Automated claims processing: Tecniseguros case study

Artificial intelligence is redefining how insurance companies manage customer service, claims processing, and digital relationships with policyholders.

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When implemented correctly, automated claims processing enables reducing response times by up to 90%, absorbing 80% of repetitive administrative tasks, and operating with 24/7 availability and minimal human intervention in routine processes.

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In this article, we share a real case of automated claims processing where Crata AI worked with Tecniseguros to transform claims management into an automated, intelligent, scalable, and efficient system.

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Tecniseguros initiated a transformation process with Crata AI aimed at modernizing its service channels and optimizing conversational management.

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The project began with a very specific objective: improve the user experience in conversational channels, streamline service flows, integrate a new intelligent assistant with Tecniseguros' existing technology, and enable more agile management of requests, documents, and customer data.

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Building on this foundation, the project is now evolving toward more advanced artificial intelligence capabilities, such as open conversation, sentiment analysis, intelligent request routing, and automatic information summarization for internal teams.

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Table of contents

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What is automated claims processing

Automated claims processing enables insurers to handle key operational processes such as customer service, claims management, document validation, and case classification by urgency and type, improving efficiency and reducing response times.

These solutions combine technologies and methodologies such as:

• Natural Language Processing (NLP)
• Large Language Models (LLMs)
• Predictive data analysis and integration with core systems
• Process Intelligence and automation

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Unlike traditional RPA (Robotic Process Automation) solutions, which are limited to mechanically replicating rigid tasks, modern AI for automated claims processing enables the redesign of complex processes to maximize efficiency where technology delivers differential value.

This system replaces static, cold forms with intelligent dialogues that guide users in a much more natural way. This means an AI assisant for insurance doesn't just answer questions. It can also:

• Interpret user intent in natural language
• Validate documents and data in real-time against databases
• Automatically open claim files
• Classify the urgency of each case
• Coordinate the internal workflow with the insurer's core systems

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The result is a human-in-the-loop operational model, where technology manages repetitive tasks and the human team focuses on validating results and higher-value decisions.

In our experience, this type of solution is not about adding technology on top of existing processes, but about redefining operations to capture the full potential of automated claims processing in insurance.

At Crata AI, we apply a proprietary methodology that goes beyond traditional automation: we analyze, redesign, and execute end-to-end processes with artificial intelligence, always oriented toward efficiency, scalability, and economic return.

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In many insurers and brokerages, the first customer contact during a claim still depends on manual processes. This approach generates several structural problems that worsen as operational volume grows.

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Channel saturation

When a spike in claims occurs, support teams receive a high volume of inquiries that includes both urgent cases and simple administrative tasks. This can overwhelm service channels and force the team to prioritize without clear criteria.

The cost of manually processing a claim can range between $40 and $60. With effective automation, that cost drops to less than $20 per claim (Research and Markets 2025).

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Slow information validation

Manual review of documents, photographs, or policies can significantly delay the opening of a file. In implementations where initial triage is done manually, the time to first meaningful contact can exceed 24-48 hours during high-demand periods, a timeframe no policyholder should have to wait at a critical moment.

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Inefficient use of specialized talent

Professionals with technical experience and valuable knowledge end up dedicating much of their time to transactional tasks such as:

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• Requesting basic documentation from the insured
• Verifying and recording identity and policy data
• Entering claims information into systems

This limits their capacity to address cases that truly require expertise and expert judgment.

In projects like Tecniseguros with Crata AI, we see this clearly: AI doesn't replace the team, it multiplies it. It eliminates low-value operational burden, accelerates processes, and allows professionals to focus on decisions where they truly add value.

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Context and challenge

Tecniseguros is an insurance broker in Latin America, recognized for its operational volume and track record in claims management and insurance services.

The company faced a clear operational challenge: scale customer service without losing service quality, maintaining an agile, user-centered experience, especially during critical moments like request management.

This approach is not accidental. At Tecniseguros, user experience and comprehensive customer support are strategic pillars, with a strong focus on building close, clear relationships oriented toward delivering real value in each interaction.

In a context where customers expect immediacy and simplicity, evolving request management models becomes key to continuing to deliver an experience that meets expectations.

The project objective was clear: redesign the conversational experience from a user-centered perspective, automating key interactions and preparing the architecture for progressive evolution toward advanced AI capabilities.

This meant not just creating a chatbot, but rethinking contact flows, improving message format, adapting the conversation to customers' real needs, and integrating the channel with Tecniseguros' technological systems.

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Project objectives

The collaboration with Crata AI has focused on five main pillars.

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Optimize the conversational experience

Remap service flows so users could progress more clearly, guided, and naturally, reducing friction at key contact moments.

Automate frequent tasks

Allow customers to make requests and updates through conversational channels, avoiding unnecessary manual processes and improving service availability.

