Summary
The article examines how AI is reshaping insurance by streamlining operations, enabling personalization, and boosting workforce productivity. It notes rising executive investment and market growth expectations while emphasizing persistent hurdles—legacy environments, siloed data, and responsible deployment—and previews a path toward scalable, value-focused adoption across underwriting, claims, and customer engagement.
Key Points:
- Defines AI’s role in insurance as a catalyst for efficiency, personalization, and productivity.
- Highlights business benefits including streamlined workflows, faster decisions, improved experiences, and stronger growth potential.
- Addresses implementation challenges involving legacy environments, fragmented data, fairness concerns, and scaling across enterprises.
- Outlines future outlook emphasizing strategic investment, responsible adoption, and measurable value across core insurance functions.
AI in insurance: Current trends and what’s next?
AI adoption in insurance is accelerating as leaders prioritize investment, and the market is projected to grow 28.4% annually from 2024 to 2030, reaching about US$3.59 billion. The technology delivers measurable gains in costs, growth, accuracy, and returns, and it is reshaping underwriting, claims, fraud detection, and data-driven risk assessment.
The benefits of AI are now broadly recognized across insurance, and 7 out of 10 company leaders rank it among their top investment priorities. Accordingly, the market is expected to expand at a 28.4% CAGR from 2024 to 2030, potentially reaching about US$3.59 billion (see the KPMG report). This momentum reflects value at both sector and enterprise levels: industry benchmarks indicate up to 40% lower onboarding costs, 10–15% premium growth, and 3–5% higher claims accuracy (see McKinsey). At the enterprise level, carriers with mature digital and AI capabilities deliver 6.1 times higher total shareholder returns than laggards (McKinsey Digital).
Underwriting and claims are among the most transformed functions, and nearly half of global insurers now rely on AI to streamline these processes (Insurance Journal). Companies are already realizing impact; for example, a leading Asian insurer automated intake and decisioning—using OCR to scan and extract information, and AI to apply pre-set criteria and suggest payout options. Therefore, processing time fell to 2 seconds per request, down from 36 hours for underwriting and 2 days for claims (case study).
Beyond workflow automation, AI is advancing underwriting through richer, real-time risk assessment. Insurers are beginning to analyze unstructured data from IoT devices, customer behaviors, and lifestyle signals. According to the OECD, some carriers use machine vision and deep learning to build 3D models of property attributes and assign risk scores, and others combine earth-observation data with change-prediction analytics to improve pricing accuracy by about 20%.
In claims, the industry is progressing toward straight-through processing to deliver a fully digital, AI-driven experience for customers and assessors. One leading insurer partnered with FPT to build an end-to-end, automated claims solution, and the transformation is illustrated as follows.
The company’s claims process before:
The company’s claims process after:
At the same time, AI is strengthening fraud detection by spotting anomalies and inconsistencies in submissions, and by cross-checking documents and images against trusted data sources. One Asian insurer paired AI with encryption, regulatory compliance, and proactive threat monitoring; as a result, fraud detection rates increased by 50% and fraud-related losses fell by up to 15% (case study).
What to watch out for?
Insurers should watch for two critical constraints: entrenched legacy systems that silo data, and fast-evolving regulation that strains compliance cycles. Modernization unlocks AI’s value yet raises costs; however, AI can accelerate modernization itself. Therefore, pairing disciplined governance with an AI‑first partner helps mitigate risk and sustain momentum.
Against this backdrop, two watch-outs consistently determine the pace and payoff of AI in insurance, and they are closely intertwined.
1. Legacy systems:
If AI is a well‑oiled machine, data is the oil that keeps it running, and quality matters. Without high‑quality, sufficient data, AI yields limited value and can introduce bias. Insurers hold abundant data; however, much of it remains siloed because the industry relies on some of the oldest legacy systems.
Unlocking that value requires modernization, which raises near‑term costs. However, there is a productive loop: AI also accelerates legacy modernization. AI tools help engineers analyze legacy programming, generate replacements, and execute transformation with higher accuracy. For instance, FPT’s xMainframe can interpret and interact with legacy mainframe systems and COBOL code with up to 97% accuracy. Accordingly, Gartner predicts that by 2027 generative AI like xMainframe will explain legacy applications and generate replacements, potentially reducing modernization costs by as much as 70% [6].
2. Regulation & Compliance:
Insurance is among the most heavily regulated industries, and compliance is critical. Yet the rulebook is not static, and requirements are tightening as AI models advance. Systems must update at a comparable pace, and given their complexity, doing so frequently is challenging. Implementing a new regulation often entails impact analysis, development, testing across individual systems, and integration before go‑live.
This cycle can take two weeks to three months, creating risks of disruption or, worse, non‑compliance. FPT’s Smart Business Rule (SBR) solution addresses this by enabling rule updates directly on the SBR platform with no programming required, and once approved, rules synchronize across integrated systems. Therefore, implementation time can drop to as little as three days to two weeks.
AI-first insurance requires an AI-first partner
The future of insurance is undeniably AI‑first, and realizing that vision requires a partner that provides a clear roadmap, accelerates delivery, and ensures scalability. With its homegrown AI platform, FleziPT, FPT advances innovation through an AI‑driven development approach, award‑winning solutions, and a deep talent pool certified by global partners including NVIDIA, Microsoft, SAP, AWS, and Google Cloud.
Over more than 20 years, FPT has partnered with global insurers such as AIA, Prudential, Allianz, and more to advance their digital transformation and AI journeys. Learn more about FleziPT — FPT’s AI‑first platform here.
Conclusion
AI is reshaping insurance from underwriting to claims, but value comes only when foundations are ready. Prioritize clean, connected data, modernize legacy stacks, and embed human oversight to keep models trustworthy.
Move deliberately: start with high-impact pilots, measure outcomes, and scale with strong governance and change management. Organizations that align talent, technology, and regulation will turn experimentation into durable competitive advantage.
- Audit and unify data; retire or integrate legacy systems.
- Start with clear ROI pilots and expand in controlled phases.
- Establish model governance, bias monitoring, and human-in-the-loop checks.
- Upskill teams and align with evolving regulations.
Frequently Asked Questions
What are the current AI market trends and investment patterns in the insurance sector?
Seven out of ten insurance company leaders view AI as a top investment priority. The AI in insurance market is projected to grow at 28.4% annually over the next decade, reflecting widespread recognition of AI's transformative potential across the sector.
How is AI transforming the insurance industry and what should companies consider before implementation?
AI transforms insurance by streamlining complex workflows, delivering personalized customer experiences, and enhancing employee productivity. Companies should consider data quality, system integration capabilities, scalability requirements, and organizational readiness before implementing AI solutions.
What are the main obstacles insurance companies face when implementing AI systems?
Legacy systems create significant barriers by trapping valuable data in silos, preventing AI access to high-quality information. Poor data quality and insufficient data integration capabilities limit AI effectiveness and can lead to biased outcomes in insurance applications.
