Real-World Data Science Use Cases in the Insurance Industry



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The Insurance Value Chain

Insurance is defined as a contract (aka policy) undertaken by an insurance company where an individual or entity receives financial protection or reimbursement against specified losses.

A value chain refers to a company’s activities in delivering value to the end customer. Although there are many types of insurance (e.g., life, health, commercial), they share similar activities across the insurance value chain.

The flowchart below illustrates the six aspects of the insurance value chain, forming the basis for today’s discussion.

Insurance value chain | Image by author

(1) Product Development

Photo by Rachit Tank on Unsplash

In the age of personalization, flexibility, speed, and connectivity, insurers are modernizing their product range to meet the expectations of consumer-centric offers.

This shift involves moving from product-driven to approaches driven by customer experience and data.

(i) Behavioral Economics — Health Telematics

  • Insurers like Blue Shield Blue Cross and John Hancock Financial launched health and wellness programs that track customers’ fitness data via smart wearables. Customers benefit through rewards (based on activity levels) and nudges towards healthy habits. Furthermore, insurers can leverage a more robust understanding of the consumers’ lifestyles to provide personalized offers and tailored services through better risk and incentive ML models.

(ii) Usage-based Insurance with the Internet of Things

  • Progressive’s Snapshot program personalizes auto insurance rates based on driving behaviors, meaning that customers pay based on how and how much they drive instead of just the traditional factors (e.g., age, gender). Customers with safe driving habits stand to receive discounted premium rates.
Information flow in auto telematics | Image from Verisk
  • While telematics is not new, developing and integrating new IoT embedded sensors have helped generate richer and larger volumes of data for more accurate modeling.
  • The utility of IoT extends beyond the auto business and into other areas (e.g., smart property surveillance by Neos) for better insights, risk assessment, and customer engagement.

(2) Marketing

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Because customers are increasingly digitally savvy with demands for customized offerings, undifferentiated marketing campaigns are no longer that effective.

This trend is also why insurers are taking the approach of personalized digital marketing, which involves reaching the right customers at the right time with targeted online messaging and pricing.

Such optimized marketing campaigns drive robust customer engagement and strong returns on marketing spend.

  • Coverfox tracks website usage to offer personalized online advertisements, thus helping to boost visibility and recall via highly relevant messages. One feature is that when a user drops off the website after taking a quote, the system captures the quote level data (e.g., car model) to serve follow-up customized advertisements.
  • Income leveraged advanced segmentation and machine learning (ML) developed on data from website traffic and digital interactions to improve marketing effectiveness. The targeted campaigns based on customer segments reduced acquisition costs and increased click-through rates.

Customer Segmentation

  • A US national insurer utilized Spatial AI’s PersonaLive segmentation system to personalize their current marketing touchpoints and strategically expand their audience, resulting in higher open rates and conversion rates. The insights were critical in identifying untapped, high-spending customer segments, as well as segments with high churn rates.

Life-Event Marketing

  • As high-impact life events (e.g., having a child) motivate purchase, a leading insurer used Speedon’s Marketing Solution to identify customers at crucial life stage events and provide them with promotional offers. These campaigns driven by life stage triggers helped the insurer generate over 4,000 new and retained policies.

(3) Distribution and Sales

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While there are varying insurance distribution channels (e.g., agency, brokers, direct), the key goal across the board is to efficiently sell the right products to the right customers.

Cross-Sell/Up-Sell Lead Generation

  • A leading insurer worked with Bain & Company to launch cross-selling efforts to identify leads with a high propensity to purchase additional policies. The insurer trained the ML models on ten years worth of data from customer profiles, trigger events for additional product sales, and time-sequence dynamics in product ownership. The result was a 25% increase in revenue potential.
  • Munich Re built cross-selling and up-selling models to boost sales and marketing efficiency, resulting in fivefold increased conversion for the customer segments with the highest (potential) value. The models used to identify and predict individual preferences were based on data around the customer’s purchasing history.

Recommendation Engines

  • On top of a cross-sell model, Edelweiss Tokio Life Insurance developed a recommendation model that identifies what policies will best serve their prospects. Using this ‘next-best-offer’ recommendation engine to rank and recommend the top three policies for each prospect, the insurer increased cross-sell rates by 200% compared to the previous year.
  • Clearcover launched the ML-driven SmartCover tool that recommends simpler, customized car insurance packages to customers so that drivers can get the coverage that best meets their needs while paying lower rates.

(4) Pricing and Underwriting

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ML models built on rich data sources have enabled insurers to improve the efficiency and accuracy of processes that used to be predominantly rule-based, such as underwriting and pricing.

Such advances are driven by a more accurate assessment of customers’ risk through access to internal and external data on their profiles and behaviors.

