AI and Automation Working Party - Survey

Introduction

The IFoA AI & Automation in Life and Healthcare Working Party is working to identify how actuaries are using AI/ML techniques in their areas of work.

The purpose is to gather relevant and useful information which will inform the Working Party of the stage actuaries are at when it comes to implementing AI/ML techniques, to identify any gap in knowledge and any other challenges in incorporating these techniques. It should help the Working Party develop an understanding of the steps that need to be taken by the various stakeholders to progress the use of AI/ML within actuarial practice areas. The findings from the survey will be analysed and be published in the form of a paper.

Your individual answers are not attributable and will not be shared with anyone outside of the Working Party.

The survey will take approximately 10 minutes to complete.

Personal Profile

Question Title

* 1. Which actuarial field are you practicing in?

Life Insurance/Reinsurance

Health Insurance/ Reinsurance

General Insurance/ Reinsurance

Pensions

Investments

Other / Non Traditional (please specify)

Question Title

* 2. What is your current level of experience within your organisation?

Student Actuary/ Junior Analyst

Nearly Qualified Actuary / Senior Analyst

Qualified Actuary

Senior Actuary / Head of Department

Question Title

* 3. When it comes to understanding of Artificial Intelligence (“AI”) and Machine Learning (“ML”) techniques and environment how would you describe yourself?

No understanding

High level understanding of concepts and ideas

Deeper level of understanding allowing for peer review and explaining of AI/ML result, but don’t have the programming skills to construct the models

Student level developer of AI and ML techniques

Expert level developer

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* 4. 4. In which area are you looking to further develop your knowledge on AI/ML? (Please select all that are relevant)

Understanding concepts, opportunities and limitations from a commercial and business point of view

Understanding features of AI/ML systems from an actuarial point of view, particularly focusing on their risk and on how to review them

Learning simple techniques which could be used to approximate results from first principle where possible

Learning relevant coding to create AI/ML models

Not looking to deepen knowledge on AI/ML

Other (please specify)

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* 5. Do you receive educational support from your organisation to support your development in the fields of AI/ML? (Please select all that are relevant)

Internal learning and development sessions on AI concepts organised by own organisation

Subscription fee for online courses available on various e-learning platforms

Institute and Faculty of Actuaries Data Science certification

Time off given for personal study

Monetary support

No support given

Other (please specify)

Organisational AI Adoption Curve

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* 6. Is your organisation using AI/ML techniques within the organisation and / or deploying solutions to its clients/ partners?

Yes, and they are fully deployed using pre-packaged solutions

Yes, and they are fully deployed using coding/development done in house (or using a mix of pre-packaged and in-house solution)

Yes, but for exploratory purposes (initial experiments/ development phase) only

No but we plan to in the next 6-12 months

Don't know

Challenges and limitations

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* 7. What do you think are the biggest challenges in using AI techniques in your organisation? Please rate from 0 – 5 from (0 = no challenge, 1 = small challenge, 3 = medium challenge, 5 = large challenge)

	0	1	2	3	4	5
Interpretability/ explainability of models and convincing executives	Interpretability/ explainability of models and convincing executives 0	Interpretability/ explainability of models and convincing executives 1	Interpretability/ explainability of models and convincing executives 2	Interpretability/ explainability of models and convincing executives 3	Interpretability/ explainability of models and convincing executives 4	Interpretability/ explainability of models and convincing executives 5
Knowledge gap (e.g. lack of sufficient talent)	Knowledge gap (e.g. lack of sufficient talent) 0	Knowledge gap (e.g. lack of sufficient talent) 1	Knowledge gap (e.g. lack of sufficient talent) 2	Knowledge gap (e.g. lack of sufficient talent) 3	Knowledge gap (e.g. lack of sufficient talent) 4	Knowledge gap (e.g. lack of sufficient talent) 5
Systems constraint (e.g. Legacy systems)	Systems constraint (e.g. Legacy systems) 0	Systems constraint (e.g. Legacy systems) 1	Systems constraint (e.g. Legacy systems) 2	Systems constraint (e.g. Legacy systems) 3	Systems constraint (e.g. Legacy systems) 4	Systems constraint (e.g. Legacy systems) 5
Lack of data availability	Lack of data availability 0	Lack of data availability 1	Lack of data availability 2	Lack of data availability 3	Lack of data availability 4	Lack of data availability 5
Poor data quality	Poor data quality 0	Poor data quality 1	Poor data quality 2	Poor data quality 3	Poor data quality 4	Poor data quality 5
Financial regulation (e.g. PRA/ FCA)	Financial regulation (e.g. PRA/ FCA) 0	Financial regulation (e.g. PRA/ FCA) 1	Financial regulation (e.g. PRA/ FCA) 2	Financial regulation (e.g. PRA/ FCA) 3	Financial regulation (e.g. PRA/ FCA) 4	Financial regulation (e.g. PRA/ FCA) 5
Data privacy regulation	Data privacy regulation 0	Data privacy regulation 1	Data privacy regulation 2	Data privacy regulation 3	Data privacy regulation 4	Data privacy regulation 5
AI/ML development are not seen as a significant business priority	AI/ML development are not seen as a significant business priority 0	AI/ML development are not seen as a significant business priority 1	AI/ML development are not seen as a significant business priority 2	AI/ML development are not seen as a significant business priority 3	AI/ML development are not seen as a significant business priority 4	AI/ML development are not seen as a significant business priority 5
Costs of implementation	Costs of implementation 0	Costs of implementation 1	Costs of implementation 2	Costs of implementation 3	Costs of implementation 4	Costs of implementation 5

