Skip to main content
The Actuary: The magazine of the Institute and Faculty of Actuaries - return to the homepage Logo of The Actuary website
  • Search
  • Visit The Actuary Magazine on Facebook
  • Visit The Actuary Magazine on LinkedIn
  • Visit @TheActuaryMag on Twitter
Visit the website of the Institute and Faculty of Actuaries Logo of the Institute and Faculty of Actuaries

Main navigation

  • News
  • Features
    • General Features
    • Interviews
    • Students
    • Opinion
  • Topics
  • Knowledge
    • Business Skills
    • Careers
    • Events
    • Predictions by The Actuary
    • Whitepapers
    • Moody's - Climate Risk Insurers series
    • Webinars
    • Podcasts
  • Jobs
  • IFoA
    • CEO Comment
    • IFoA News
    • People & Social News
    • President Comment
  • Archive
Quick links:
  • Home
  • The Actuary Issues
  • December 2021
General Features

Unfamiliar territory for artificial intelligence-related risk

Open-access content Wednesday 1st December 2021
Authors
Andrew Morgan
Valerie du Preez
Natasha Naidoo

Artificial intelligence-related risk differs from traditional model risk, explain Andrew Morgan, Valerie du Preez and Natasha Naidoo – how can we get to grips with it?

Unfamiliar territory for artifical intelligence-related risk

Many insurance organisations are investing heavily in artificial intelligence (AI) and machine learning experiments – but, surprisingly, many pilot projects are struggling to gain traction and scale across organisations. One of the reasons for this is the lack of investment in the risk management of AI, whose model outputs often feed into a variety of processes and decisions that can have significant impacts on customer outcomes. This is compounded by the expectation of forthcoming regulations in this space, such as the EU’s proposed AI Act (bit.ly/EU_AILaw).

Insurance risk teams are experienced at identifying model risk, and most have resources and frameworks in place to deal with traditional (insurance risk-focused) models. However, the key risks that could arise through the use of AI are likely to be reputational, conduct-related and regulatory, rather than the more quantifiable risks that have historically been the focus of insurance risk and model validation frameworks.

AI risk is different from traditional model risk for several reasons:

  • AI models may use complex non-linear methods that can be hard to interpret or explain

  • AI models may use varied ‘high velocity’ data and predictions (such as real-time indicators of customer behaviour) that can make decisions dynamically; this differs from typical traditional models, which make use of more ‘static’ data

  • The underlying infrastructure, tools and code libraries required to run and deploy AI models may be new and foreign to traditional insurance organisations and their existing IT or infrastructure capabilities

  • The nature of data has changed – from data that is structured and easily validated, to data that has been taken from different sources and may be of different types. This may mean they could be difficult to reconcile.

Existing insurance model validation frameworks may not be equipped for measuring and managing specific AI-related risks because:

  • Model validation teams tend to adopt risk-based approaches in which different modules are assessed with a frequency based on a combination of materiality and available resources. While this may be suited to static models, it could be hard to adapt to AI models, whose modelling parameters are not necessarily fixed

  • Model validation has traditionally focused on measurable, quantifiable risks such as market risks – but non-quantifiable risks such as conduct and reputational risk may be more important for AI models. An example is unintentional bias in an underwriting model

  • Tools such as stress and sensitivity testing typically identify the key model risk drivers, helping model validation teams to interpret and explain the applicability of model results. With more complex AI models, such tools may not be sufficient to validate if model output is aligned to expectations

  • The scope of AI models in use that require validation may not be straightforward to assess and include within the framework, due to the wide variety of domains and use cases – for example, chatbots are increasingly complex, but existing validation frameworks often do not cater for them

  • The specific IT and data science skills needed to better measure and mitigate these risks may be lacking in model validation teams, which tend to be weighted towards traditional actuarial or risk management skills.

In practice

To make these abstract issues more practical, we consider the potential implications for model risk management in the context of policy cancellation (lapse) experience investigations.

Traditionally, actuaries have attempted to understand lapse experience for groups of policyholders with particular characteristics when performing their regular experience analysis investigations, often averaging experience across certain groups. These traditional modelling approaches have mostly used linear models, and are typically less automated.

With new data sources, modelling techniques and better infrastructure available, the experience analysis team can now enhance their processes and analyses to understand and manage the risk they face from lapses in a different way – for example by incorporating advanced machine learning and AI. This could bring significant improvements (such as speed, improved predictions and cost effectiveness) over traditional modelling approaches. Managing lapses can also be more granular and forward-looking, and feed into front-office processes such as marketing or retention strategies to improve future lapse rates.

New data sources, such as those related to customer interactions with an insurance organisation via an online platform, may require licensing permissions to be recorded and used for such a study, and must be compliant with internal practices and legal frameworks, such as the General Data Protection Regulations. Manipulating these vast datasets would require new data skills, moving away from spreadsheet analysis and towards dedicated programming languages such as R and Python.