Automated Predictive Underwriting

  • Swiss Re built predictive ML models that effectively triage Life & Health underwriting to simplify the consumer journey by segmenting customers based on risk levels. Besides improving customer experience, this capability has allowed them to manage claims and reserves in a better manner.
  • AXA developed ML models to reduce the time and effort spent by underwriters to accurately price risk premiums for commercial customers (e.g., marine, SMEs). The efficiency gains in the underwriting process helped save valuable man-hours for underwriters to deal with more complex risks instead.

Dynamic Pricing

  • McKinsey reported that several life insurers in Japan are moving to a “pay as you live” premium schedule with dynamic pricing. For example, customers who exhibit regular healthy behaviors (e.g., exercising and attending medical checkups) enjoy customized lower premiums.
  • Root Insurance leveraged ML models built on driving behavior data (e.g., signals for hard braking, distracted driving, etc.) to offer premium rates based on how safe (or risky) the customers drive.

(5) Service and Administration

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The challenge of delivering a top-notch customer experience across service support is that it can be difficult to scale or keep consistent.

Fortunately, data science has opened new opportunities for insurers to understand, engage, delight, and retain consumers effectively.

Conversational AI Assistant

  • Accenture has implemented intelligent virtual assistance for numerous global insurers through its proprietary AI chatbot, Cathy. The chatbot has been trained to address 70% of service requests and inquiries, such as issuance status and claims status.
  • One of the largest US property and casualty insurers used Amelia (a conversational chatbot) to interact with human agents. The aim is to provide smart assistance for agents (e.g., guides for procedures like coverage change) to respond to customers’ queries promptly and accurately. Amelia enabled the insurer to decrease customer call duration while increasing the rate of inquiries resolved in the first call.

Document Processing

  • AXA CZ/SK worked with Rare Technologies to develop a model that automatically extracts data fields from incoming unstructured scanned handwritten documents. The goal is to increase administrative efficiency and reduce the cost of human labor. The final solution is a fully tailored, highly accurate deep learning solution specifically for AXA documents.

Customer Churn Prediction

  • Munich Re implemented churn models to predict the likelihood of customers leaving the company’s services in the following months. Combined with customers’ lifetime values, the company boosted the retention of their most valuable customers by 3–5%.
  • According to McKinsey, a large European insurance group developed an ML model to predict customer churn. Built on relevant features like premium amount and policy tenure, the model can identify clients most at risk of leaving, thereby allowing efforts to be focused on retaining them. As a result, renewal rates increased by up to 7 percentage points and profits by as much as 5 percent.

(6) Claims Management

Photo by Matt Hudson on Unsplash

Traditionally, the claims process is thought to be lengthy and manual, resulting in frustration for all stakeholders (e.g., claim handlers, workshops, customers).

In recent years, insurers have explored the use of data science to streamline this process. Beyond creating a smooth process that ensures customer satisfaction, insurers also look to reduce costs and mitigate fraudulent claims.

Claims Handling

  • Tractable uses computer vision to assess images of vehicle damage and estimate repair costs. This AI-powered visual damage assessment has helped speed up claims and reduce friction in the process. The result is ten times faster auto insurance cycle times, along with 70% of claims reviewed without human involvement.
  • Allstate, a US auto insurance provider, worked with Super.Ai to build a computer vision system to scale car claims processing. As a result, the insurer could review thousands of images and video footage of car damage and expedite the claims adjustment process from weeks to days.
  • Aetna built an auto-adjudication ML system that boosted the efficiency and accuracy of claim resolution, resulting in an annual savings of $6M in processing and rework costs. The system blends natural language processing, an unstructured text parsing methodology, and database software to identify payment attributes and construct data that systems can automatically read.

Fraud Detection

  • Mitsui Sumitomo Insurance worked with Shift Technology to implement a cloud-based fraud detection model to identify fraudulent claims amongst the millions of claims processed annually. The solution allowed the insurer to speed up the response to make changes in the claims handling process and also achieve faster payments for insurance claims.
  • Lemonade’s claims bot (AI Jim) has superior claim fraud detection capabilities built into the system. While the system can automatically detect fraud with high accuracy, it also flags complex cases for further human review. The fraud system is built upon the tenets of data integration within the company, a fully digital claims process, and advanced machine learning modeling.

Wrapping it up

We have seen how data science positively impacts the insurance sector using real-world examples.

While the use cases mentioned above are not expected to be exhaustive, they serve as a valuable starting point to understanding the application of data science across the insurance value chain.

If you know of other useful examples, please share them in the comments section. I would be delighted to hear about it!

Before You Go

I welcome you to join me on a data science learning journey! Follow this Medium page and check out my GitHub to stay in the loop of more exciting data science content. Meanwhile, have fun implementing data science in insurance!

Note: The author is a data scientist at one of the world’s leading insurers.

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