Digital Thought Leadership in Insurance

“Data across the insurance value chain”

Tokyo, 3 February 2021- NTT DATA , a leading IT services provider, has launched the first issue of a new insurance sector white paper series, “Data Across the Insurance Value Chain,” analyzing the specific challenges and themes of the insurance sector and presenting the company’s recommendations based on their global approach. The following is an abstract of NTT DATA’s research and findings..

Market momentum

Knowing that Artificial Intelligence and smart data have become essential in the insurance industry today, and as a first approach within the series, NTT DATA has analyzed the maturity stages of AI-Driven Organizations, offering the keys for companies in the sector to evolve from an incipient, opportunistic and tactical use of AI to models that orchestrate Data & AI as crucial assets to generating business and building a bonding model for relevant customers in real-time and based on actionable insights

According to a survey by Gartner, CIOs are considering the leveraging AI across the insurance value chain as a top priority. Opportunities are growing for competitors who have unique technological expertise that allows them to expand their diversity of offerings to meet customer expectations. These customer expectations are all about flexibility and prevention, two factors that have become highly relevant.

The current business context is characterized by a paradigm shift with the entry of Insurtech companies, new players that are competing directly with traditional players and accelerating their entry into the market by leveraging AI as a key competitive advantage. In addition, investment funds have shown interest in these new players. Despite the circumstances of 2020, investments and transactions have been at record levels. This quarter, the share of seed investments grew to 57%, returning to pre-Covid-19 levels, and half of transactions have been in the insurance distribution sector.

Where the industry is heading: scalability and monetization

Considering the importance of the critical technological factors mentioned, insurance companies are continuing to make a significant global push to increase their use of AI and their data foundations capabilities, as well as deploy more and more sophisticated use cases. Everything is always focused on improving people’s lives and reducing costs.

Currently, there are clear examples in the market. Companies in the Insurtech sector entered the industry with strength by offering the option to purchase insurance through an app in a simple and flexible way. Also, the Tesla insurance case, an app that allows users to take out insurance online, and then adjusts prices based on data generated by the car, according to the driving parameters of each person. Delta Dental, America’s largest dental insurance provider, used brushes that send information about the oral condition of each user. In short, examples of how companies adapt to new times.

After an early “learn by doing” period, nowadays the market has entered a phase of scalability and monetization. Leveraging AI at scale requires organizations to frame key pillars to become AI-driven in alignment with the Corporate Governance: Data & AI Governance that support AI strategy and MLOps to become the orchestrator of the data and AI lifecycle so that insurance companies manage AI and data product lifecycles at scale to boost monetization.

But, apart from having functionalities issues in mind, other significant topics to be covered over the following years are AI ethics, trust, and security. Insurance is a top relevant actor in shaping both the economic and social context. Together with Financial Services, Insurance is meant to be a key player to shape the future of Responsible AI. Both industries share a core dependency on big data to develop their existing and new value proposals.

Managing both personal and behavioral data on a large scale, insurers will need to deploy mechanisms meant to identify and mitigate data proxies and bias and define a clear strategy to provide their stakeholders with an understanding of AI models on areas such as claims management or underwriting.

NTT DATA’s insight

The positive impact of Artificial Intelligence on generating new business value should balance with purposeful strategies to minimize creating any disadvantages, harm, or discrimination in people’s lives, for instance depriving them of the right risk protection level.

Thus, the race to lead the insurance market in the near future has started. Applying the following best practices might make the difference between success and failure:

• Guarantee that this transformation is managed holistically at a company level: There is every likelihood it will fail if the only decisions made are to appoint a guru as chief data and analytics officer and create a new team based on data scientists and engineers. If more transformational and ambitious actions are not made, these talented resources will leave the company sooner than later.
• Manage and challenge the status quo of a traditional insurance company: The typical insurer has gone through different mergers and acquisitions processes and now it is in the middle of an operational excellence transformation to significantly reduce its workforce in order to be more efficient and competitive.
• Create a small new team isolated from the others (IT, business intelligence, etc.): the goal is to be able to share with the market and the shareholders that the company really invests in this market trend.
• Establish a global, coordinated, and ambitious organization chart and operating model: to guarantee the whole company is acting under a data-driven approach, leveraging all synergies
• Embrace new use cases: powered by cutting-edge capabilities with a shorter time-to-market.
• Think out of the box: insurance companies tend to focus on how leveraging AI and Smart data within their current business model and value chain, but the real transformation is how the business model and the value chain have to be further evolved thanks to new AI capabilities.