Quant Insight Limited (Qi), a macro data and analytics firm, and AkinovA Limited (AkinovA), an electronic marketplace for the transfer and trading of insurance risk, have formed a partnership to enable the construction of new and more precise risk transfer products with an understanding of how a range of macro-economic risks such as climate change impact those transactions.
Quant Insight Limited (Qi), a macro data and analytics firm, and AkinovA Limited (AkinovA), an electronic marketplace for the transfer and trading of insurance risk, have formed a partnership to enable the construction of new and more precise risk transfer products with an understanding of how a range of macro-economic risks such as climate change impact those transactions.AkinovA and Qi intend to work with insurance, data driven brokers and capital markets clients to optimise risk transfer products to mitigate insurance and investment risks which, if left exposed, have the potential to cause catastrophic financial impact. The goal is to enable re/insurers, brokers and investors to identify new ways to protect exposures that were otherwise large blind spots without the ability to extract precise signals.
Qi’s unique ability to ingest and analyse huge data lakes including millions of daily securities, some 25 macro trends including inflation expectations, GDP growth, credit spreads, risk aversion, sovereign stresses utilising advanced artificial intelligence and analytics is already used by family offices all the way to USD1 trillion global asset managers.
Mahmood Noorani, QI’s Founder and CEO, says: “QI has developed a unique blend of insights from experienced investors and leading academics to create a high-end cloud computing and a proprietary machine learning framework capable of untangling market complexity. Recent work on climate risks’ connection with a range of securities and other asset classes clearly indicates that our technology is highly applicable to the re/insurance and Insurance Linked Securities (ILS) industry. This is why we elected to partner with AkinovA who offer a neutral, regulated and digital-first marketplace to transfer and trade re/insurance risks.”
Henri Winand, AkinovA’s Co-founder and CEO, says: “Our partnership will serve the entire insurance eco-system, including participants from capital markets and corporations. Armed with Qi’s unique data, analytics and ability to identify portfolios with specific, desired macro characteristics, re/insurers and ILS funds will be able to strengthen the control of significant emerging and often unseen risks, and opportunities, arising from re/insurance and investment-related activities. To date, those risks and opportunities remained largely unquantified and are often imprecisely hedged. Beyond climate-related products, we expect the combination of Qi’s insights and AkinovA’s electronic risk transfer marketplace to power a new class of highly dynamic risk transfer products suitable for intangible assets and cyber risks transfers.”
For instance, climate change is an undeniable threat to todays’ society. However, while the impact is broadly visible and regulators are actively pushing re/insurers, investors and corporates alike to disclose their exposure, handling its precise financial impact is much harder.
With this partnership, on the insurance side, security baskets can be created that closely track climate change data in specific locations including land, sea temperature or wind speed averages. The key is to use data local to the exposure in order to design safety valves in capital markets that hedge against meaningful vulnerabilities, thereby linking insurance with investment activities.
Yet, on the investment side, managers often lack a real time appreciation of how macro forces impact their portfolio’s assets. This means they can unwittingly ‘long’ or ‘short’ macro factors such as corporate credit risk, FX moves, index volatility, central bank QE policies, inflation growth or shifts in real time GDP. Qi’s dimensionality reduction and signal extraction allows investors to understand and isolate each of these underlying driver sensitivities which can dominate the value of a portfolio in dynamic ways.
