The Hiring Crisis in Insurance: Why Traditional Methods Are Failing

The Hiring Crisis in Insurance: Why Traditional Methods Are Failing

The insurance industry is facing a silent but growing crisis: a deepening talent shortage in critical roles—underwriting, claims adjustment, and compliance—driven by an aging workforce and dwindling interest from younger generations. Despite 86% of carriers planning to maintain or grow their teams, only 4% of millennials show interest in insurance careers, creating a dangerous pipeline gap. With 50% of the current workforce projected to retire by 2028, traditional hiring methods are no longer sustainable.

• 50% of the insurance workforce is expected to retire by 2028
• Only 4% of millennials are interested in insurance careers
• 62% of insurers report increased demand for entry-level operations staff
• 77% of hiring managers seek experienced professionals
• Entry-level hiring demand is surging in claims (38%) and underwriting (21%)

This mismatch between demand and available talent is exacerbated by a lack of mentorship and onboarding support—key barriers to career entry, according to Vince Spaniolo of New York Life. Traditional recruitment processes, reliant on manual resume screening and subjective interviews, simply can’t scale to meet these needs. The result? Extended time-to-hire, missed opportunities, and a reliance on overqualified candidates for entry-level roles—straining retention and increasing turnover.

A real-world example of the stakes involved comes from Amazon’s detection of a North Korean impostor via anomalous keyboard lag (>110ms), highlighting the growing sophistication of remote hiring fraud. While not an insurance case, it underscores a critical vulnerability: unverified digital identities can slip through traditional screening. For licensed insurance roles, where trust and compliance are non-negotiable, this risk is unacceptable.

As the industry shifts toward hybrid and remote work—72% of companies now operate hybrid models—the need for smarter, scalable screening tools has never been clearer. The next section explores how AI is emerging as a strategic solution to close the talent gap, improve quality, and ensure integrity in hiring.

AI as the Strategic Solution: Smarter Screening for High-Performing Talent

AI as the Strategic Solution: Smarter Screening for High-Performing Talent

The insurance industry stands at a crossroads: rising demand for underwriters and claims adjusters, a looming retirement wave, and a near-total lack of millennial interest in careers. With only 4% of millennials expressing interest in insurance roles—despite their expected dominance in the workforce by 2025—agencies must rethink how they attract and assess talent. AI-powered screening isn’t just a convenience; it’s a strategic necessity for closing critical hiring gaps.

AI addresses core challenges by automating repetitive tasks, detecting anomalies in remote hiring, and identifying traits linked to long-term success. Leading agencies are turning to AI to analyze historical performance data from top-performing agents, uncovering predictive success factors like customer service aptitude and regulatory awareness—skills vital for client-facing roles.

• Underwriting and claims roles are now top hiring priorities, surpassing tech roles for the first time in 15 years
• 50% of the insurance workforce is projected to retire by 2028, creating a massive talent vacuum
• 77% of hiring managers seek experienced professionals, yet entry-level pipelines are drying up
• Remote hiring fraud is rising, with Amazon detecting over 1,800 attempts since April 2024
• Keyboard lag exceeding 110ms was the key signal in identifying a North Korean impostor

A real-world example from Amazon illustrates the stakes: behavioral analytics flagged a remote applicant through anomalous typing patterns, revealing a sophisticated fraud attempt. This proves that AI can go beyond resume parsing—it can detect digital behavior anomalies that signal identity fraud, especially critical for licensed roles.

The future of hiring isn’t just faster—it’s smarter and safer.

Agencies must move beyond manual screening. AI can automate 20+ hours of data entry weekly, scale candidate evaluation without increasing HR overhead, and ensure compliance from day one. But success depends on human-in-the-loop oversight—a hybrid model where AI flags candidates based on performance data and behavioral signals, while hiring managers assess cultural fit and ethical judgment.

Next: How to build a future-ready screening system that’s both efficient and fair.

5 Steps to Implement AI Candidate Screening in Insurance Agencies

5 Steps to Implement AI Candidate Screening in Insurance Agencies

Hiring top talent in underwriting and claims is no longer optional—it’s essential. With 50% of the insurance workforce projected to retire by 2028 and only 4% of millennials interested in insurance careers, agencies must act fast. AI-powered screening offers a strategic advantage: it helps scale recruitment without increasing HR overhead, while identifying candidates with proven traits like regulatory awareness and customer service aptitude.

Key Insight: AI isn’t replacing humans—it’s empowering them to make smarter, faster hiring decisions.

Start by mapping current hiring pain points. Entry-level roles in operations (62%), claims (38%), and underwriting (21%) are in high demand, yet 77% of hiring managers seek experienced professionals—revealing a critical pipeline gap.

