The Top 7 Cutting-Edge Technologies Transforming Insurance Underwriting in 2024

Hey there! As an insurance industry analyst, I‘m excited to walk you through the top 7 technologies that are completely transforming underwriting in 2024. These innovations in data analytics, connectivity, automation and more are enabling insurers to operate with newfound speed, accuracy and profitability.

The key technologies we‘ll explore are:

1. Advanced Analytics on Structured Data
2. Natural Language Processing (NLP)
3. Optical Character Recognition (OCR) & Handwritten Character Recognition (HCR)
4. Digital Twins
5. Internet of Things (IoT)
6. Application Programming Interfaces (APIs)
7. Blockchain

Now, let‘s dive into each one and see how it‘s disrupting underwriting!

Why Underwriting Needs Cutting-Edge Technology

First, what exactly is underwriting? Simply put, underwriting is the process of evaluating risk to determine appropriate premiums for insurance policies. Underwriters calculate the likelihood and impacts of potential losses, then sell appropriately priced policies to cover events that meet pre-defined criteria.

Effective underwriting allows insurers to:

• Accurately price policies to optimize profits
• Maintain a balanced, diversified portfolio
• Operate at the speed customers demand

However, many insurers struggle with razor-thin margins. According to McKinsey, the average return on surplus (ROS) in the industry is 11.6%. But most firms fall under the 11.3% median return, with only the top quintile achieving substantially higher profits.

This spread highlights a pressing need to enhance underwriting practices. Let‘s look at how leading insurers are leveraging technology to transform speed, profitability and customer experience.

1. Advanced Analytics for Precision Underwriting

Advanced analytics refers to sophisticated statistical and machine learning techniques that uncover patterns within structured data. When applied to the trove of information insurers collect – like age, health, lifestyle, finances – these techniques can automate and improve risk assessment.

Predictive analytics, artificial intelligence and robust data science enable insurers to:

• Price policies with precision – By analyzing correlations between variables in historical datasets, predictive models can estimate loss probabilities for new applicants with striking accuracy. This prevents under or overcharging for policies.
• Optimize portfolio diversification – Advanced analytics also facilitates portfolio strategies that minimize overall risk. Techniques like mean-variance modeling and robo-advisors help diversify across demographics, geographies and other dimensions.
• Detect fraud earlier – Behavioral analytics reviews past digital activity to identify markers of fraudulent behavior. This allows insurers to incorporate fraud risk into underwriting and pricing decisions.

McKinsey found that analytics leaders achieve up to 30% greater underwriting returns. Adoption is still slow though, with most insurers only using basic regression modeling.

Implementing enterprise-wide data science capabilities requires investments in technology, talent and transformation. The long-term benefits of precision underwriting warrant the effort for insurers willing to embrace analytics.

2. Natural Language Processing: Making Sense of Documents

While analytics models work with structured data, natural language processing (NLP) extracts insights from unstructured text and audio. NLP uses linguistic rules and machine learning to analyze relationships between words and assess meaning.

In underwriting, NLP can be applied to:

• Questionnaires
• Doctors‘ reports
• Call center transcripts
• Online forms
• Social media posts
• Other text-heavy documents

By categorizing this data and discerning correlations, NLP identifies risk indicators that humans might miss. This improves pricing accuracy without requiring manual document review.

For example, certain phrasing on medical reports could suggest increased substance abuse risk. Analyzing syntax and sentiments in social media posts might indicate depression. By feeding NLP outputs into other underwriting models, insurers can incorporate insights from documents that previously sat untouched in archives.

However, NLP has its challenges. Accuracy depends heavily on training data quality. Interpreting complex human language also remains difficult for machines. But advances in contextual learning are making NLP a more viable underwriting tool.

3. OCR & HCR: Extracting Information from Documents

While NLP analyzes document text, optical and handwritten character recognition technologies allow underwriters to digitize printed and handwritten documents.

• OCR – Optical character recognition software detects text in scanned or digital documents and converts it into editable, searchable formats.
• HCR – Handwritten character recognition does the same for handwritten input, which is much harder to decipher.

These technologies alleviate the need for manual data entry from paper applications and claims documents. For some insurers, over 50% of underwriters‘ time is spent on manual data entry – a frustrating drag.

OCR and HCR enable underwriters to:

• Automate information extraction into structured databases
• Reduce repetitive manual tasks
• Unlock handwritten and printed documents for NLP and analytics

However, character recognition accuracy depends on factors like penmanship, font choices and scan quality for printed materials. Human review is still needed to verify results and handle exceptions.

