Trailblazing a path beyond the “General Account”, leveraging on customers’ experience and digital

Persistent persistent zero interest rates have created a dilemma for those planning for retirement

- the most famous traditional insurance product called “General Account”, characterized by full guaranteed capital and 80-90% low-risk debt investments (broadly half govies bonds, half corporates bonds), has reached strong limits of sustainability, as illustrated by unattractive yields for the customer and expensive cost of capital for the insurance company. So that several insurance companies CEOs have recently announced the expected death of this major insurance product. Still alternative equities unit-linked remain inherently risky given the multiple unexpected volatility spikes and drawdowns since 2015.

- General Account products are still today based on a top-down approach, not caring enough about customers’ risk appetite, products usage and time horizons.

As a result trailblazing a path beyond the General Account relies on a multiple pillar integrated strategy, meeting risk appetites of both shareholders (sustainable growth and higher profitability, lower cost of capital, controlled risks), and customers (higher yields combined with some guarantees, tailored to emerging behaviour changes):

1. Be “Customer centric” in designing the Liabilities from “profitability/ risk / cost” profiles tailored to specific customers’ segments, while ensuring recurrent revenues to the insurance company for a resilient / sustainable business growth (e.g. mitigating lapse and withdrawals behaviour risks).

- Such « capital light » hybrid insurance policies designs meets diverse and evolving customers’ yields appetite and protection needs, translating into some dynamic usage of life insurance products features over their full duration (full or partial guaranteed capital, punctual lump sums to support children education payments, personal trainings, unemployment periods), at reasonable costs in line with the persistent low interest rates environment (from 1% to 2,5% all-inclusive annual fee).

-This initiative takes advantage of customers experience through building clients profiles segmentations (current and expected), extracted from integrated internal and external data (market environment, underwriting, inforce book, customers support, claims management, social networks), and the use of digital technologies (« data virtualization », chatbots, Artificial Intelligence).

-In particular, the Artificial Intelligence digital technology provides an “efficient behavior” approach which is a prudent forward Looking approach, based on individual policyholder’s optimisation of net profit over the full life of the contract duration, depending on key drivers such as the moneyness of the guarantee or the level of interest rates. “Efficient" modelling framework is a useful benchmark that can be used in pricing once key drivers are also considered to get realistic prices consistent with practice: market conditions, policy durations, past decisions (potential partial withdrawals), tax regime. Such a modelling framework can also be used in product designs to stir policyholder behaviour whilst also fitting client appetite. For instance, the design of the fees opens a way to improve both the competitiveness and the clients’ risk appetite fit, ensuring a sustainable growth.

2. Design the Assets investment mix tailored to specific customers’ segments appetites, while mitigating ALM risks and meeting regulatory constraints for the insurance company.

- Such a design initiative to offer more resilient and generate significantly higher returns leverages on (i) a high fundamental quality stocks selection (healthy and stable profitability, strong free cash flows, low debt and shareholder-friendly practices, above average dividend payout, low net equity issuance) and technical (with focus on skewness and kurtosis); (ii) an asset allocation using capital requirements and transaction costs-optimized portfolio rebalancing, forward-looking volatility mitigating mechanisms, some derivatives for performance protection to mitigate drawdowns or boost returns within sideways markets and to manage “gap risks”; (iii) long-term partnerships with asset management firms and investment banks.

- Such an initiative leverages on digital technologies to merge and visualise data (data virtualisation and BI), forecasting returns/ risk and customising the utility function of the customers (artificial intelligence), transaction costs modelling (artificial intelligence technologies using “optimal control” and “feedback loops” modelling for devising the rebalancing strategy depending on the risk tolerance relative to a target allocation and the amount of transaction costs).

- In particular Big Data and Artificial Intelligence provide more resilient managed volatility funds, that do not overreact and exacerbate non-fundamental sell-offs but only consistently with sound economic concerns (e.g. by introducing macro-economic and supply and demand indicators into the volatility control mechanism).

3. Reduce the « time-to-market » at much lower costs than today, through the use of the RPA digital technology, providing automated processes and reporting.

4. Lower cost distribution channels while offering high standards customized advisory on product selection and usage, by developing on-line digital platforms that dynamically integrate customers’ savings behaviours (via « machine learning » technology) and help « cross selling» (via chatbots).

5. Improve agility and governance by leveraging on real-time 360° views internal dashboards based on digital technologies (data virtualization & visualization) for the Management Board to help define long-term goals and manage critical situations.