Top 4 Cutting-Edge Technologies Automating Insurance Underwriting in 2024

Insurance underwriting is undergoing a high-tech transformation. By leveraging automation technologies like AI, RPA, APIs and advanced analytics, forward-thinking insurers are slashing underwriting times from days to minutes while also improving risk assessment accuracy. This article will explore the top 4 automation innovations revolutionizing insurance underwriting along with real world examples and adoption trends. Let‘s dive in.

AI and Machine Learning Unlock Speed and Precision

AI and ML tools excel at rapidly analyzing massive datasets and discovering correlations that enable sharp risk scoring for underwriting. We‘ll look at the key technologies in this category:

Advanced Analytics Crunch Claims Data

Advanced analytics utilizes sophisticated algorithms to find patterns in vast troves of structured and unstructured data. For instance, life insurer Cloverleaf leverages advanced analytics on thousands of data points about applicants including prescription history, driving violations and credit information. This enables Cloverleaf to automatically underwrite 95% of applicants and shorten underwriting decision times from weeks to 24 hours.

According to Willis Towers Watson, advanced analytics adoption is exploding with 95% of insurers planning to invest in it over the next 3 years. Cost savings on operations like underwriting average 19%.

NLP Extracts Insights from Documents

Insurance underwriting requires ingesting and comprehending vast documents like medical reports. For humans, this is tedious. But NLP models can rapidly digest the unstructured data in these documents and extract useful risk assessment insights.

For example, HyperScience uses NLP techniques like semantic search on years of underwriting guidelines and individual medical histories to instantly evaluate applicants.

According to MarketsandMarkets, the NLP market will reach $80 billion by 2028 as demand grows across insurance and other document-heavy sectors.

Computer Vision Scans Forms and Images

A subset of AI called computer vision utilizes deep learning models to analyze visual content like scanned documents, forms and images. This further expands underwriting automation.

For example, Swiss insurtech Stategik combines RPA bots with AI computer vision technology. This system handles over 200,000 unstructured documents annually to accelerate underwriting and other processes.

Per Fortune Business Insights, the computer vision market will grow from $11.4 billion in 2022 to $24.4 billion by 2029 driven partly by document automation use cases.

RPA Bots Excel at Structured Tasks

While RPA lacks the intelligence of AI, its bots shine at automating high volume, rules-based tasks common in underwriting such as:

  • Collecting data from internal systems
  • Populating forms
  • Checking for errors

For example, a top 10 global insurer applied RPA and AI to underwriting processes. According to Celent, this generated savings of 4,500 work hours per month worth over $2 million annually.

However, RPA alone struggles with unstructured data or dynamic decision making required in underwriting. It‘s most effective when combined with AI tools like computer vision or NLP. McKinsey finds the combination of RPA plus AI boosts cost reduction 2-3x compared to RPA alone.

APIs Connect Core Systems to External Data

Insurers rely on mountains of third party data from sources like medical labs, MVRs and credit bureaus to feed their underwriting models. Manually managing connections to these external data feeds is tedious and risky.

Instead, APIs allow seamlessly integrating core underwriting systems with outside data sources, IoT ecosystems and partner applications:

  • Guidewire offers a suite of underwriting APIs to quickly connect risk selection systems with useful data sources like credit scores and geocoding.
  • Startup Setoo uses APIs to ingest policyholder data from wearables and connected cars into its AI underwriting models according to Insurance Business Mag.

Per Mulesoft, 83% of insurers believe API-led connectivity is critical to underwriting automation initiatives. APIs minimize IT headaches while maximizing data leverage.

Advanced Analytics Unlock Big Data Potential

Previously insurers lacked the processing power to fully harness massive datasets for underwriting. But advanced analytics and AI models now allow insurers to rapidly gain insights from big data to improve risk assessment.

For example, PRA Group analyzes thousands of consumer data variables enabling 95% of life insurance applications to be automated with greater accuracy.

Megaliths like Swiss Re are also embracing big data analytics to understand complex risk factors like climate change and quickly translate them into commercial underwriting decisions.

The future potential is massive according to BCG, with big data analytics projected to lower underwriting loss ratios by up to 25% and acquisition costs by up to 40% in the next decade.

Over 75% of Insurers Actively Piloting Automation

The digital transformation of underwriting is accelerating quickly. According to Earnix, over 75% of insurers are currently piloting AI and analytics innovations to reinvent underwriting.

While thoughtful change management is required, those insurers who fully embrace these disruptive technologies will gain speed, productivity and risk scoring advantages over competitors still mired in legacy processes.

The future of insurance underwriting is automation. AI, RPA, APIs and advanced analytics will drastically shrink underwriting decision times while improving accuracy. Underwriters will shift their focus from processing paperwork to maximizing value from integrated automation systems.

Insurers wanting to lead the pack must start evaluating these transformative technologies now to get ahead of the curve. The automation revolution in underwriting has arrived.

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