How AI speeds up the claim management and reduces costs

The benefits of AI for efficiency and cost savings 

The gap between customer expectations and technological reality is becoming increasingly apparent in the insurance industry. According to Gartner, nearly 70% of financial services leaders view generative AI tools as beneficial for their organizations, despite the risks. And there’s good reason for that: within the insurance and finance industries, AI is increasingly being used for document processing, risk analysis, fraud detection, and customer communication—applications that directly contribute to more efficient processes and an improved customer experience.

In this article, we explore how a combination of low-code and targeted AI models can help modernize claims management. We’ll cover three key areas:

• What’s wrong with the current, manual processes?
• How can you effectively address this with AI?
• And what are the concrete benefits? We’ll use a real-world case study from Hollard Insurance as an example.`

The pitfalls of manual claim management

Many insurers still struggle with a slow, inefficient claims process. Claims are assessed manually, often one by one, leading to high processing costs and heavy reliance on individual claims adjusters. This makes the process not only labor-intensive but also error-prone. Assessments vary by claims adjuster, increasing the risk of inconsistency and eroding customer trust.

At the system level, the problems are just as acute: outdated IT architectures slow down innovation and make integrating new technologies complex and time-consuming. Insight into key process data—such as turnaround times, reasons for rejection, or signs of fraud—is often completely lacking. And if decision rules aren’t properly documented, compliance becomes a risk in itself rather than a safety net. The result: long wait times, rising costs, and a customer experience that fails to deliver on the promise of short turnaround times and transparency.

How AI contributes to real-time claim status

To address these bottlenecks, more and more insurers are turning to Artificial Intelligence (AI). Not to fully automate the process, but to better structure the initial steps. Whereas each claim used to be assessed manually, AI models now handle part of the initial analysis. AI in claims management revolves around three things: speed (of response), consistency, and scalability.

But what does that mean in practice? It starts with the intake: incoming claims are classified extremely quickly by type, urgency, or risk. No more waiting for someone to “take a look at it”—instead, claims are sorted immediately using smart AI models.

Next, the system automatically analyzes documents. Using OCR (optical character recognition) and NLP (natural language processing), the system reads submitted documents without manual intervention. What used to take hours now happens in seconds. Meanwhile, machine learning helps identify suspicious patterns or anomalous behavior based on historical data. It automatically flags potential fraud indicators for review.

And a lot is changing for customers as well: virtual assistants and chatbots keep them actively informed. No more frustrating silence or phone calls—updates arrive automatically, exactly at the right time. This is how AI transforms the claims process from reactive to proactive: fast, predictable, and scalable.

Building a digital foundation for smart claim processing

For projects like this, we combine low-code technology with flexible AI components. The OutSystems platform serves as the foundation. The major advantage: it lets us build quickly and focus on what’s truly needed in the process—no generic software—just custom solutions that grow with the organization.

Whether it’s an automated workflow, a customer portal, or an internal dashboard, everything is designed around how the organization actually operates. And because the application is built visually and modularly, changes can be implemented quickly. This makes it easier to adapt to changing legislation or new insights gained from day-to-day operations. Here, AI isn’t added as a separate layer; it’s integrated into the application’s core.

At LINKIT, experts across teams work together to make this possible. OutSystems developers integrate AI modules directly into business processes, such as document recognition or real-time customer updates. Data scientists develop and refine models for tasks such as classification, scoring, or fraud detection. Cloud engineers ensure a scalable infrastructure in which AI solutions run stably and efficiently. And consultants focus on governance, compliance, and ethical AI to ensure the technology is used responsibly. Through dashboards, teams gain insight into performance, turnaround times, and anomalies—information they can use immediately to make decisions.

This creates an agile digital environment where processes run more efficiently, become more transparent, and are easier to manage. Technology that supports rather than hinders, and that allows for evolution rather than mere execution.

“Low-code + AI = speed + agility”

Use case: optimizing the internal workflow process

At Hollard Investments, part of the Hollard Group, there had long been a need for an efficient way to manage customer cases within the call center. Employees relied on manual processes and scattered tools, with no central place to organize their work. The result: limited control over ongoing tasks, no clear overview, and wasted time handling day-to-day matters. As project manager and business analyst Zainub Khan describes it: “We wanted to move away from manual processes for managing daily tasks, have a central location where the administrative team could manage their work, and have an overview of all the work distributed among the team, including automatically generated task reports and team statistics.”

“We wanted to move away from manual processes for managing daily tasks”

In the past, the team evaluated various tools, but none proved suitable. They were too complex, required many plug-ins (integrations), or didn’t align with the team’s workflow. Together with LINKIT, the team opted for a low-code approach using OutSystems. Even during the first demo, it became clear that the platform aligned well with the team’s needs.

“It was clear that using OutSystems and the ‘drag-and-drop’ functionality of low-code would help us deliver within a short period of time; in fact, a fast time-to-market was one of our most important objectives.”

“A fast time-to-market was one of our most important objectives.”

We delivered a working MVP within six months. The solution now provides the team with a real-time overview, automatically generated reports, and a centralized workflow environment. The visual, flexible approach to development proved crucial.

“Low-code development addressed several challenges: it provided a visual and collaborative environment, accelerated development, simplified integration, and improved adaptability to changing business requirements.”

The initial results are positive, and the platform is now used daily. Hollard Investments is looking ahead and sees plenty of opportunities to expand the solution further, including into document storage and correspondence. As Zainub sums it up: “We definitely see potential for using the platform for future projects.”

“We definitely see potential for using the platform for future projects.”

What do these optimizations actually mean for your organization?

The benefits of an AI-driven, low-code approach quickly become apparent. In our own projects and across the market, we’ve observed, among other things:

• 50–80% less manual work per claim
• Claims processed within 1 business day
• Up to 40% lower claim costs due to fewer errors and greater automation
• 10–25% more timely fraud detection
• Higher customer satisfaction (NPS) thanks to real-time updates and clear communication

Curious about what this could mean specifically for your team or process? Get in touch with one of our specialists via our contact page.