Explainable Machine Learning
Monday 27th April 2020 12 PM BST
Machine learning techniques have become more and more popular within the financial industry mainly because of
- the potential to capture complex interactions from data
- the potential for better predictive models than traditional statistical models
- the ability to capture non-linear interactions within a range of inputs
Machine Learning techniques have been viewed as useful additions in the actuary’s modelling toolkit that could enable insurers to process and learn from more data
Nevertheless, these models - sometimes viewed as black box models - can sometimes be hard to interpret, audit and debug, subsequently making it harder to trust and use the outcome of the prediction resulting from these models.
Building on from Actuartech’s previous webinar on Interpretable Machine Learning we are pleased to bring another insights session on the topic of explainable Machine Learning - presented by Reacfin.
We will talk about some of the worries around delegating decisions to machines, and how to overcome some of these challenges. We will touch on some of the issues surrounding the trade-off between predictive power and explainability.
We will also expand beyond some of the technical concepts and explore ways to design and build these models in order to make them usable within stakeholder communication scenarios as well as make them suitable to meet professional conduct requirements and usable in the context of achieving fairness in insurance pricing.
During this webinar we will expand on some of the previous techniques highlighted and will introduce additional techniques that can be used in order to better understand and interpret machine learning models and results, showing why they need not be viewed as ‘black box models’.
We will build on this to help identify ways to obtain sufficient comfort in the models in order to make business decisions and to be able to explain the impact to stakeholders.
These interpretability tools make the use of ML techniques much more relevant in practice as it enables to benefit from their higher predictive power while understanding the drivers of the results; which is fundamental to adopt them and take relevant business decisions. As these new methods for investigating machine learning methods are developed, we expect insurers to grow more confident in using these methods and shift more towards models with higher predictive power.
The presentation will cover:
- The importance of interpretability
- What it means to have an interpretable machine learning model
- A non-exhaustive reminder of some useful machine learning techniques
- An introduction to machine learning interpretation tools
- How to make the most of machine learning techniques
A huge increase in data generation, data capture and data storage combined with significantly increased computing power is providing insurers with a unique opportunity to re-evaluate the value that their data can provide; and the technologies available to do that.
Methods used in non-life pricing are evolving at a fast pace and more advanced actuarial and statistical techniques are being used in pricing, competition analysis and profitability analysis.
How can life insurers address low persistency? How can data and analytics help? Join our webinar on Friday 12 March 12pm as we discuss how the full cycle of actuarial analysis is evolving
A huge increase in data generation, data capture and data storage combined with significantly increased computing power is providing insurers with a unique opportunity to re-evaluate the value that their data can provide.
Improvements in computational power has given rise to the use of data science and artificial intelligence techniques in a wide variety of areas, including finance, driverless cars, image detection, speech recognition etc. This is directly impacting business practices within the financial services through its application within banking, insurance and asset management.
In the current economic climate, insurers need to improve their processes continually – making them more efficient and cost-effective while maintaining the agility to deal with new requirements. At the same time, technological change is providing new ways of achieving these objectives. In particular, the term ‘robotics’ appears to be used everywhere, and it is important to grasp the impact and potential use of these new technologies.
Listen to Valerie du Preez, founder and Managing Director of Actuartech, on the panel for Legerity's IFRS 17 webinar discussing, "Can IFRS17 help deliver digital transformation?".
In a world of high volume and varied datasets, data science techniques can add valuable tools to an actuary’s toolkit to provide actionable insights from data.
Sophisticated machine learning models have the potential for high predictive accuracy but their complexity may sometimes result in black box models, which, in some cases, may appear to be a trade-off between accuracy and interpretability.
Sign up to our newsletter for the latest in Actuarial technology