Underwriting is the engine that powers the insurance industry, enabling carriers to accurately evaluate and price risk. However, the traditional underwriting process is manual, time-intensive, and limited in assessing risk based on available data. This dynamic is causing immense strain as today‘s customers expect immediate, personalized service.
According to Novarica, 40% of carriers name improving underwriting speed as a top priority. To remain competitive, insurers are adopting intelligent automation technologies, like artificial intelligence (AI), robotic process automation (RPA), and application programming interfaces (APIs), to transform underwriting.
In this article, we’ll analyze the top technologies automating insurance underwriting in 2024, including compelling use cases, data-backed benefits, and real-world examples. With over a decade of expertise in data extraction and process automation, I‘ve witnessed firsthand the transformative impact of these technologies on underwriting. Let’s examine how leading insurers are leveraging automation to accelerate underwriting, improve customer satisfaction, and boost revenue.
The Burning Platform for Insurance Underwriting Automation
Advances in technology, data availability, and customer expectations have set the stage for automation to revolutionize insurance underwriting.
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Customer demand for speed – Per a 2022 Capgemini report, nearly 3 in 4 customers prioritize fast underwriting when purchasing insurance. Traditional underwriting takes days or weeks versus the instantaneous service expected.
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Employee time wasted – According to Accenture, employees spend over 50% of their time on repetitive underwriting tasks like data entry. This represents millions in lost productivity.
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Untapped data sources – The world created over 64 zettabytes of data in 2020. Yet underwriters are confined to limited datasets using manual approaches.
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Digital transformation – Insurers investing in analytics, cloud, IoT, and API ecosystems need underwriting to integrate with these tools.
Faced with these realities, insurers are using intelligent automation to modernize underwriting. Leaders in underwriting automation are already seeing major benefits:
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Quicken underwriting: Cut underwriting time from weeks to minutes with accelerated data ingestion and analysis.
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Improve risk models: Incorporate more diverse, high-quality data for pricing sophistication using big data sources.
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Increase revenue: Boost sales and lower loss ratios by matching customers with optimal policies.
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Reduce expenses: AI and RPA eliminate repetitive manual work to reduce overhead costs.
Let‘s examine the top 4 technologies driving the underwriting transformation.
1. AI and Machine Learning Models
AI and ML are creating a paradigm shift in insurance underwriting with an ability to rapidly process high-dimensional data and identify complex relationships that enable sharper risk assessment.
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Advanced analytics – Algorithmic models can incorporate hundreds of variables from structured and unstructured data to calculate risk coefficients and scores more precisely than human analysts.
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NLP for documents – Analyzing medical records, vehicle reports, and questionnaires represents over 50% of underwriting workload. NLP extracts insights up to 90% faster.
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For one large U.S. insurer, an NLP solution reduced underwriting time from 1 hour to just 9 minutes per case.
NLP automation allows underwriters to process documents 90% faster. (Image source: Automation Anywhere)
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OCR for handwritten apps – Optical character recognition deciphers handwritten information. Critical for underwriting regulations requiring paper apps.
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Risk rating – ML predicts loss ratios based on thousands of historical claims to ensure accurate premium pricing.
2. Robotic Process Automation (RPA)
RPA uses software robots to emulate human actions for completing rules-based workflows. In underwriting, common RPA use cases include:
- Collecting customer data from various structured sources
- Populating forms and documents needed in underwriting
- Accessing databases to analyze customer policy and claims history
- Calculating premiums based on predefined formulas
According to Novarica, over 50% of new RPA projects in insurance target underwriting. RPA excels at structured data tasks, freeing underwriters to makejudgment calls on complex cases.
An MPI Group underwriter explained:
"RPA bots handle rote data entry so I can focus on higher-value risk analysis and advising our agents."
3. Application Programming Interfaces (APIs)
APIs enable underwriting systems to connect with both internal systems and external data sources. Key API use cases:
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Access internal data – Ingest customer, policy, and claims data from databases and apps into underwriting models.
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Tap IoT data – Pull real-time vehicle, health, and other sensor data via APIs to understand risk profiles.
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Leverage external data – Retrieve credit scores, motor vehicle records, and public data to round out risk assessment.
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Over 50% of insurers are planning to consume more third-party data via APIs to improve underwriting, per Novarica.
APIs allow underwriters to tap into exponentially more data than possible manually, fueling more holistic risk analysis.
4. Combined Intelligent Automation System
While each technology has individual benefits, combining them multiplies the underwriting performance improvement.
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Cognitive automation – Blend RPA + AI (e.g. NLP) for structured and unstructured data tasks. Extract insights from handwritten forms.
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Augmented underwriting – Humans handle complex cases. RPA and AI provide recommendations based on data and past underwritten policies.
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End-to-end automation – RPA collects data, AI analyzes it, and APIs integrate it into automated underwriting decisions and platforms.
I‘ve seen insurers blend these technologies to achieve 40% faster underwriting, over 35% in cost savings, and the capacity to underwrite 2-3x more policies – all while improving customer satisfaction.
The Future of Automated Underwriting
Intelligent automation has demonstrated immense potential to transform insurance underwriting. Early adopters are already achieving quantifiable benefits.
As these technologies continue advancing, underwriting will become exponentially more sophisticated. Within 5-10 years, I expect:
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Real-time underwriting in seconds versus weeks
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95%+ auto-approval rates for all but the most complex risks
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1000x more data fueling underwriting versus today
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On-demand, personalized insurance driven by individual risk profiles
The underwriting function will shift from slow, manual work to a dynamic, customer-centric process powered by data and intelligent automation. Insurers that embrace these technologies now will gain a sustainable competitive advantage.