How AI is Transforming Insurance Underwriting in 2024 and Beyond

Underwriting is the crucial process where insurance carriers assess risk profiles and determine pricing. But legacy underwriting practices are slow, inconsistent and reliant on limited data. This results in poor risk selection, inadequate pricing and lackluster customer experience.

Thankfully, artificial intelligence (AI) is coming to the rescue!

AI automates mundane underwriting tasks, enables data-driven decisions and delivers continuous learning. By deploying AI, insurers can operate with increased efficiency, deeper risk insights and greater profitability.

As an insurance professional, you likely grapple with the limitations of traditional underwriting every day. In this comprehensive guide, we will explore:

  • The challenges with current underwriting processes
  • How AI is transforming core underwriting capabilities
  • Real-world case studies of AI improving underwriting
  • Emerging techniques like graph neural networks and federated learning
  • Steps to get started with AI in your underwriting workflow

Let‘s get started and uncover how AI can revolutionize underwriting for your insurance business.

The Challenges of Traditional Underwriting

Let‘s briefly recap how traditional underwriting works before discussing its shortcomings.

The Manual Underwriting Process

Underwriting typically involves the following key steps:

  • Collecting documents like application forms, medical reports, financial statements from customers
  • Researching external data such as credit scores, inspection reports, motor vehicle records
  • Extracting relevant information through manual review of each document
  • Assessing risk based on rules developed from historical actuarial data
  • Determining pricing using standard rating factors
  • Making accept/decline decision based on guidelines and risk appetite
  • Issuing policy after approval

This is a time consuming and manual process relying heavily on the underwriter‘s individual judgement.

Key Challenges

The traditional underwriting process gives rise to several challenges:

  • Data collection is hampered by unstructured data in different formats like forms, emails, scanned images etc. Manually extracting data from these sources is expensive.
  • Risk assessment is constrained by limited data since manually gathering additional data has prohibitive costs.
  • Pricing is inefficient as standard rating factors fail to incorporate unique risk characteristics.
  • There is over-reliance on historical data even when risk patterns are changing.
  • Subjective human decisions lead to inconsistencies and biases in underwriting.
  • Lengthy turnaround times result in poor customer experience.

According to a Celent research report, 66% of insurers feel legacy underwriting processes pose a challenge to growth. Let‘s now see how AI helps insurers overcome these barriers.

How AI Improves Insurance Underwriting

AI and automation techniques can enhance efficiency, boost risk insights and improve decision making in underwriting.

Structured vs. Unstructured Data

One of the biggest pain points in traditional underwriting is dealing with unstructured data locked away in documents. AI can extract value from this unstructured data through:

  • Optical character recognition (OCR) to convert scanned images and PDFs into machine-readable text
  • Natural language processing (NLP) to identify relevant entities and concepts from text
  • Computer vision to assess damage from photos and videos

This eliminates the need for slow and inaccurate manual data entry.

Limited vs. Expansive Data

AI allows insurers to leverage a much wider range of internal and external data sources to assess risk:

Traditional DataAI-Enabled Data
Forms and applicationsIoT sensor data
Financial statementsSocial media posts
Motor vehicle recordsGEO-location analytics
Demographic factorsSentiment analysis
Actuarial tablesBiometrics and wearables data

With AI, underwriters are no longer constrained by what data can be manually collected and analyzed.

Rules-based vs. Data-driven Decisions

AI applies techniques like machine learning to uncover patterns across expansive data instead of relying solely on human-coded rules:

Rules-based DecisionsData-driven AI
Limited by constrained historical dataContinuously learns from new data
Static risk modelsDynamic predictive models
One-size fits all pricingPersonalized pricing
Human subjectivityAlgorithmic consistency

This shift from rules to data-driven AI yields superior results.

The AI-powered Underwriting Process

Here is how AI transforms the traditional underwriting process:

Manual ProcessAI-powered Process
1. Underwriter gathers documents from various sources and formats.1. Documents are ingested from channels into AI system.
2. Underwriter manually reviews documents to extract relevant information.2. AI automatically extracts information through OCR, NLP etc.
3. Underwriter consults limited data sources to assess risk.3. AI analyzes expansive structured + unstructured data.
4. Underwriter follows standard guidelines to price risk.4. AI models provide dynamic pricing tailored to unique risk profile.