Integrate the assistant with Tecniseguros' technology

Connect the conversational channel with APIs and internal systems to achieve fluid management of data, users, policies, and requests.

Manage documentation more efficiently

Facilitate the handling of lengthy documents, multiple files, and different formats, preparing the process for increasingly automated validation and reading.

Build a scalable foundation for advanced AI

Develop the system so it could evolve toward capabilities like open AI conversation, sentiment analysis, intelligent classification, and automatic request routing.

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The solution: the new virtual assistant Leobot

To address these challenges, Crata AI developed Leobot for Tecniseguros, a virtual assistant that combines conversational automation, technological integration, and a smoother customer experience.

Leobot was not born as a simple automated response channel, but as an automated conversational management layer integrated with Tecniseguros' operations.

The solution enables guiding users through structured flows, collecting relevant information, automatically updating data associated with their profile, and adapting the experience based on the relationship between users, contact numbers, and policies.

One of the project's key points was integration with Tecniseguros' technology. This allowed the assistant to function not as an isolated tool, but as an interface connected with existing systems, capable of querying, recording, and updating information fluidly.

Building on this foundation, Tecniseguros and Crata AI are evolving Leobot toward a more intelligent model, incorporating conversational AI capabilities, intent analysis, sentiment analysis, and automatic request routing.

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Flow optimization and smart routing

The first step was to optimize existing conversational flows: review the user journey, adjust messages, reduce ambiguities, and adapt each interaction to customers' real needs.

But the project's value isn't just in the conversation visible to the user. Behind Leobot, a solid technological foundation was built, integrated with Tecniseguros' ecosystem, capable of connecting the conversational channel with their internal systems, managing dynamic user and policy information, and operating on a structure prepared to scale.

This foundation enables handling complex scenarios typical of the insurance sector: a single contact number can be associated with multiple users, each user can have different policies, and each interaction must adapt to the correct context. Leobot organizes this complexity to offer a smoother experience for customers and more orderly management for internal teams.

Additionally, the solution incorporates a document management layer designed to work with multiple files, lengthy documents, and different formats. This enables better structuring of received information, easier processing, and significant improvement in document management efficiency.

On this architecture, the project advances toward an AI-managed Smart Routing model, capable of interpreting open requests, identifying what information should be requested from the user, analyzing interaction context, and summarizing key information to facilitate subsequent management.

This evolution combines three layers:

How automated claims processing works in practice

In practice, Leobot functions as a conversational layer integrated with Tecniseguros' operations:

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1. The user initiates the conversation from WhatsApp.
2. Leobot guides the interaction through flows designed according to the type of request.
3. The assistant collects and structures the information needed to advance the management.
4. When applicable, the solution queries or updates data associated with the user's profile, their policies, or their relationship with Tecniseguros.
5. Integration with internal systems allows information to flow more orderly between the conversational channel and operational teams.

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Building on this foundation, the project is evolving toward AI capabilities that will allow interpreting open requests, classifying cases, analyzing sentiment, and summarizing key information to facilitate subsequent management.

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This model reduces operational friction and enables initiating the process in a matter of seconds.

"Leobot converts technology into an intelligence layer serving the customer: it integrates data, documents, and processes so that each interaction is simpler for the user and more efficient for operations."

The complex claims management flow, together with integration with core systems, enables scaling toward more sophisticated models of automation, analysis, and decision-making.

At Crata AI and Tecniseguros, we are already working on evolving this solution toward an increasingly autonomous, intelligent operation oriented toward business impact with the integration of advanced AI capabilities.

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Automation vs traditional process

The following table summarizes the key differences between the traditional manual process and the automated model we are developing with the Leobot system:

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Key solution capabilities and evolution toward AI at Tecniseguros

The solution developed by Crata AI for Tecniseguros combines conversational automation, technological integration, and a clear roadmap toward advanced AI.

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• Optimized conversational guidance: accompanies the user through clearer flows, better-structured messages, and an experience adapted to their needs.
• Dynamic management by user and policy: enables working with scenarios where a single number can be associated with multiple users or policies, adapting the conversation according to context.
• Integration with internal systems: connects the conversational channel with Tecniseguros' technology to query, record, and update information more fluidly.
• Efficient documentation management: facilitates handling lengthy documents, multiple files, and diverse formats, incorporating advanced document management, parsing, and validation technologies.
• Evolution toward conversational AI: the project advances toward cutting-edge AI capabilities that allow interpreting natural language, analyzing intent, detecting sentiment, and routing requests intelligently.

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Together, these capabilities convert WhatsApp communication into a more decisive, integrated, and scalable channel. The solution enables structuring information from first contact, reducing operational friction, and paving the way toward increasingly autonomous management, supported by advanced AI capabilities.

