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The Insurance Brain: AI-Driven Policy Recommendations

This article was originally posted February 18, 2020 on Forbes Cognitive World – AI Contributor Group.  dotData’s Founder and CEO – Ryohei Fujimaki, PhD was an interviewed contributor for this important information share.  

 

In today’s competitive global insurance market, insurers are striving to create new ways to successfully overcome two important and opposing forces: creating short-term revenue growth for the company, while also meeting customers’ needs for product offerings and services that are personalized, relevant, and provide long-term value.

In meeting these challenges, insurers realize the strategic importance of their data, and how AI and machine learning (ML) can help them better achieve their business goals. But while investments in AI are growing, challenges in resources, technology infrastructure, and the ability to operationalize models quickly and efficiently can prevent insurers from fully leveraging AI and data science to drive business impact.

These were some of the challenges faced by leading global insurance company MS&AD Insurance Group Holdings (MS&AD), and the impetus behind its development of an innovative solution that leverages AutoML 2.0 to optimize their data science investments. This fully automated data science platform enables MS&AD agents to create proposals to reflect the current and potential insurance needs of their customers, leading to increased revenue, as well as customer loyalty and satisfaction.

MS&AD, Innovation Through Technology

MS&AD Insurance Group Holdings is the world’s fifth-largest property and casualty insurance company with $50 billion in revenue. MS&AD has been a leading innovator in leveraging digital transformation to advance the insurance business. One of the critical goals in their digital transformation journey is to optimize customer value and utilization of its products and services.

The idea of using digital transformation to enhance customer experiences led to the development of a strategic digital platform, called MS1 Brain. The MS1 Brain platform utilizes AI and machine learning to analyze available customer data, such as contract details and history, accident information, and lifestyle changes, to predict customer needs and recommend the best products and services to meet those needs and drive long-term value. The platform also helps generate targeted customer communications, including personalized videos on products and services created to meet the specific needs of each customer.

MS1 Brain was created for MSI, an affiliate of MS&AD, to enable MSI’s agents to create personalized, data-driven proposals tailored to consumer needs. The platform also needed to be easy to use so that agents could generate proposals and leverage data without prior data expertise.

Challenges Along the Way to Innovation

MS1 Brain utilizes many AI models for its intelligent predictions and decisions. MS&AD faced challenges in scaling their data science practice and building the MS1 Brain – both because of the difficulty involved in creating effective machine learning models with transparency of AI-decisions, as well as in finding the right level and skill of talent. Adding to this dual-challenge was a very aggressive timeline for the development of MS1 Brain.

As a solution to this challenge, the business and innovation team at MS&AD identified automated machine learning (AutoML) as a critical accelerator to meet the development timeline of MS1 Brain. In particular, MS&AD found it particularly important to automate the feature engineering process, which is often the most manual and time-consuming part of data science projects, as well as to automate machine learning (a.k.a. AutoML 2.0).

AutoML 2.0: The Foundation of MS1 Brain

The primary motivation for choosing an AutoML 2.0 platform focused on three core areas: acceleration, augmentation, and democratization. Acceleration was essential to develop many AI models for MS1 Brain within a short timeframe and allows MS&AD to explore 10x more use cases and to build accurate models for production quickly. AutoML 2.0 explores millions of features and hundreds of ML models based on raw business data consisting of various raw relational and transactional data with billions of records just in hours. Augmentation was also critical as a direct output of automated feature engineering in AutoML 2.0. Through the features automatically designed by the platform, MS&AD discovered many deep business insights that provide explainability of AI-recommendations and also are useful to improve their services to meet the customers’ needs. Democratization was the third critical component. Beyond MS1 Brain, MS&AD needed to establish scalable and sustainable AI and ML capabilities. With the AutoML 2.0 platform, even business analysts could perform the end-to-end data science process with neither SQL/Python coding nor knowledge of sophisticated statistical and mathematical formulas.

Additionally, by augmenting the AutoML 2.0 solution in MS1 Brain with advanced automated video generation technologies, MS&AD was able to create a system that automatically analyzes customer data, and provides personalized video-based recommendations for products and services to its customers. This new capability has enabled MS&AD to optimize customer value, increase utilization of its products and services, and drive additional revenue growth.

Accelerating Business Innovation

While data science is becoming a valuable tool in the insurance industry, deriving value from AI and machine learning initiatives can be challenging. As with MS&AD, organizations that embrace new data science automation technologies will benefit from streamlined processes, greater transparency, and deeper insights to help drive short-term revenue growth while exceeding customer demands for long-term value. As a result, insurance organizations can rapidly scale their AI/ML initiatives to drive transformative business changes.

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