Artificial Intelligence (AI) for Insurance Claims

Artificial Intelligence in Insurance Claims: Quick Summary

Artificial intelligence for insurance claims helps reduce claim resolution costs by 20–50%, drive an up to 50% increase in claim specialists’ productivity, and achieve 5–10x faster claim cycles due to intelligent process automation.

AI-powered claim management systems can process 70–90% of simple insurance claims in a straight-through manner, with claims decisions delivered in minutes rather than weeks. They enable instant fraud detection, deliver accurate damage estimates, and provide intelligent recommendations on risk mitigation, helping insurers and claims service providers minimize financial losses and elevate customer experience.

Generative AI specifically is estimated to unlock a $100-billion benefit opportunity for insurers in P&C claims through reducing loss-adjusting expenses by another 20–25% and leakage — by 30–50%.

The Market of AI for Insurance Claims

The global market of AI in insurance is projected to grow from $14.99 billion in 2025 to $246.3 billion by 2035, exhibiting a CAGR of 32.3%. Claims processing is considered one of the largest use case segments for AI in insurance.

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 and TPAs can eliminate claim fraud and effectively mitigate losses.

Sample Agentic AI Workflows

The workflows below illustrate how specialized AI agents collaborate across claims systems, knowledge bases, external data sources, and customer interaction channels to automate repetitive claims tasks, improve adjustment quality, and speed up claim resolution.

Human experts remain involved in high-risk, ambiguous, and validation-flagged cases, while human feedback continuously improves agent performance through updated prompts, rules, and knowledge bases. Every action is logged for transparency and auditability.

Claim intake and triage

The claim intake agent collects FNOLs and supporting evidence from customer portals, mobile apps, and TPA platforms. The data validation agent verifies the accuracy and completeness of the submission package, identifies gaps, and request additional data when needed.

Once the claim package is complete, the claim triaging agent analyzes the submission using claim handling policies, triage rules, catastrophe event data, internal compliance requirements, and regulatory guidelines and determines claim type, priority, severity, and routing instructions. Built-in validation checkpoints verify the completeness and accuracy of outputs before claims enter downstream processing.

Claim fraud detection

Specialized AI agents work in parallel to detect claim inconsistencies and potential indicators of fraud. They validate document authenticity, compare FNOL descriptions against submitted evidence (images, videos, reports, etc.), verify claims through customer interactions, and match submissions against trusted third-party sources (e.g., telematics, weather, medical, fraud databases).

The fraud scoring agent consolidates the detected fraud signals, evaluates them using the company’s fraud-scoring models, investigation guidelines, and regulatory requirements, and produces fraud risk scores. The investigation support agent generates evidence-backed case reports and suggests next actions, like additional verification steps, claim holds, or investigator review.

Adjustment decision support

The claim summarization agent consolidates FNOL data, evidence, inspection reports, repair estimates, communications, and policy details into concise case overviews. The eligibility verification agent then checks coverage conditions, policy limits, deductibles, exclusions, and utilization and identifies claims that do not meet coverage requirements. It can also generate eligibility explanations for claimants.

For eligible claims, the adjuster decision-support agent analyzes claim details, aligning with claim management policies, the company’s compliance requirements, and regulatory rules. It checks submissions for fraud and parses claim execution costs across vendor networks. The agent then produces evidence-backed settlement suggestions and reserve estimates while flagging risks and inconsistencies. Human adjusters remain responsible for final decisions.

Architecture of Claims AI Analytics Engines

In the reference architecture below, ScienceSoft’s architects show how traditional AI models can be used as a core for a specialized AI analytics solution for the claims domain, outlining the solution’s key components and data processing flows.

An AI-driven analytics solution for insurance claims integrates with core insurance systems, customer-facing apps, and relevant third-party data sources to 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.

Challenges of AI for Insurance Claims — And How We Address Them

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.

Standards and regulations we adhere to

NAIC (including AI Principles and Model Bulletin), US state-level regulations (e.g., Colorado AI rules), NIST AI RMF, GLBA, NYDFS, CCPA, HIPAA (for health insurance), GDPR and AI Act (for the EU), IA (for the KSA), SOC 1/2, bank-grade model risk management practices (e.g., SR 11-7), and more.

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.

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.

Claims AI Consulting and Engineering Services by ScienceSoft