Overall though, OCR and HCR deliver immense time savings that allow underwriters to focus on value-added analysis. They also expand insurers‘ datasets for other techniques.

4. Digital Twins: Simulating the Future

Digital twins are virtual representations of people, businesses or objects. They allow insurers to simulate countless "what-if" scenarios to stress test underwriting strategies.

Some important applications include:

• Portfolio simulation – Testing geographical, demographic and other diversification strategies against catastrophe scenarios helps insurers evaluate and optimize their overall risk balancing.
• Product evaluation – Simulating new coverage options provides insurers data to decide which products to invest in.
• Catastrophe planning – Modeling extreme events with low historical precedence, like 100-year floods, improves disaster preparedness and pricing.

By generating synthetic datasets, digital twins provide valuable insights not contained in historical data. Leading insurance twins model are calibrated with real-world data and physics to closely mirror reality.

As duals become more photorealistic and higher-fidelity, their value for underwriter planning and training grows. But development requires massive data inputs and computing power. Despite the investment needed, digital twin simulation enables more resilient underwriting.

5. Internet of Things: More (Real) Data

While digital twins synthesize scenarios, the Internet of Things (IoT) collects vast amounts of real-time data from smart devices and sensors embedded in the world around us.

IoT enables dynamic, usage-based coverage that aligns premiums with policyholders‘ actual monitored risk profiles. Sensors transmit data like:

• Health stats from wearables
• Vehicle telemetry
• Home climate readings
• Business facility operations

This real-world data powers the advanced analytics engines discussed earlier. More diverse, granular data leads to more accurate underwriting decisions.

IoT also facilitates pay-as-you-live and behavior-based insurance. Customers receive financial incentives to minimize risks, saving costs for both parties. For instance, discounts for:

• Driving safely
• Maintaining healthy biometrics
• Following physician care plans
• Adopting cybersecurity measures

However, IoT also multiplies exposure to data breaches and system outages. Insurers must implement robust cybersecurity and resilience measures to capitalize on IoT‘s benefits while minimizing risks.

6. APIs: Seamless Data Sharing

Application programming interfaces (APIs) enable seamless data transfer between underwriting platforms and external systems. They eliminate friction in transmitting data stored across various locations and formats.

APIs allow underwriters to efficiently leverage both internal and third-party data sources like:

• Public records
• Financial information
• Smart home data
• Health records
• Transportation databases

This optimized data orchestration maximizes underwriters‘ ability to incorporate diverse, far-flung information within their models.

Forrester predicts insurers that master external data consumption through APIs and cloud platforms will improve loss ratios by up to 15%. But normalization, governance and monitoring are required to ensure quality.

Overall, well-managed APIs create a robust data supply chain that empowers underwriters.

7. Blockchain: Privacy and Automation

Blockchain offers a transformative way to share information while preserving privacy – a perfect fit for the highly sensitive data insurance underwriting relies on.

Blockchain‘s cryptography and consensus mechanisms enable insurers to:

• Preserve confidentiality – Encrypted, permissioned blockchains allow only authorized underwriting systems to access sensitive information like health records. This maintains privacy while permitting analysis.
• Automate with smart contracts – Programmable smart contracts can automate approval and payment for pre-defined insurance events. This accelerates claims assessment and settlement.
• Share data seamlessly – Once data is logged on the blockchain ledger, all permitted parties gain access. This streamlines information sharing without giving up control.

By negating some third-party involvement, blockchain also reduces administrative costs associated with underwriting and claims. However, blockchain networks require tremendous coordination and standardization to deliver value at scale.

Carefully implemented, blockchain can transform data exchange and automation in underwriting. But insurance-grade solutions are still emerging.

The Future of Insurance Underwriting

These seven technologies represent powerful opportunities to upgrade underwriting practices. Leaders harnessing analytics, automation and connectivity will unquestionably write the industry‘s future.

But modernizing underwriting requires more than just adopting tech. Insurers must rethink processes, talent models, partner ecosystems and organizational culture. Fortunately, the staggering benefits of underwriting innovation warrant this degree of transformation.

I hope this overview gets you excited about the future of 21st century insurance underwriting. Reach out if you want to dive deeper into any area or discuss how these technologies apply to your insurance business specifically. The underwriting revolution is here – let‘s talk about seizing it!