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.
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 the AI-Driven Organization, 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 generate business and build a bonding model to the relevant customer, in real-time and based on actionable insights.
According to a survey by Gartner, CIOs are considering a top priority the fact of leveraging AI across the insurance value chain. Opportunities are growing for competitors with certain technological expertise, which expands the diversity of offerings and customer expectations. These customer expectations are all about flexibility and prevention, two factors that have become totally relevant.
The current business context is characterized by a paradigm shift with the entry of new players (Insurtech companies) that compete directly with traditional players and accelerate 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, the largest amount of investment/transactions has been recorded in history. This quarter, the share of agreements in seed investments grew to 57%, returning to pre-Covid-19 levels, and half of the 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 keep making a significant global push to both increase their use of AI and data foundations capabilities, as well as deploy more and more sophisticated use cases. Everything always focused on improving people’s lives and reducing costs.
Currently, there are clear examples in the market. Companies in the fintech 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 you to take out insurance online. Then with the data generated by the car, it adjusts the price to the driving parameters of each person. Delta Dental, America’s largest dental insurance provider, used brushes that sent information about the oral condition of each user. In short, examples of how companies adapt to new times.
After a first period that could be title “learn by doing”, nowadays the market is going through a second phase focused on scalability and monetization. Such purpose of leveraging AI at scale requires the organization frames the 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 high 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 explainability of AI models on areas such as claims management or underwriting.
NTT DATA’s insight
So, 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 this transformation is managed holistically at a company level: There is every likelihood it would 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…): 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 the 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 about how the business model and the value chain have to be further evolved thanks to new AI capabilities.
Applying AI and smart data across the insurance value chain will continue being, without a doubt, one of the main strategic priorities for the industry in the coming years. The main reason is that there is no other transformational initiative with numerous ways of making a relevant and tangible impact on both the P&L and customer experience. And on top of that, it is a sustainable competitive advantage with a high entry both technology and talent.
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