AI is transforming claims processing, offering insurers greater efficiency, accuracy, and fraud prevention. However, with a number of solutions available, it’s important to be aware of what features matter most when selecting an AI-powered claims automation system.
This guide breaks down the key factors to consider.
Read more: A Business Case for AI: Insights from 20 Interviews with Senior Claims Leaders
- End to end claims processing
The best AI-powered claims technology can automate the entire claims workflow, from First Notice of Loss (FNOL) to final decision. Claims processing software should offer straight-through processing capabilities for simple claims, allowing them to be approved or denied without human intervention. This frees claims handlers to focus on complex cases, while preventing fraudulent claims from slipping through an auto-approval net.
Look for solutions that use Natural Language Processing (NLP) to extract key information from structured and unstructured documents like medical reports, police reports, and customer communications. Image recognition tools are also important as they reduce the time needed for manual inspection and can detect subtle details that may be missed by the human eye.
Read more: Did you know Sprout.ai can process claims documents in over 100 languages, from Japanese to Greek?
- Complex claim capabilities
Automated claims management systems should be able to process and assess complex claims with multiple parties and a range of documents. Machine learning models should be trained to recognise and interpret claims data across a variety of formats and languages. The system should be able to extract, structure, and categorise information, then cross-check it with policy terms and conditions to ensure eligibility and coverage.
This doesn’t replace the human element of claims handling, but rather frees claims handlers from slow, manual tasks so they can spend more time working directly with customers.
- Fraud detection
Fraudsters are increasingly using AI to create compelling fake documents and receipts that are undetectable to the human eye. In fact, 19% of claims handlers believe that up to one in four claims now involve fake supporting documents created or altered using AI and digital tools, according to recent research by Sprout.
In the UK alone, fraud costs insurers more than £1bn every year, adding £50 to each annual household insurance premium. Undetected fraud is estimated to cost insurers a further £2bn.
AI claims technology should include robust fraud detection capabilities to reduce losses from fraud, waste and abuse. The system should be able to spot patterns and anomalies that suggest fraudulent activity, while using predictive analytics to flag high-risk claims automatically.
Fraud detection algorithms should work with historical claims data, document metadata, and other external data sources to identify fraudulent behaviour. Advanced models can detect tampered documents, altered images, and suspicious patterns like repeated claims from the same geographic area.
Read more: How Sprout.ai reduces fraud and waste in insurance claims
- Data handling and unstructured inputs
AI claims management systems should be able to efficiently process both structured and unstructured data, including handwritten documents, PDFs, images, and emails, ensuring all relevant information is captured and used in decision-making.
The best claims AI systems use computer vision to analyse visual data like damage photos and OCR to extract data from documents. This ensures all data types, including handwritten notes and scanned images, are processed accurately. The AI should also understand context, ensuring the data is interpreted correctly within the scope of the claim.
- Real-time data processing
Over a fifth of consumers expect their claims to be resolved within hours. However, 43% of customers wait over two weeks for a claim to be resolved, according to research from Sprout.ai.
Claims AI systems should enable insurers to process claims in real time, reducing the time it takes to deliver decisions to customers. This will boost customer satisfaction and tNPS scores.
The AI claims platform should be capable of real-time data extraction and analysis, allowing claims to move quickly from intake to decision without bottlenecks. By continuously learning from each processed claim, the best systems become faster and more accurate over time, improving response times and customer satisfaction.
- Easy integration with existing systems
AI claims solutions should integrate smoothly with existing Claims Management Systems (CMS) and claims processing workflows. This allows insurers to implement the AI without disrupting operations or requiring additional training for claims handlers.
Look for systems that enable seamless data exchange between the AI tool and existing platforms such as policy management and underwriting systems. The solution should also support various file formats, including PDFs, images, and scanned documents.
Read more: Out-of-the-box claims solutions vs. building a bespoke solution: Which is right for you?
- Accurate decision-making
For 62% of customers, being able to trust in a fair outcome is the most important factor when it comes to selecting an insurer.
AI systems should not just be reliable, but should enable more accurate decision-making by claims handlers, especially for complex claims. An ability to process large amounts of data quickly ensures decisions are based on the most up-to-date and relevant information.
By analysing hundreds of data points from a claim and comparing them to historical patterns and policy terms, AI can enable more precise decision-making, reducing errors and ensuring fair claim outcomes.
- Scalability and flexibility
AI-powered claims systems should be able to adapt to different increasing claim volumes as business needs grow. The system should be flexible enough to handle different types of claims, from simple property damage to complex liability claims.
Scalable AI claims tools use cloud computing to handle high volumes of claims data without performance degradation. Flexibility is achieved with machine learning models that can adapt to new data and regulatory changes over time.
- Compliance and data protection
Compliance with data privacy regulations like GDPR is a necessity in the insurance industry. AI systems should handle sensitive customer data securely and ensure compliance with all relevant legal frameworks.
AI claims platforms should use data encryption and secure cloud storage to protect customer information. They should also include built-in compliance checks to ensure claims processes meet regulatory requirements, including policy limits and exclusions.
Black box AI solutions—those that operate with complex, often opaque algorithms—can raise concerns about accountability and data handling practices. Insurers need to understand how AI models make decisions, particularly when dealing with sensitive customer data, to ensure they remain within legal boundaries and build trust with customers. To address this, AI claims platforms should aim for explainability, which allows insurers to interpret how the AI reached its conclusions.
Read more: How Sprout.ai uses AI, and what sets it apart from other AI tools
Sprout.ai: The most advanced AI-powered claims technology
Sprout.ai can automate the entire claims workflow, from FNOL to final decision, allowing for straight-through processing of simple claims while enabling claims handlers to focus on more complex cases.
It uses Natural Language Processing (NLP) and Optical Character Recognition (OCR) to extract key information from structured and unstructured data such as medical reports and damage assessments, ensuring real-time, accurate decision-making.
Read more: How Sprout.ai transforms claims
It works in the background, integrating seamlessly with existing Claims Management Systems without disrupting workflows.
Sprout.ai also comes with advanced fraud detection, analysing historical claims data, document metadata, and external data sources to identify suspicious patterns and potential fraud.
The platform is scalable and flexible, using cloud-based infrastructure to handle growing claims volumes. It is ISO27001 certified and compliant with data protection regulations like GDPR through encryption and secure data storage.
