Insurance Nexus is now Reuters Events - LEARN MORE
A new series on The Exponential Actuary of the Future
The future of actuaries cannot be ascertained without looking at the macro "meta-trends" working in the larger scheme of things... In the fourth post of a new series on The Exponential Actuary of the Future, which outlines multiple scenarios for the future of the actuarial profession, we elaborate a plan of action for actuaries to remain relevant in future ...
Future Proofing our Modeling Regimes
- Apply both quantitative and qualitative analytics. No one model or method knows it all
- Never forget risk; almost everything can be broken down into: what core risks are you taking as an insurer or Insurtech? Model those risks
- Integrate your big-data strategy with other crucial strategies like product strategy, business plans and so on. Make holistic sense; one strategy should complement the other
- Put the customer and Artificial Intelligence (AI) at the core of everything that you do. Without better customer experience, the products won’t sell. Without putting AI at the heart of what we do, other players (especially tech disrupters) will make you redundant.
- Learn continuously; new technologies are emerging before previous ones mature. Never be afraid to try out something new and learn fast and quickly adapt. Have a futuristic vision; communicate your vision to stakeholders and invite them along for the journey.
- Combine domain expertise with AI to overcome limitations of each (domain or AI) on their own.
- Handling more types of data, not just structured in spreadsheets; more types of modeling; qualitative streaming in real time etc more schemas of data like MongoDB etc
- Descriptive analysis
- Predictive analytics
- Unstructured data analytics
- Big Data
- Actuarial analytics
- Enterprise Risk Management (ERM) modeling
- Qualitative profiling including emerging risks
- Rega Life
- Bit Life and Trust
- Unity Matrix Commons
- Improve our tools. We live with increasingly complex systems and solve complex problems but many of our tools are too reductionist for handling these nuances.
- More importantly, change our mentality; qualifying as an actuary alone will not guarantee everything. Many tools will likely be outdated once an aspiring actuary ultimately becomes a fellow. Familiarity bias, where we continue using tools which we know rather than what is the best, coupled with being ambiguity-aversion may prove to be our Achilles’ heel. We need to learn more diverse subject areas and viewpoints, strengthen qualitative understanding, and be more pro-active and better at communication. Continuous learning has to be in our bones.
- Actuarial modelling projects naturally fall into the category of supervised learning, with tasks such as insurance contract pricing or pension scheme valuation naturally fitting into this framework. This makes supervised learning tasks a natural place for actuaries to initially explore machine learning techniques.
- Start with what you know; innovate within our comfort zone in supervised learning in current insurer jobs and then go ahead into unsupervised and other careers etc.
- No one can do it all. Share, build team work, collaborate across multi-disciplinary teams