulent claims in a timely manner.
• Fines or litigation due to management errors affecting clients.
• Insured customer attrition, frustrated by the slow resolution of their cases.

Failing to invest in modernizing these processes means continuing to fuel losses and falling behind competitors already using AI.

Before implementing AI, an insurer cited by PwC faced significant efficiency losses in its claims assessment process. The growing demand for damage estimates exceeded the capacity of its adjusters, resulting in long wait times for clients, delayed compensation payments, and an unsatisfactory experience that jeopardized policyholder loyalty.

In addition, operating costs skyrocketed as it had to hire and train more specialized personnel to keep pace. The company was reaching a critical point, and every day of delay in its processes represented lost revenue and opportunities compared to more agile competitors. 
 

Until the company, through a software provider, developed AI models to detect and classify damage, improving efficiency. 
 

What AI can save insurers in claims management

Implementing predictive analytics in insurance claims can reduce maintenance costswas significant:

• Savings of up to 30% in operating costs by automating simple claims.
• Early fraud detection, reducing losses worth millions.
• Fewer human errors, thanks to the consistency of automated decisions.
• Increased customer satisfaction, which translates into retention and cross-selling.

At Rootstack, we have helped insurers develop customized insurance claims data analytics solutions. Our team of developers and AI experts integrates machine learning models into existing management systems, ensuring a tangible return on investment and a superior customer experience.

Recommendations for transparency and fairness in the use of AI in insurance companies

For these solutions to be sustainable and build trust, insurers must prioritize: 
 

• Explainability of AI models: allowing the customer to understand why their claim was approved or denied.
• Human oversight: especially in complex or high-value claims.
• Periodic audits to avoid bias and continuously improve algorithms.
• Regulatory compliance, aligning technology with privacy and consumer protection regulations.

Rootstack advises companies on the implementation of these best practices, ensuring that technological innovation goes hand in hand with ethics and transparency.

Conclusion

Predictive analytics in insurance claims is not just a trend, but a strategic necessity for insurers seeking to survive in a digital market. Investing in AI for claims management allows for reducing fraud, optimizing resources, and delivering a much faster and fairer customer experience.
 

At Rootstack, we've developed predictive claims analytics projects that have helped insurers save millions in costs and gain the trust of their customers. 
 

If your company wants to modernize its claims cycle, contact us today and find out how we can help you take your operation to the next level.
 

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