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Operational results: impact on efficiency

The implementation of Leobot has not only digitalized a channel but has improved Tecniseguros' performance KPIs, enabling a transition toward an intelligent operations model with direct impact on several key areas:

• Significant reduction in response time: By providing instant attention during claims, it not only improves customer satisfaction but drastically reduces opportunity cost and initial operational friction.
  

• Savings in manual workload: Leobot absorbs the bulk of transactional, low-value-added tasks. This enables re-skilling of human capital, freeing support teams to act as critical case managers, increasing productivity per employee without increasing opex.
  

• Total 24/7 availability: Guarantees uninterrupted business continuity. This elastic availability enables managing variable demand volumes in real-time, eliminating bottlenecks outside business hours and ensuring full coverage of the customer lifecycle.
  

• Greater consistency in information management: Thanks to automation of rules and flows, Leobot helps reduce manual errors, organize received information, and improve traceability of each interaction.

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Artificial intelligence doesn't just optimize customer service: it enables redefining the complete operational model of an insurer.

The market clearly reflects this. According to McKinsey and Company (2025), 70% of insurers are planning to increase their investment in AI, and it's not just a technological trend: it's a direct response to competitive pressure and growing policyholder expectations.

Among the most relevant benefits for companies in the sector:

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• Greater operational scalability without proportional cost increase
• Reduction of administrative costs by up to 50% in claims management (Research and Markets 2025)
• Improved customer experience during the most critical moments of the relationship cycle
• Faster claims resolution, reducing average closing time
• Fraud reduction through predictive analysis in real-time during the claims process

Beyond external service, many operations teams complement these solutions with internal knowledge chatbots so their agents can instantly access coverage, procedures, and policies, without depending on manual document searches.

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The sector's evolution points toward increasingly intelligent models. In the coming years, we will see how artificial intelligence is used not only to manage claims but also to:

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• Anticipate risks through predictive analysis before claims occur
• Detect fraud with real-time data models during the claims process
• Personalize insurance products based on each customer's actual risk profile
• Optimize customer retention with proactive engagement based on behavior

Market data: The global AI market in claims processing will grow by USD 1.39 billion between 2024 and 2029, with a CAGR of 28.4% during the period, according to Research and Markets.

AI enables transforming insurance from a reactive model to a proactive one. Companies leading this transition will be better positioned to retain customers, reduce claims, and operate more efficiently in an increasingly competitive market.

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Drive your operations with automated claims processing

If you want to explore how to integrate artificial intelligence into your operations, design custom automation, or start with a Crata AI AI Quickstarter, we can help you define the most suitable roadmap for your company.

The AI Quickstarter is our structured methodology to move from intention to execution in a few weeks. We combine business analysis, data assessment, and technical validation to identify where AI can generate real impact. This isn't about theory or generic cases: we work on your processes and your information to detect concrete opportunities, prioritize use cases with the greatest return, and validate their feasibility before investing in development.

During the process, we analyze your current operations, identify bottlenecks and repetitive tasks, assess automation potential, and design an initial architecture adapted to your context. The result is a prioritized list of use cases, a clear impact estimate (time savings, efficiency, or revenue improvement), and an actionable roadmap to implement AI progressively and safely.

If you're looking to go beyond exploration and start applying AI with business sense, this is the starting point.

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Contact: info@crata-ai.com

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FAQs about automated claims processing

When should automated claims processing be implemented?

Automated claims processing is especially valuable when the volume of manual procedures slows down the resolution of urgent cases, when customer wait times exceed industry expectations, or when the operations team spends more than 40% of their time on repetitive administrative tasks. This technology is the ideal solution for companies seeking immediate scalability in channels like WhatsApp without increasing headcount.

What AI do leading insurance companies use?

Leading insurers implement a combination of conversational AI, natural language processing (NLP) models, and in some cases, computer vision for claims image analysis. These technologies enable understanding customers' natural language, validating documentation, and executing management flows autonomously. According to Deloitte, 76% of insurers in the US have already implemented generative AI in some of their operational areas.

What is an AI insurance chatbot?

It is an intelligent assistant designed to execute end-to-end operational processes: from policy and coverage queries to complete claim reporting. Unlike basic bots based on fixed rules, an AI chatbot for insurance is trained to understand user intent, adapt to conversation context, and execute actions in backend systems such as automatic file opening.

What does AI enable in claims management?

It enables advancing from manual and fragmented processes toward more agile, integrated models assisted by artificial intelligence. In an initial phase, this includes optimized conversational flows, integration with internal systems, and better data and document management. In more advanced phases, AI can interpret open requests, classify cases, analyze sentiment, summarize information, and support routing to the appropriate team.

What function do chatbots serve in the insurance sector?

Their main function is to act as an executive filter that autonomously manages up to 80% of repetitive queries and procedures. By handling all initial administrative burden, data collection, identity validation, and file opening, they allow human agents to focus exclusively on complex cases requiring empathy and expert judgment.