Always innovating
Nick Reilly, head of business development for UK and North Europe at RNA Analytics, discusses the actuarial modelling and risk management software provider’s rolling programme of continuous improvement, and how it is supporting insurers.
RNA Analytics is committed to simplifying the complexity of actuarial, regulatory and risk-based requirements. This helps our clients to deliver consistent calculations, models, approaches and processes with the greatest possible accuracy, granularity and speed of delivery.
Our R3S software suite continues to expand and develop our open modular design building on our heritage of nearly two decades of actuarial excellence. The R3S story started in April 2006 when Watson Wyatt launched VIPitech to the market. In June 2010, Algorithmics acquired VIPitech’s software solutions, intellectual property, infrastructure and the team. This was renamed Algo Financial Modeler (AFM) in 2011.
Later that year, Algorithmics was acquired by IBM and then rebranded as IBM® Algo Financial Modeler® in 2012. In 2017, RNA Analytics acquired the assets and technology from IBM® and rebranded the software suite as R3S.
Has RNA Analytics updated its actuarial modelling software in the past year? What key regulations is RNA Analytics supporting insurers with worldwide?
RNA has a rolling programme of continuous improvement and development for our software suite. Not only do we update for regulatory changes, but we also do this to increase the functionality and content of our asset and liability product libraries.
In addition, we constantly strive to improve the efficiency of our solutions, in both time and cost terms (manual intervention and computing power). Each new release is therefore designed to be more efficient than the last.
On regulation, we have seen a continued demand from our customers to support their needs in regard to IFRS 17. This has been from customers who had not needed to implement this yet, and, surprisingly, also from prospective clients who have implemented other solutions, and found them wanting.
In particular, many solutions for IFRS 17 have been within a ‘black box’. Prospective clients have been unable to drill down and scrutinise the data, and so have had to develop interim solutions to enable them to understand their numbers in greater detail.
RNA developed our R3S IFRS 17 package to enable full flexibility, so enabling each element to be fully understood from first principles. We are therefore able to meet all of the needs of the market within our solution.
How do you expect artificial intelligence (AI) to impact risk pooling?
The area of risk pooling has been an area of personal interest for me for many years. As such, I have been discussing this with my fellow actuaries from the Institute and Faculty of Actuaries. We have set up a working party to look at trends within risk pooling, and the implications for the future.
There are also many groups looking at AI, and we plan to combine their thought leadership in relation to risk pooling.
I have had personal concerns that our industry has slowly and steadily been reducing access to insurance by making small incremental steps in the reduction of risk pools. This has occurred in all areas of financial services, whether that is protection, GI or pensions.
AI, in itself, will not speed this process up. However, it could be used as a tool to do just this if it is taught to find new and innovative ways to identify the ‘best’ risks. This is, of course, subject to regulatory, moral and operational issues, but if these can be overcome, AI could be a tool to reduce risk pooling further.
Some believe this is a good thing, as prices are ‘fairer’, and by reducing the price for the best risks, it increases profit for the companies that identify those risks first. However, this leads to more customer segmentation, more customers finding insurance unaffordable or unattainable, and so an ever decreasing market for insurance.
The short term gain may lead to long term market issues, and that is what needs to be considered sooner, rather than later.
As a technology provider, AI continues to be an important area for RNA Analytics, as we see many uses for it to improve the efficiency of our modelling solutions. We believe that we will be able to save time and resources for our clients, while reducing operational risk and human error. RNA Analytics is expecting to launch our AI initiatives in a fully controlled way during 2024 and 2025.