Furthermore, the data needs to be in the optimal format for each machine learning model. For example, when building a lapse model, multiple models may be fitted and chosen based on run-time, cost, effort, frequency of use, accuracy and expert knowledge (such as multiple linear regression, classification and regression tree, random forest and gradient boosting machine models). The ability to assess the fit of each model, and measure the goodness of fit, will require a specialist skillset and experience with error measures in non-traditional applied statistics.

In addition, the ability to constantly monitor the performance of these models in light of changing data and interactions within the data will be essential.

Communication and interpretability of output and limitations will be key.

In conclusion, an updated framework, within which the insurance organisation must manage any additional risks, may be required. Having the right team with the right skillset to deal with the emerging risks, tools and techniques will be important for the second line, as well as ensuring there are internal practices, policies and frameworks that can help to continuously monitor this risk.


Tips for the risk function

While the industry is still grappling with how to close the gap between traditional modelling and the new risks introduced by implementing AI, we have seen organisations consider the following in their development – and think about how risk teams could be part of the adoption of AI.

  • Review the team’s skills and knowledge. While a solid actuarial base is essential, consider bringing in skills from wider industries, such as software engineering or data science skills, to provide a more holistic skillset. Also consider a strategy for any training programmes, to avoid getting lost in the vast online resources available.

  • Change the mindset. Have a five-to-10-year view for modernising the business, rather than being reactive to immediate changes. This includes thinking about what senior management and the board need to know today so they can prepare for the future.

  • Consider advanced tooling to help the risk manager to assess AI that comes with complexity that necessitates a different approach. Analytical tools that help with understanding specific risks such as bias, explainability or model performance measures will be essential.

  • Keep abreast of global regulatory developments in this space, as well as professional bodies that have been debating AI risk for some time from differing perspectives, such as the CFA Institute (bit.ly/CFA_AI-ethics) and the Institute of Electrical and Electronics Engineers (ethicsinaction.ieee.org).

  • Start designing the components of an AI risk framework that goes beyond what is currently in place. This includes approaches and processes that revolve around model inventories, risk score-carding, independent model assessments, business continuity, and AI-related key risk indicators or key performance indicators that will also include live robustness indicators, as well as ownership and accountability for the models and decisions used in the business.


Andrew Morgan specialises in innovation, data science and risk management at Deloitte

Valerie du Preez is a senior consulting actuary and founder of Actuartech

Natasha Naidoo is chief risk officer at Generali UK

Image Credit | IKON

ACT Dec21_Full LR.jpg
This article appeared in our December 2021 issue of The Actuary .
Click here to view this issue

You may also be interested in...

Lessons learnt from long-term care insurance in Israel

Lessons learnt from long-term care insurance in Israel

Amiad Ben-Meir and David Zaray-Mizrahi set out the successes and failures of long-term care insurance in Israel, and the lessons learnt by the country’s insurance sector along the way
Wednesday 1st December 2021
Open-access content
Considering the concept of exclusion-free insurance

Considering the concept of exclusion-free insurance

Richard Hartigan discusses the possibility of an insurance policy without exclusions
Wednesday 1st December 2021
Open-access content
Under the microscope: Financial regulation in small states

Under the microscope: Financial regulation in small states

Servaas Houben and Ronald Ketellapper explore the challenges of financial supervision in small countries, looking in particular at Curaçao
Wednesday 1st December 2021
Open-access content
Rated 0: Getting ready for Ogden changes

Rated 0: Getting ready for Ogden changes

Mohammad Khan, Francisco Sebastian and Andrew Corner share the Ogden Discount Rate Working Party’s findings on a potential new rate, and whether the insurance sector is prepared for one
Wednesday 1st December 2021
Open-access content
Jørgen Randers: An end to growth?

Jørgen Randers: An end to growth?

Professor Jørgen Randers talks to Chris Seekings almost 50 years after the publication of his report The Limits to Growth, and outlines what we can expect for the world during the next half-century
Wednesday 1st December 2021
Open-access content
Game over for game theory and vaccinations

Game over for game theory and vaccinations

Game theory is no basis for decision-making when it comes to normative issues such as vaccination, says Ronald Meester
Wednesday 1st December 2021
Open-access content

Latest from Risk & ERM

KV

Liability-driven investments: new landscape

What now for liability-driven investments, after last year’s crash in the market? Pensions experts Rakesh Girdharlal and Moiz Khan say it should lead to a more balanced approach
Wednesday 1st February 2023
Open-access content
cj

Natural capital investing

Chris Howells and Andrew Dreaneen discuss how today’s investments in natural capital profit portfolios as well as the planet and humanity
Wednesday 1st February 2023
Open-access content
bl

'Takaful' models of Islamic insurance

Ethical, varied and a growing market – ‘takaful’ Islamic insurance is worth knowing about, wherever you’re from and whatever your beliefs, says Ali Asghar Bhuriwala
Wednesday 1st February 2023
Open-access content

Latest from General Insurance

td

Brain power

The latest microchips mimic cerebral function. Smaller, faster and more efficient than their predecessors, they have the potential to save lives and help insurers, argues Amarnath Suggu
Wednesday 1st March 2023
Open-access content
bl

'Takaful' models of Islamic insurance

Ethical, varied and a growing market – ‘takaful’ Islamic insurance is worth knowing about, wherever you’re from and whatever your beliefs, says Ali Asghar Bhuriwala
Wednesday 1st February 2023
Open-access content
il

When 'human' isn't female

It was only last year that the first anatomically correct female crash test dummy was created. With so much data still based on the male perspective, are we truly meeting all consumer needs? Adél Drew discusses her thoughts, based on the book Invisible Women by Caroline Criado Perez
Wednesday 1st February 2023
Open-access content

Latest from General Features

yguk

Is anybody out there?

