Artificial Intelligence (AI) for Insurance Claims

Artificial Intelligence in Insurance Claims: Quick Summary

Artificial intelligence in insurance claims helps reduce claim resolution costs by up to 75%, drive higher claim specialists’ productivity, and achieve a 5–10x faster claim cycle due to intelligent process automation.

AI-powered claim management systems enable instant detection and rejection of fraudulent claims, deliver accurate damage estimates, and provide intelligent recommendations on risk prevention, which helps insurers minimize financial losses and improve customer experience.

The Market of AI for Insurance Claims

The global market of AI in insurance was valued at $2.74 billion in 2021. It is projected to reach $45.74 billion by 2031, growing at a CAGR of 32.56%.

McKinsey predicts that by 2030, claim processing will turn into the most important insurance function, with AI being the primary driving force of its digital transformation. Key factors spurring the popularity of AI for insurance claims are the increasing demand for fast claim settlement and personalized customer approach. With the help of AI, insurers can eliminate claim fraud and effectively mitigate losses.

How AI for Insurance Claims Works

Architecture

Below, ScienceSoft’s consultants share a sample architecture of AI-powered claim processing software that we can implement.

An AI solution for insurance claims integrates with an insurer’s corporate systems, customer-facing apps, and relevant third-party data sources to instantly capture structured and unstructured claim-related data. It moves the collected raw data to the data lake for storage, then directs it to the advanced analytics system for preprocessing (data sorting, filtering, enrichment, cleansing). The preprocessed data is stored in a data warehouse for further analysis.

An analytics engine with a pre-trained machine learning model at its core analyzes the available claim event details to offer intelligent damage estimates and suggestions on whether the claim should be approved or rejected. ScienceSoft’s data scientists suggest using neural network-based models to ensure precise analysis and forecasting of even the most complex claim cases. A dedicated model management module is needed to design, train, and continuously tune the models, improving their accuracy over time.

Finally, the analytical results are stored in the analytical database to be used by claim professionals (via a claim management app) and for further AI model self-learning. The system instantly communicates claim-related decisions to the relevant systems (e.g., an insurance portal, underwriting software, an accounting system) to make them available for claimants and other insurance teams.

Functionality

While every AI-powered system we build is tailored to our clients’ specific needs, ScienceSoft’s consultants assembled a list of the most popular features usually requested by our clients from the insurance industry.

How Insurance Companies Benefit from AI for Claims Processing

Security mechanisms we work with

• Data protection: DLP (data leak protection), data discovery and classification, data backup and recovery, data encryption.
• Endpoint protection: antivirus/antimalware, EDR (endpoint detection and response), EPP (an endpoint protection platform).
• Access control: IAM (identity and access management), password management, multi-factor authentication.
• Application security: WAF (web application firewall), SAST, DAST, IAST (security testing).
• Network security: DDoS protection, IDS/IPS, SIEM, XDR, SOAR, email filtering, SWG/web filtering, VPN, network vulnerability scanning.

The Challenges of AI for Insurance Claims

AI poses a few unique challenges that complicate its incorporation into claim resolution processes. Below, ScienceSoft’s data scientists, solution engineers, and security officers share their hands-on experience to address the potential risks and maintain the economic feasibility of AI for claim management.

Challenge #1: Achieving high accuracy of analytical results

“Good” AI model accuracy starts at about 70%, but to reliably automate high-risk processes such as insurance claim management, the accuracy needs to be above 95%. To maximize the precision of AI insights, your AI model must be properly designed and trained on the appropriate data sets. Plus, its decisioning logic must be clear and explainable to avoid unintended bias and ensure compliance with the legal claim disclosure standards.

Challenge #2: Sensitive data security

When performing claim validation, AI deals with the policyholders’ sensitive data, including personal/business, financial, and health data. It poses strict security and compliance requirements for the AI-based claim processing solution.

Costs of Implementing AI for Insurance Claim Processing

Below, our consultants provide a list of main factors that may impact the development budget and duration:

• The scope and complexity of the solution’s functional capabilities.
• The number and type of AI models (non-neural network ML models, DNN models, CNN models, etc.) for intelligent process automation.
• Performance, scalability, security, and compliance requirements for the solution.
• The number and complexity of integrations.
• The chosen sourcing model (e.g., outsourced, in-house development) and team composition.

Implement AI for Insurance Claims with ScienceSoft

Since 1989 in AI implementation and since 2012 in engineering insurance solutions, ScienceSoft delivers reliable AI solutions for insurance claim processing.