The line between groundbreaking technology and a passing fad is often drawn by a single factor: the ability to solve real-world problems. On which side of that line will artificial intelligence (AI) in insurance ultimately fall?
Consider the cautionary tale of the Segway. Launched in 2001 with grand promises of revolutionizing personal transportation, it failed to gain widespread adoption because it was simpler and cheaper to walk or ride a bicycle. Thus, it was a product with no real problem to solve.
The smartphone stands in stark contrast. When the first iPhone was introduced in 2007, it solved multiple problems simultaneously, combining communication, internet access, photography, and personal computing in a single, portable device.
Truly useful technology must solve tangible problems. Does AI address daily challenges faced by underwriters?
This article delves into six common challenges life underwriters face and examines how AI fares in addressing them. Through this analysis, it becomes clear that AI is already a powerful tool solving real underwriting problems. In doing so, it is transforming the insurance industry.
The scope of AI's inroads
By early 2023, more than 40% of life insurers had adopted or planned for some level of AI in their underwriting process, an increase of 28% in just one year. Additionally, 70% of insurers globally now use an automated underwriting system (AUS) to some degree.
These systems provide measurable benefits.
The message is clear: AI in life underwriting is not just coming; it is here, and it is delivering results.
Yet as AI reshapes the process, it is important to remember what remains unchanged: the central role of the human underwriter. Underwriting is ultimately about judgment, ethics, and human-to-human assurance. AI is not about replacing life underwriters; it is about empowering them and further increasing their efficiency. Think of AI as an analyst that works 24/7, sifting data and spotting patterns, allowing senior underwriters to focus on critical decisions and client needs.
Thus, AI is already looking more like a smartphone and less like a Segway. For those who have yet to capitalize on the AI opportunity, let's dive deeper.
6 challenges (and hidden opportunities) for life underwriters
1. Fragmented data leads to information overload
The details – An underwriter often has to compile data from a medical exam report, Rx prescription history, motor vehicle record, and attending physician statement, each arriving in a different format. That forces an underwriter to flip among an emailed PDF, a webpage, and antiquated faxes. Important details are easily missed, and it takes hours to create a full picture.
The opportunity – This data chaos begs for aggregation. AI, acting as a digital assistant, can digest data from all these sources and present a unified, searchable summary of an applicant's health and history. Underwriters then spend less time playing detective and more time making decisions.
2. Lengthy manual processes are slow and labor intensive
The details – Traditional life underwriting can involve a slew of manual tasks, including retyping information from PDFs and handwritten notes into a system, eyeballing hundreds of pages of medical files, and checking off requirements one by one. This is tedious work that delays policy decisions and leads to inconsistencies and mistakes in data entry.
The opportunity – AI and automation tools can take over repetitive tasks – deciphering messy handwriting using optical character recognition, auto-filling application data, and triggering routine decisions. By automating evidence collection and data entry, AI can cut weeks of waiting time. The machine does the rote clerical tasks in seconds, and underwriters can focus on reviewing complex cases and important outcomes. This results in faster decisions and a smoother experience for customers and underwriters.
3. New data means new challenges in an evolving risk landscape
The details – Life underwriters now have to contend with a variety of new evidence types, from genetic testing results and the long-term effects of COVID-19 to the proliferation of wellness data from wearables and much more. Underwriting guidelines struggle to keep up with these emerging risks and data sources. What should be done if an applicant submits a genetic test showing a predisposition to a certain cancer? How should an underwriter interpret two years of Fitbit data showing 10,000 steps a day? These weren't questions underwriters faced even a decade ago. Today, they are, and the data proliferation is only accelerating.
The opportunity – AI shines by finding patterns in large datasets. Machine learning models can incorporate novel data to discover how they correlate with risk. Instead of guessing, underwriters are gaining genuine insights. For example, AI might learn that a certain activity level corresponds to a 5% lower mortality risk, which could justify a better rate class. AI can continuously update risk models as new outcomes and medical research emerge, which means underwriting decisions stay data driven and current. Thus, AI helps underwriters adapt quickly in a changing world in ways basic rules alone cannot.