There’s no point speaking if no one hears you. Effective communication starts with silence – this is the understated art of listening, says Tan Suee Chieh
Thursday 2nd March 2023
Open-access content
ers

By halves

Reducing the pensions gap between men and women is a work in progress – and there’s still a long way to go, with women retiring on 50% less than men, says Alexandra Miles
Thursday 2nd March 2023
Open-access content
web_Question-mark-lightbulbs_credit_iStock-1348235111.png

Figuring it out

Psychologist Wendy Johnson recalls how qualifying as an actuary and running her own consultancy in the US allowed her to overcome shyness and gave her essential skills for life
Wednesday 1st March 2023
Open-access content

Latest from Data Science

gc

Free for all

Coding: those who love it can benefit those who don’t by creating open-source tools. Yiannis Parizas outlines two popular data science programming languages, and the simulator he devised and shared
Wednesday 1st March 2023
Open-access content
il

When 'human' isn't female

It was only last year that the first anatomically correct female crash test dummy was created. With so much data still based on the male perspective, are we truly meeting all consumer needs? Adél Drew discusses her thoughts, based on the book Invisible Women by Caroline Criado Perez
Wednesday 1st February 2023
Open-access content
res

Interview: Tim Harford on the importance of questioning our assumptions

Tim Harford speaks to Ruolin Wang about why it’s so important to slow down and question things from emotive headlines to the numbers and algorithms we use in our work
Wednesday 30th November 2022
Open-access content

Latest from Valerie du Preez

web-p17-19-Data-Science-Particle-connection-concept-Getty--119

Built to skill: embracing modern data science tools and techniques

By embracing modern data science tools and techniques, actuaries can operate more efficiently, increase value and better manage risk, say Valerie du Preez, Xavier Maréchal and Anja Friedrich
Friday 28th January 2022
Open-access content

Latest from December 2021

Puzzles-iStock-1146577830.jpg

Puzzles December 2021

The November puzzles and the solutions are viewable in PDF format only.
Wednesday 1st December 2021
Open-access content
People and society news: December

People and society news: December

Mr Piyush Majmudar (pictured, on left, with Tan Suee Chieh) passed away on 4 November 2021, just short of 90 years old. He qualified as a Fellow of the Institute of Actuaries in 1968 , becoming part of the early generation of actuaries in India.
Wednesday 1st December 2021
Open-access content
Best kept secrets

Best kept secrets

Adeetya Tantia reflects on increasing concern around data privacy, and whether it may halt the predicted personalisation of insurance pricing
Wednesday 1st December 2021
Open-access content
Share
  • Twitter
  • Facebook
  • Linked in
  • Mail
  • Print

Latest Jobs

Senior Manager - Building new team!

London (Central)
Up to £130k + Bonus
Reference
148845

Shape the Future of Credit Risk Model Development

Flexible / hybrid with 2 days p/w office-based
£ six figure salary with excellent bonus potential + package
Reference
148843

Longevity Director

Flexible / hybrid with 2 days p/w office-based
£ six figure salary with excellent bonus potential + package
Reference
148842
See all jobs »
 
 
 
 

Sign up to our newsletter

News, jobs and updates

Sign up

Subscribe to The Actuary

Receive the print edition straight to your door

Subscribe
Spread-iPad-slantB-june.png

Topics

  • Data Science
  • Investment
  • Risk & ERM
  • Pensions
  • Environment
  • Soft skills
  • General Insurance
  • Regulation Standards
  • Health care
  • Technology
  • Reinsurance
  • Global
  • Life insurance
​
FOLLOW US
The Actuary on LinkedIn
@TheActuaryMag on Twitter
Facebook: The Actuary Magazine
CONTACT US
The Actuary
Tel: (+44) 020 7880 6200
​

IFoA

About IFoA
Become an actuary
IFoA Events
About membership

Information

Privacy Policy
Terms & Conditions
Cookie Policy
Think Green

Get in touch

Contact us
Advertise with us
Subscribe to The Actuary Magazine
Contribute

The Actuary Jobs

Actuarial job search
Pensions jobs
General insurance jobs
Solvency II jobs

© 2023 The Actuary. The Actuary is published on behalf of the Institute and Faculty of Actuaries by Redactive Publishing Limited. All rights reserved. Reproduction of any part is not allowed without written permission.

Redactive Media Group Ltd, 71-75 Shelton Street, London WC2H 9JQ