Machine learning and Actuaries
Machine Learning has become an unavoidable topic. It has also created a substantial stir within the industry of actuaries. Machine learning has, in more recent times, in an ever-evolving digitized world, become an invaluable tool. One imperative thing to know about these two separate fields of study, is the distinct differences between them. This can clearly be seen when looking at what each field of study focuses on.
Machine Learning
Machine learning is a branch of Artificial Intelligence (AI) which allows computers to learn without being explicitly programmed. Here, the development of computer programs which can teach themselves to both grow and change, is the core factor.
Actuarial Sciences
This field of study applies mathematical and statistical methods in order to assess risks in insurance, finance, and several other industries and professions. Actuaries themselves, are professionals who are qualified in this field through education and experience. Statistical interference is what helps Actuaries predict the future, so to say. In addition, they help many underwriters understand the ‘why’ that presents itself in data. They are valuable specifically to underwriters and organizations which work to understand specific risks according to the statistical data. You might also be interested in an Ultimate Guide to Big Data.
If statistics works, why should Machine Learning interfere?
The focus in a world which is continuously evolving is for processes to become automated, why? It saves time, money, and effort in addition to being more accurate, efficient, and effective. More Actuaries have begun to apply machine learning tools in their work where they use it to unlock potential data which has been overlooked, such as text fields, images, or other data. They make use of it to leverage the data that they already possess. In addition, machine learning can be used in the field of actuarial to analyse mortality experience to identify new trends, the pricing of insurance products, forecasting financial data and several others.
Problems and solutions in training
In order to train actuaries to use machine learning, it requires a revamp of the entire syllabus due to gaps in the education system, leaving the actuarial syllabus in danger of becoming anachronistic. The syllabus to train Actuaries was only revised and revamped in 2019. This resulted in the introduction of new material to ensure that Actuaries are effectively introduced and trained in using machine learning.
How has the machine learning approached improved the actuarial field?
- Expense savings as less actuaries are needed.
- Time speed of analysis through the use of the algorithm which improves the processing on submission information, speed of the transaction and analysis.
- Scale – through the nature of the machine learning algorithm, it means an increased scale as well as growth for insurers and reinsurers.
The Outcome
- A need for data engineers and scientists in the insurance industry.
- Less resources focussed only on actuaries.
- There will be two types of insurers and reinsurers, quick quote and small lines and slow quote larger lines.
- Underwriters will be more versed in the field of data science.
- Employees, that receive the necessary training in machine learning in the field of actuaries, have the potential to become more valuable in the generation of alpha data.
- The development of platforms which are easy-to-use output for non-data scientists.
For more information visit OQLIS’ website here: https://www.oqlis.com/.