4. Detecting fraud is becoming more challenging
The details – Evolving insurance processes have created new opportunities for fraud and misrepresentation. Life underwriters do have red-flag rules that can find inconsistencies between reported weight and a medical exam, for example, but sophisticated fraud slips through too frequently. Meanwhile, blunt rules can also cast a net far too wide, flagging honest customers and slowing down their applications. Fraud remains a significant and growing challenge, and catching the truly risky cases without inconveniencing the many honest ones is only getting more difficult.
The opportunity – AI excels at pattern recognition and can dramatically improve risk integrity. AI-based anomaly detection can sift through thousands of data points to find subtle signals of potential misrepresentation – combinations of factors that humans cannot easily spot. For example, an algorithm might notice that an applicant's provided phone number and email were linked to past suspect cases or that the medication history does not quite match the medical records. It then quickly gives a "fraud likelihood" score. Legitimate applications sail through, while the few truly suspicious ones are flagged early for deeper review.
5. Customer demand speed and personalization
The details – In the age of one-click purchases and instant loans, consumers expect buying life insurance to be fast and tailored. Every extra day and each unnecessary test jeopardizes new-business acquisition. Traditional underwriting, with its blood test requirements and multiple weeks of review for even moderate coverage amounts, seems archaic to the potential customer, who also expects to feel understood. For example, a healthy 30-year-old applicant might wonder, "Why am I being asked about conditions that only affect older people?" Applicants often will drop out if they encounter friction or will opt for a competitor who offers instant, algorithmic underwriting.
The opportunity – AI enables accelerated and personalized life underwriting. Predictive models can identify low-risk applicants to approve instantly or with minimal questions, bypassing invasive tests. For those who do need extra checks, AI helps tailor the process, asking only relevant questions and even enabling them to interact through chatbots that create a personalized and engaging experience. Moreover, AI can help recommend the best products or riders for each customer by analyzing what similar customers have chosen. The overall experience becomes faster and more user-friendly, while underwriters are freed to dedicate time to more complex cases.
6. The web of regulatory and ethics requirements is increasingly complex
The details – Privacy laws such as HIPAA and GDPR dictate how insurers must handle personal health data. Anti-discrimination regulations ensure insurers don't unfairly use factors such as race or genetics. With AI and more data sources, a new dimension of compliance is emerging. Insurers must ensure the algorithms themselves are fair and transparent. They rightly are asking whether their AI models are biased against certain groups and how they can explain AI-driven decisions to regulators. This is made more challenging because evolving regulations vary by region. What is allowed in one country is sometimes forbidden in another.
The opportunity – Modern AI auditing tools can check underwriting models for bias, and natural language processing can monitor decision rationale to ensure compliance. Tools such as SHAP (SHapley Additive exPlanations) or LIME (Local Interpretable Model-agnostic Explanations) can show which evidence and disclosures influenced a decision. AI systems such as MLflow can log every step of the decision-making process, creating a transparent record for regulators. A second AI-driven program can scan a decision to confirm that no unapproved data from a specific region influenced the outcome. AI can also help with data anonymization, ensuring any data used to train models is stripped of personal identifiers and is more consistent, compliant, and audit friendly.
Every challenge is an opportunity for innovation. By leveraging AI, insurers can turn fragmented data into holistic insight, tedious processes into automated workflows, evolving risks into sophisticated models, fraud detection into real-time scans, customer expectations into personalized journeys, and compliance hurdles into improved operations.
For as many core challenges as an underwriter may face, an AI-powered solution is either already available or is in development. The next step – and the real art – is putting all these solutions together in one seamless underwriting process.
The exciting part? This transformation is just getting started, and everyone – carriers, underwriters, and applicants – stand to win.