Other (please specify)

Applications of AI/ML

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* 8. How important are the following AI/ML areas to your field of work?
Please rate from 0 – 5 from (0 = n/a, 1 = not important, 3 = fairly important, 5 = very important)

	0	1	2	3	4	5
Optical Character Recognition (OCR) e.g. text mining, automatic document categorisation	Optical Character Recognition (OCR) e.g. text mining, automatic document categorisation 0	Optical Character Recognition (OCR) e.g. text mining, automatic document categorisation 1	Optical Character Recognition (OCR) e.g. text mining, automatic document categorisation 2	Optical Character Recognition (OCR) e.g. text mining, automatic document categorisation 3	Optical Character Recognition (OCR) e.g. text mining, automatic document categorisation 4	Optical Character Recognition (OCR) e.g. text mining, automatic document categorisation 5
Predictive Modelling e.g. predictive underwriting, claims modelling	Predictive Modelling e.g. predictive underwriting, claims modelling 0	Predictive Modelling e.g. predictive underwriting, claims modelling 1	Predictive Modelling e.g. predictive underwriting, claims modelling 2	Predictive Modelling e.g. predictive underwriting, claims modelling 3	Predictive Modelling e.g. predictive underwriting, claims modelling 4	Predictive Modelling e.g. predictive underwriting, claims modelling 5
Natural Language Processing (“NLP”) – e.g. document classification, chatbots	Natural Language Processing (“NLP”) – e.g. document classification, chatbots 0	Natural Language Processing (“NLP”) – e.g. document classification, chatbots 1	Natural Language Processing (“NLP”) – e.g. document classification, chatbots 2	Natural Language Processing (“NLP”) – e.g. document classification, chatbots 3	Natural Language Processing (“NLP”) – e.g. document classification, chatbots 4	Natural Language Processing (“NLP”) – e.g. document classification, chatbots 5
Anomaly detection – e.g. fraud detection, data checking	Anomaly detection – e.g. fraud detection, data checking 0	Anomaly detection – e.g. fraud detection, data checking 1	Anomaly detection – e.g. fraud detection, data checking 2	Anomaly detection – e.g. fraud detection, data checking 3	Anomaly detection – e.g. fraud detection, data checking 4	Anomaly detection – e.g. fraud detection, data checking 5
Clustering – e.g. customer segmentation	Clustering – e.g. customer segmentation 0	Clustering – e.g. customer segmentation 1	Clustering – e.g. customer segmentation 2	Clustering – e.g. customer segmentation 3	Clustering – e.g. customer segmentation 4	Clustering – e.g. customer segmentation 5

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* 9. Which other areas of AI/ML are important to your organisation? (Please specify)

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* 10. Do you have practical experience of applying AI/ML techniques in any of these areas? (Please select all that are relevant)

Pricing and product development

Marketing

Claims analytics and fraud detection

Reserving

Capital management, ALM and investment

Other (please specify)

No experience

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* 11. If you have chosen any option except ‘no experience’ in the question above, would you like to share your experience with the Working Party? If yes, please share your contact email below

Name

Email Address

100% of survey complete.

Question Title

* 1. Which actuarial field are you practicing in?

Question Title

* 2. What is your current level of experience within your organisation?

Question Title

* 3. When it comes to understanding of Artificial Intelligence (“AI”) and Machine Learning (“ML”) techniques and environment how would you describe yourself?

Question Title

* 4. 4. In which area are you looking to further develop your knowledge on AI/ML? (Please select all that are relevant)

Question Title

* 5. Do you receive educational support from your organisation to support your development in the fields of AI/ML? (Please select all that are relevant)

Question Title

* 6. Is your organisation using AI/ML techniques within the organisation and / or deploying solutions to its clients/ partners?

Question Title

* 7. What do you think are the biggest challenges in using AI techniques in your organisation? Please rate from 0 – 5 from (0 = no challenge, 1 = small challenge, 3 = medium challenge, 5 = large challenge)

Question Title

* 8. How important are the following AI/ML areas to your field of work?Please rate from 0 – 5 from (0 = n/a, 1 = not important, 3 = fairly important, 5 = very important)

Question Title

* 9. Which other areas of AI/ML are important to your organisation? (Please specify)

Question Title

* 10. Do you have practical experience of applying AI/ML techniques in any of these areas? (Please select all that are relevant)

Question Title

* 11. If you have chosen any option except ‘no experience’ in the question above, would you like to share your experience with the Working Party? If yes, please share your contact email below

* 8. How important are the following AI/ML areas to your field of work?
Please rate from 0 – 5 from (0 = n/a, 1 = not important, 3 = fairly important, 5 = very important)