Insurance is sometimes described as a “decision chain.” Decision-making in underwriting affects the speed and accuracy of risk assessment and policy pricing. Effective decision-making in claims ensures prompt, accurate payments to make customers whole.
Eighty-two percent (82%) of insurance leaders recognize AI as a pathway to empowering decision-making across their business’s financial and operational performance. Those that have successfully adopted AI are creating new standards for operational efficiency, significantly improving decision-making accuracy, recruiting and retaining the best talent, and delivering outstanding customer experiences.
As you read this, traditional, non-traditional, and even non-insurance brands are using AI to power growth via more customer-centric insurance products and services.
- AI helps Lemonade boost revenue across all operations areas and delight customers by providing competitive pricing in minutes-- and claims settlements within seconds
- Tesla’s insurance brand uses AI to leverage telemetric driver data for real-time risk scoring
- Medical malpractice insurer ProAssurance has deployed AI to answer customer questions, assist agents, and streamline fraud detection
- And global insurance giant Liberty Mutual equips its teams with AI to drive efficiency, resulting in average time savings of 1.5 hours a week across each of its 45,000 employees
At the same time, many insurers are missing out on AI’s benefits, relying on legacy technology and manual effort to automate processes and manage critical decision data. To close the AI gap separating themselves and the competition using AI, these businesses must undergo a similar transformation. Modern AI solutions for insurance can streamline this transformation to strengthen the insurance decision chain.
Now is the time to act to build your advantage. Let’s explore 12 ways, within the following three groups, adopting AI can help your business compete and win in today’s insurance competitive landscape.
1. Increased Productivity
Insurance relies heavily on data for risk assessment/pricing and claims management. The vast majority of data leveraged across the industry is unstructured—which means they’re locked within emails, PDFs, faxes, handwritten notes, and other documents.
Even today, much of this data is processed manually by underwriters and claims managers. AI automates the extraction of critical data to boost productivity across underwriting, claims management, and policy servicing functions.
- Unlocking Information from Unstructured Data - In underwriting, AI processes unstructured data in agent and broker submissions faster and more accurately than manual effort, letting underwriters focus on what they do best – assessing risks and determining optimal policy pricing. In claims, AI automates FNOL processing by extracting and analyzing relevant policyholder and incident reports to support efficient and accurate claims decisions. By automating these and other functions, AI substantially reduces processing time, giving human experts more time to focus on high-value work rather than administrative tasks.
- Utilizing Straight-Through Processing
o In underwriting – AI risk assessment enables automation of standard, lower-complexity submissions, allowing up to 80% of these forms to process straight through without human intervention. In a line of work where the fastest quote wins business, insurers implementing straight-through processing have reduced quote turnaround times from days to minutes.
o In claims – AI-powered systems can process 99% of routine claims information straight through, significantly reducing the time to process these documents. For many applications, AI automates claims validation and fraud detection to reduce processing time from days to minutes while cutting operational costs.
- Better Decisioning – Underwriters and claims managers make hundreds of decisions every day on whether to underwrite risks and how to price them. Decisions like these have a significant impact on an insurer’s financial performance. And all of them rely on the quality and usability of available data. AI transforms unstructured data into actionable insights that empower better, faster, more accurate decisions to generate more revenue and reduce claims overpayment and other types of revenue leakage.
2. Greater Profitability
As insurance emerges from a persistent hard market, insurers look to more accurate underwriting risk assessment and efficient claims handling processes to restore profitability. AI has played a key role in helping successful insurance businesses leverage data to improve risk assessment, optimize claims management, and capture new business while retaining and expanding existing customer relationships as the industry enters a soft market.
- Reduced Loss Ratio – AI improves insurance loss ratios by enabling more accurate risk assessment through predictive modeling. This allows insurers to identify high-risk policies and price them appropriately. For claims teams, AI reduces claims leakage by proactively managing potential claims through fraud detection, early intervention strategies, and optimized claims handling processes, ultimately leading to more accurate payouts.
- Increased Capacity/Efficiency – Using AI to surface decision data faster and more accurately significantly shortens time-to-quote and claims settlement timelines. By reducing workloads year-round, AI supports claims teams in managing demand spikes during CAT season and other peak times—without increasing headcount or relying on outsourcing work.
- Improved Customer Success – Underwriters spend approximately 40% of their work time processing and reworking unstructured data and performing other non-core functions. Claims leaders report spending nights and weekends processing forms at home. AI streamlines these traditionally labor-intensive processes by automatically gathering and analyzing relevant data faster for rapid quote turnaround and claims payments. Better customer outcomes enhance brand reputation and NPS to help retain existing customers and attract new business.
- Personalized Risk Assessment – AI’s ability to analyze vast amounts of data helps to create more accurate individual risk profiles, allowing insurers to offer more competitive pricing to lower-risk customers while maintaining appropriate rates for higher-risk ones.
3. Better Workforce
Insurance is the midst of a persistent talent crisis, facing the need to replace a greying workforce across all operations areas, half of which will age out by 2040.
Insurance people are passionate about their work. More than 3 million people across the US—any self-described “insurance nerds”—choose this industry for their love of problem-solving and desire to make businesses and communities safer and more resilient. AI workflow automation delivers a vital competitive edge in recruiting, developing, and retaining talent by boosting job satisfaction and supporting a better work-life balance.
- Reduce Workload and Employee Burnout – Repetitive administrative tasks increase workloads, leaving less time for higher-value tasks. The heavy reliance on manual processing in insurance work leads to burnout, higher turnover rates, and difficulty retaining skilled underwriters and claims experts. AI eliminates up to 99% of manual work in key underwriting and claims areas to effectuate a better work/life balance.
- AI to Support Underwriting Talent – AI is a critical link to making insurance work more productive and fulfilling. AI-powered data processing and workflow automation let underwriters focus on core responsibilities. AI creates value for policyholders with accurate, more transparent pricing and easier renewals. Eliminating or significantly reducing data processing allows underwriters to focus on building stronger relationships with brokers and agents.
- AI to Support Claims Talent – From mailrooms to claims managers’ desks, manual data processing and analysis significant productivity loss. AI-powered claims processing automates routine tasks, allowing claims professionals to focus on complex cases requiring human judgment. AI also eliminates manual data processing, reducing overtime work and burnout caused by demand spikes.
- Strengthen Reputation for Recruitment – There are more than 125 major insurance-themed conferences annually. The top five LinkedIn insurance groups have more than 48,000 members combined. Insurance people are a passionate bunch, so word gets around fast—both good and bad. AI supports reputation-building by delivering faster, more responsive and personalized customer service. AI adoption also displays a deep commitment to innovation and supporting teams in creating business and customer success.
Companies adopting AI are transforming their operations and workforce. Even in an industry that’s reluctant to change, insurance is rapidly embracing AI for its remarkable capabilities. Businesses that fail to adopt artificial intelligence risk falling further behind competitors already using AI in production.
Today, AI is emerging as the default solution for efficiently extracting actionable information from data for effective insurance decision-making. This technology is remaking the insurance competitive landscape, and companies that haven’t made the leap could be in danger of falling behind.
To learn more about the state of AI adoption in the insurance industry and get key insights and best practices for driving value from your AI investment, download a copy of our annual report.