A Study on the Application of Generative AI in Addressing Claims Processing Challenges in Medical Insurance

The processing of medical insurance claims has long been a cumbersome process involving manual paperwork, expert reviews, and delays that frustrate customers and strain insurer resources. Generative AI (Gen AI) is revolutionizing this by enabling automated data extraction, adjudication, and fraud detection, leading to faster processing times, improved efficiency, and enhanced customer satisfaction.

Leading insurers like AIA, Ping An, Manulife, AXA, Allianz, and UnitedHealth (via Optum) are deploying Gen AI solutions across Asia, Europe, and North America. This report examines these implementations, compares them across markets and insurers, and analyzes business impacts, including operational efficiencies and potential risks like algorithmic denials.

Data from 2025 industry reports highlight significant improvements, such as AIA's claims processing reduced to under 25 minutes in Korea and UnitedHealth's 90% auto-adjudication rate.

Generative AI in Addressing Claims

Introduction to the Pain Point

Medical insurance claims processing traditionally requires customers to submit physical documents, followed by manual verification, medical review, and approval by insurers. This leads to delays (often days or weeks), errors, high operational costs, and customer dissatisfaction.

Gen AI addresses this through optical character recognition (OCR), natural language processing (NLP) for summarizing reports, and anomaly detection for fraud. By automating these steps, insurers achieve straight-through processing (STP), reducing human intervention and accelerating payouts.

Generative AI in Addressing Claims

Gen AI Implementations by Insurers in Claim Process

Insurers are adopting multi-tiered Gen AI systems tailored to their markets. AIA's three-tiered approach—Gen AI OCR for submission, AI summarization for adjudication, and AI for fraud, waste, and abuse (FWA) detection—has been rolled out in four Asian markets. Similarly, Ping An in China uses AI for seconds-long underwriting and minutes-long claims.

In the US, UnitedHealth's Optum employs AI for risk scoring and auto-adjudication, though it faces scrutiny for denials. European players like AXA and Allianz use Gen AI copilots for claims workflows and fraud detection.

The table below compares key Gen AI features across insurers.

Insurer	Key Gen AI Features	Markets	Processing Time Reduction	Accuracy/Other Metrics
AIA	Gen AI OCR (97% accuracy, multi-language/handwriting), report summarization (halves review time), FWA detection (20+ indicators)	Asia (Hong Kong, Korea, etc.)	From 2+ days to <25 minutes (Korea)	STP: 22% to 73%; Auto-adjudication: 41% to 75%
Ping An	Smart Quick Claim (image recognition, OCR), AI underwriting/claims	China, Asia	Average claim: 7.4 minutes; 93% underwritten in seconds	High triage accuracy; Near-full diagnosis accuracy
Manulife	AI automation for claims, contact centers; Over 35 AI solutions	Asia, North America	Not specified (focus on efficiency)	Ranked #1 in AI maturity; Enhanced operations
AXA	Secure GPT for text/code generation; AI for claims automation/fraud	Europe, Global	Streamlined workflows (not quantified)	Improved claims handling; Fraud detection
Allianz	Insurance Copilot (Gen AI for workflows); AI fraud detection	Europe, Global	Faster assessments (not quantified)	Automated claims; £1.7M safeguarded from fraud (2023 proxy)
UnitedHealth (Optum)	AI risk scoring, auto-adjudication; 1,000+ AI use cases	US	90% auto-adjudicated	Faster state agency claims; Controversial denials

Market Comparisons

Gen AI adoption varies by region due to regulatory environments, healthcare systems, and digital maturity.

In Asia-Pacific (APAC), rapid innovation is driven by high mobile penetration and investments like AIA's $800 million cloud migration. China (Ping An) leads in speed, while Hong Kong/Korea (AIA, Manulife) emphasize customer-centric features.

In Europe, AXA and Allianz focus on compliance and fraud amid GDPR regulations. The US (UnitedHealth) sees high automation but faces lawsuits over AI-driven denials, highlighting ethical concerns.

The following table compares Gen AI impacts across markets.

Market	Leading Insurers	Adoption Drivers	Challenges	Business Outcomes
APAC (e.g., Hong Kong, China, Korea)	AIA, Ping An, Manulife	Digital transformation, cloud adoption, cross-border health	Economic ties to global tariffs	STP up 51% (AIA); Claims in minutes; High NPS
Europe (e.g., France, Germany)	AXA, Allianz	Regulatory compliance (GDPR), fraud focus	Data privacy restrictions	Streamlined workflows; Fraud savings (£1.7M proxy)
North America (US)	UnitedHealth (Optum)	High healthcare costs, advanced AI tech	Lawsuits over denials, ethical AI use	90% auto-adjudication; 1,000+ use cases

Business Impacts

Gen AI transforms insurer operations by reducing costs (e.g., halving review times at AIA), boosting efficiency (UnitedHealth's 90% auto-adjudication), and enhancing customer experiences (AIA's top NPS in Hong Kong).

Fraud detection saves millions, as seen with Allianz's £1.7 million in 2023. However, risks include biased denials (UnitedHealth lawsuits) and over-reliance on AI, potentially eroding trust. Overall, Gen AI drives revenue growth through faster payouts and personalized services, with APAC insurers like Ping An achieving 7.4-minute claims, far surpassing traditional timelines.

The table below quantifies before-and-after metrics from available data.

Metric	Before Gen AI	After Gen AI	Insurer Example	Business Benefit
Straight-Through Processing (STP)	22% (June 2020)	73% (Dec 2024)	AIA	Reduced manual work; Cost savings
Auto-Adjudication Rate	41%	75-90%	AIA, UnitedHealth	Faster decisions; Efficiency gains
Claims Processing Time	2+ days	<25 minutes to 7.4 minutes	AIA (Korea), Ping An	Improved customer satisfaction
Fraud Detection Savings	Not quantified	£1.7M (proxy)	Allianz	Risk mitigation; Profit protection
Review Time per Claim	Standard (full time)	Halved	AIA	Operational scalability

Challenges and Future Outlook

While Gen AI offers immense benefits, challenges persist. In the US, regulatory scrutiny (e.g., AMA fighting AI denials) could slow adoption. In Europe, privacy laws limit data use, while APAC benefits from agile regulations but faces economic risks. Insurers must prioritize ethical AI, as per Manulife's responsible AI principles. By 2030, industry forecasts (e.g., from BCG and Oliver Wyman) predict 95% AI-facilitated interactions, making Gen AI essential for competitiveness.

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Conclusion

Gen AI is solving the longstanding pain point of medical claims processing by automating submissions, adjudication, and fraud detection, as exemplified by AIA's innovations. Comparisons show APAC leading in speed and customer focus, Europe in compliance, and the US in scale but with ethical hurdles. Business impacts include cost reductions, faster payouts, and higher satisfaction, positioning forward-thinking insurers for growth in a digital era.

Everbright Actuarial Consulting and Broker Services is at the forefront of integrating Generative AI into insurance operations, offering specialized consulting to help insurers optimize claims processing, enhance fraud detection, and improve overall efficiency. With expertise in AI-driven solutions tailored to medical insurance challenges, Everbright partners with leading firms across APAC and beyond to implement multi-tiered systems like OCR, auto-adjudication, and anomaly detection. Our services ensure seamless digital transformation, boosting customer satisfaction and operational scalability—contact us today at info@ebactuary.com to revolutionize your medical insurance workflows and stay ahead in the AI era.