Life Actuary or General Insurance Actuary – why not be both?
APR was founded in 2006 by Life actuaries, Roger and Gary, and the business naturally established its roots in the life insurance sector. As APR has grown, we’ve diversified our services, leveraging off our established business model to deliver high-quality solutions to clients outside the life sector. Since 2012, we’ve successfully been providing resourcing solutions to non-life insurers with clients including Lloyd’s of London, London market syndicates and others.
Having been with APR for nearly 6 years, I’ve been involved in a wide range of projects spanning the Life, Pensions and General Insurance (GI) sectors. The diversity of our clients provides continual opportunity for APR staff to work in new areas and expand their knowledge. My experience to date has included:
- SII implementation and reserving transformation project at mid-sized mutual life insurer.
- With-profits stochastic modelling for a globally systemic life insurer.
- Pricing bulk annuities for DB pension de-risking.
- GI London-market syndicate reserving.
Few actuaries have experience working in both general and life insurance and the opportunity to do so provides a unique perspective. While the risks, modelling and approaches can be quite different, there is overlap in the knowledge and skills required and there are lessons which each could learn from the other. I get to see what clients do well and what they do not so well, and I can use this experience to shape my own solutions to tasks that I’m faced with. In this article I reflect on these experiences to compare the skillsets and experiences of GI and Life actuaries. I will also add that, as with all good pieces of actuarial work, there is a caveat that these observations have been informed by my own experience, and as such, will not match the experience of every actuary!
I could devote an entire book to this topic and so in the interests of brevity I will focus on the following three areas:
- Data and processes.
- Assets and liability modelling.
- Data analytics.
Data and Processes
Data underpins our work as actuaries. We typically use past experience as the basis for modelling the future. An actuary once said to me that we should spend 20% of our time processing information and 80% of our time analysing it. Yet, regularly I see the opposite to be true. Wherever I go, I find significant challenges with data both in life and non-life insurance.
From my experience, Life companies are ahead in this area. There was significant investment in transformation projects as part of the implementation of Solvency II regulations. In many cases, liability valuation processes were rebuilt and streamlined, data systems were integrated where possible and new software and technology was adopted. One life insurer I worked at was utilising Robotics Process Automation tools nearly 5 years ago. This investment has allowed actuaries to devote less time to manual processing and more time to valuable analysis, although it has not been universally successful.
Cashflow projections are not part of the foundations of general insurance reserving so Solvency II was largely a bolt on to existing workflows for some GI companies. However, many GI companies are now catching up with significant investment linked to their IFRS17 roll-out. This is still an ongoing piece so I often see GI actuaries having to get very involved in data cleansing and manipulation on a regular basis. This can have advantages as it means they‘re acutely aware of their data’s limitations, but ultimately it reduces the time available for more valuable analysis.
In both the life and GI sectors, we are seeing some companies move away from traditional industry modelling software to more bespoke solutions.
Another commonality between the sectors is the reliance on Excel as a go-to tool for analysis and presenting results. While we are starting to see more extensive use of alternatives, I suspect Excel will remain the backbone of our work for a while. What surprises me most in this regard is the lack of formal training provided to new joiners to the industry (unless you work at APR!). Skills are developed on the job, learning from spreadsheets which have often been in existence for many years. These are often clunky, slow and do not conform to modern best practice. A minority of actuaries can write coherent VBA to automate basic routines so time is often consumed on manual tasks or waiting for unnecessarily slow models to run.
Assets and liability modelling
For life insurers, liability matching and investment linked benefits mean investment modelling and management is an integral part of actuarial work. Complex stochastic models are used to model the inherent links between assets and the value of very long-tailed liabilities, and to understand the impact of changes in economic conditions.
The link between assets and liabilities is less material within General Insurance, but investment risk does provide some diversification benefits to primary insurance risk. A large proportion of the risk is short-tailed with durations of less than 3 years, although inflation aspects can be important for long–tailed casualty insurance.
One of the complexities of general insurance is that risks evolve much more fluidly. Changes in environmental patterns are impacting the frequency and severity of hurricanes and wildfires, while technological advances have increased the need for protections against cyber attacks. Evolving and new risks have to be monitored and modelled where often little data exists. In GI, expert judgement forms an integral part of decision making in areas where there is significant uncertainty.
Life actuaries have the benefit of knowing that everyone will eventually die, it is just a case of when. Large data sets often allow for robust statistical analysis, but we are equally challenged in trying to understand how mortality rates will evolve in the future. Changes in government policy, such as pension reforms, and changes in environmental conditions can also have a greater impact on policyholder behaviour and their propensity to purchase insurance.
Specialty general insurance markets are exposed to fast-moving underwriting cycles. Insurers will enter and leave particular sectors far more often, creating gaps and competition which can impact pricing and the quality of the risks that are underwritten. Such information is not visible in emerging data, so general insurance reserving actuaries need a much closer working relationship with underwriters and pricing actuaries to understand the changes in market conditions and risk profiles.
In the last few years, there has been widening engagement in data analytics across both life and non-life insurance sectors. As technology develops, the opportunity to collect and unlock additional value from data increases. Wearable technology such as fitness trackers and “black-boxes” used to monitor drivers can be valuable sources of data for assessing risk at a micro level. Data analysis techniques are advancing too, with examples including machine learning techniques applied to credit-risk modelling and blockchain technology in commercial use within the marine insurance sector.
What the future holds in regards to data analytics is less clear. While actuaries are highly numerate, for data science to be fruitful, we will need to successfully collaborate with specialist from fields of computer engineering and statistics and create an integrated solution. Furthermore, in the highly regulated industry we work in, black box models may not be accepted in the sphere of liability valuation and capital modelling, if model risk is deemed to outweigh policyholder protection.
While we use historical experience as the starting point of our analysis, we are acutely aware of the changing environment which will lead to potentially vastly different experience in the future. It may be challenging to capture that holistic information and uncertainty in ever more complicated models and I don’t doubt that actuaries will remain important subject matter experts in this area for the foreseeable future.
Life vs non-life insurance
Regardless of which sector you work in, the ability to adapt and respond to changing market conditions is essential, whether that be new regulations, new technology or emerging issues such as climate change. Insurers need to make sure their offerings remain relevant, profitable, affordable and compliant to meet the needs of their customers.
Many people ask me which I prefer – Life or GI? Having spoken to other individuals who have also worked across both sectors, I have found people sitting on both sides of the fence. For me, it’s a trade-off between the interesting challenge of applying significant expert judgement found more within GI versus a playing field where more robust statistical analysis is fruitful. The opportunity that APR affords me to work in both halves is one which I will find difficult to give up.