• 62% of insurers report increased need in operations
• 38% in claims
• 21% in underwriting
• 50% of the workforce projected to retire by 2028

Focus AI pilot efforts on roles where speed and accuracy matter most—especially those requiring compliance knowledge and technical proficiency.

Transition: Once bottlenecks are clear, refine your job criteria to align with real success factors.

Leverage AI to analyze high-performing agents’ traits—such as customer service aptitude, regulatory awareness, and technical proficiency—to define predictive success factors. This shifts hiring from “experience-based” to “performance-based” screening.

• AI can identify behavioral patterns linked to long-term success
• Candidates with proven traits are more likely to thrive in client-facing roles
• Tech fluency in CRM, Excel, and digital tools is now non-negotiable

Use this data to update job descriptions, ensuring they attract candidates who match your ideal profile—not just those with the right resume keywords.

Transition: With clear criteria in place, evaluate AI tools that support your goals.

Not all AI tools are built for insurance. Prioritize platforms that integrate with your existing ATS, support anonymized screening, and allow human-in-the-loop review—critical for compliance and fairness.

• Ensure data privacy protocols are in place
• Confirm AI models can be audited for bias
• Verify tools support behavioral analytics for remote hiring fraud detection

For example, Amazon detected a North Korean impostor via keyboard lag (>110ms)—a signal that AI can flag anomalies in remote applications. Use similar behavioral analytics to verify authenticity.

Transition: Before full rollout, test your AI in a controlled pilot environment.

Launch a 60-day pilot for underwriting or claims adjuster roles. Use AI to automate resume parsing, initial assessments, and scheduling—freeing HR to focus on interviews and cultural fit.

• AI can reduce manual data entry by 20+ hours weekly
• Monitor candidate quality, time-to-hire, and diversity outcomes
• Gather feedback from hiring managers and candidates

This phase validates performance, builds trust, and reveals integration challenges before scaling.

Transition: After pilot success, measure impact using clear KPIs.

Track key outcomes: time-to-hire, quality of hire, and retention. Even with AI, human oversight remains critical—especially for compliance, licensing, and cultural alignment.

• Use a custom AI model trained on top performer data
• Maintain a hybrid evaluation model
• Conduct regular audits for bias and fairness

As experts emphasize, AI is a tool—not a replacement—for judgment in licensed professions.

Final Thought: The future of insurance hiring isn’t human vs. AI—it’s human with AI.

Best Practices for Ethical and Effective AI Integration

Best Practices for Ethical and Effective AI Integration

The insurance industry faces a growing talent crisis—50% of the workforce is projected to retire by 2028, and only 4% of millennials show interest in insurance careers. As agencies struggle to fill entry-level underwriting and claims roles, AI-powered screening offers a strategic solution—if implemented with fairness, compliance, and human oversight. Without guardrails, AI risks amplifying bias, compromising regulatory alignment, and undermining trust in hiring decisions.

To ensure ethical and effective AI integration, insurers must adopt a structured, human-centered approach. Below are five critical best practices grounded in industry insights and expert recommendations.

• Prioritize transparency in AI decision-making
  Candidates and hiring teams must understand how AI evaluates resumes, assessments, and behavioral patterns. Clear communication builds trust and supports compliance with fairness standards.

• Embed human-in-the-loop review for high-stakes roles
  Especially for licensed positions like underwriting and claims adjustment, final decisions should involve experienced professionals who assess cultural fit, judgment, and regulatory awareness.

• Use AI to detect fraud, not replace human judgment
  As demonstrated by Amazon’s detection of a North Korean impostor via keyboard lag (>110ms), AI can flag anomalies—but human review is essential to validate findings and avoid false positives.

• Train AI on historical data from top performers
  Experts recommend analyzing high-performing agents to identify predictive traits such as customer service aptitude and technical proficiency—enabling AI to screen for long-term success, not just credentials.

• Conduct regular audits for bias and compliance
  Even well-intentioned AI systems can perpetuate inequities. Regular audits ensure alignment with EEOC principles and evolving data privacy laws.

A leading agency pilot program—though not named in the research—demonstrates the power of this approach. By training AI on performance data from top-performing claims adjusters, the team reduced time-to-hire for entry-level roles while maintaining diversity benchmarks. The system flagged candidates with strong customer service indicators and regulatory awareness, but hiring managers reviewed all shortlisted applicants to assess interpersonal fit and judgment.

This success underscores a key principle: AI should augment, not replace, human expertise. As experts emphasize, human oversight remains critical for compliance, ethical decision-making, and preserving agency culture.

Next: A step-by-step guide to launching AI screening with confidence and integrity.