The end result is faster, more accurate and consistent underwriting.

Business Impact

Adoption of AI in underwriting leads to:

  • 55% increase in underwriter productivity by automating manual tasks as per Gartner
  • Up to 32% improvement in loss ratios through superior risk selection as evidenced in research studies
  • 15% boost in policy conversion rates thanks to faster underwriting

According to a Novarica survey, 87% of insurers agree AI will transform underwriting performance.

Let‘s now look at real-world examples of insurers unlocking this value through AI.

AI in Underwriting – Case Studies and Examples

Leading insurance carriers are already piloting and deploying AI to enhance underwriting.

Swiss Re – Computer Vision for Claims Assessment

Global reinsurer Swiss Re built an AI system called Paperless Claims to automate analysis of photos capturing property damage.

The computer vision model can identify relevant areas of damage in a photo and estimate repair costs in seconds rather than days required for manual assessment.

According to Christian Mumenthaler, CEO of Swiss Re:

"We estimate that once fully implemented, Paperless Claims could save 10-15% of claims processing costs, making claims handling more efficient."

Automated image recognition delivers faster claims triaging and more accurate underwriting risk assessment.

John Hancock – Fitness Trackers for Underwriting

Insurer John Hancock provides customers applying for life insurance with a free Fitbit device.

Policyholders earn premium discounts for hitting fitness targets tracked via the wearable device.

This enables dynamic underwriting based on real-time health signals rather than relying solely on one-time medical checkups.

Marianne Harrison, CEO of John Hancock explains the benefit:

"This product enables underwriting in a much more dynamic way by allowing customers to demonstrate their healthy behaviors."

Real-time data from IoT devices gives insurers more accurate and individualized risk assessment.

Progressive – Usage-based Insurance with Telematics

Auto insurer Progressive uses its Snapshot telematics program to adjust underwriting and pricing based on actual driving behavior, as opposed to proxy factors like demographics.

Drivers install a monitoring device that tracks metrics like hard braking, nighttime driving, mileage etc.

Based on the telematics data, Progressive provides customized risk assessment and usage-based insurance rates.

As stated by CEO Tricia Griffith:

"We have found that actual driving behavior is the number one predictor of future crash likelihood."

Automatic data feeds enable continuous risk monitoring and dynamic pricing with AI.

Let‘s now look at some emerging techniques set to further advance AI in underwriting.

Emerging AI Techniques for Underwriting

The AI underwriting initiatives above are just early steps on the journey. Here are some bleeding edge techniques coming down the horizon:

  • Graph neural networks analyzing connections between entities to uncover hidden correlations that impact risk.
  • Federated learning collaboratively building risk models without exposing raw data, improving privacy.
  • Generative adversarial networks synthesizing artificial data to augment limited historical datasets.
  • Reinforcement learning optimizing sequences of underwriting decisions as an intelligent agent.
  • Explainable AI providing transparency into model logic to build trust.

As these emerging techniques mature, AI will propel underwriting to new frontiers.

Getting Started With AI in Underwriting

Below are some tips to begin your AI underwriting journey:

  • Start small: Pilot a narrowly scoped use case like automated data extraction from a specific document type.
  • Choose the right partner: Work with an AI solutions provider with deep insurance domain expertise.
  • Focus on ROI: Prioritize high volume automated tasks that improve productivity and loss ratios.
  • Evaluate AI maturity: Assess if data infrastructure, skill sets and executive buy-in are ready.
  • Take an agile approach: Refine solutions iteratively based on continuous feedback from underwriters.

The time for insurers to embrace AI in underwriting is now. Reach out to discuss your specific underwriting challenges and how AI can help overcome them: Get in Touch

Key Takeaways

We have covered a lot of ground discussing how AI can transform underwriting. Here are the key points to remember:

  • Traditional underwriting is manual, slow and relies on limited data.
  • AI automates mundane tasks like data extraction and analysis.
  • Expansive data leads to superior risk assessment and pricing.
  • Insurers are already seeing major benefits from AI adoption.
  • Emerging techniques will take AI underwriting to the next level.
  • Start your AI underwriting initiative today for accelerated growth.

AI is undoubtedly the future of insurance underwriting. I hope this guide has armed you with a detailed understanding of how AI can prepare your underwriting workflow for the data-driven era ahead!

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