Section 9 Flashcards
Describe the process of fraud detection set out in the article “Road testing:
machine learning and the efficiency of fraud detection”. [15]
A machine learning pipeline was developed to replace the existing rules-based
approach
* The aim was to detect more cases of fraud
* And speed up the claims process for valid cases
* Claims assigned a fraud likeliness score, with an upper and lower threshold for
intervention
* Claims below lower threshold fast tracked for payment
* Claims above upper threshold sent directly to anti-fraud action
* Claims between the thresholds were sent to the fraud management unit
* The upper threshold was set to minimise the number of claims falsely
identified as fraud
* The lower threshold was set by considering the number of claims that could
be investigated by the fraud management unit
* Exploratory analysis revealed the most important variables to include in the
model
* The most important variable was found to be the time taken to report the
claim from the purchase of the policy
* A supervised learning classification algorithm was adopted
* Three algorithms were tested along with an ensemble method that combined
the first three, the ensemble model was found to have the best predictive
power
* The AUC was used to select the most appropriate model
* The data was split: 60% for training, 20% for validation, 20% for testing
* If the model is to be adapted a monitoring framework will be essential
* This would compare actual and predicted fraud rates
One of the principles set out in the Guide for Ethical Data Science by the IFoA and
RSS is “Avoid Harm”. Describe what this means. [5
Examples of harm include:
* Financial loss or disadvantage
* Damage to reputation
* Exclusion from benefits or services
Practitioners can avoid harm by:
* Using data that has been ethically sourced – data has been provided
knowingly
* Obeying regulations
* Linking in privacy and ethics
Discuss current usage of data science within actuarial fields. [15]
- Data science is now well established in some actuarial fields
As discussed in Thematic Review: - Increased capacity, availability and profile of data science
- And new data sources introduces new challenges for actuarial work
- Increasing level of actuarial involvement
- In traditional areas of work and wider fields.
- Presents new opportunities for actuaries
- But actuaries will need continued professional development and support
- Actuaries need to be aware of safe usage and evolving regulation
Uses include: - Telematics (GI)
- Life insurance pricing (“The price is righter”)
- Fraud detection (“Road testing: machine learning and the efficiency of fraud
detection”)
Discuss regulation for actuaries working in data science. [8]
- Compliance with existing regulation remains relevant to data science (e.g.
GDPR) - Actuaries also need to operate within the public interest
- Given potential ethical and wider public interest issues, important to consider
regulation of professionals working within data science – actuaries or
otherwise - Insurers operating globally will have to comply with multiple data protection
regimes - Consent to use data is key
o For example, where data is collected from social media, have
policyholders and social media platforms necessarily given consent? - Insurers will need to have robust data governance processes to ensure they
are meeting their obligations - Potential conduct risk concerns for insurers
o Consider whether product design and pricing are putting consumer
needs first - Need for regulation to be fit for purpose, without being overly burdensome
Describe the key findings of the IFoA’s thematic review on the use of data science
and AI within the actuarial profession. [10]
- Increased capacity, availability, and profile of data science and AI tools all
feeds into a rapidly changing environment. This, coupled with technological
advances and ever-growing sources of data, potentially changes existing risks,
and introduces new risks where such tools are adopted in areas of actuarial
work. - There is an increasing level of actuarial involvement in AI and data science
across a range of domains, and also plans to further increase usage. - The range of applications is increasing, beyond GI pricing, into other
traditional actuarial areas of work. There is also some evidence of emerging
involvement of actuaries in wider fields. Participants in the review remarked
there may be challenges in ensuring standards and guidance remain
proportionate and appropriate to the growing applications of data science
and AI where actuaries may apply their skills. - Often actuaries will be working alongside data scientists, and other experts,
with organisations being more focused on relevant skills than professional
qualifications. This may bring challenges in maintaining demand for actuaries
in certain types of work, at a time where there is increasing demand from
employers to use data science and AI techniques. - There has been and continues to be extensive regulatory activity around the
globe, although still at different stages and pace of action. 2024 is likely to see
further developments and in certain jurisdictions a move from principles-
based guidance to more formal regulation. - The IFoA, and other actuarial regulators, already have standards and guidance
in place which is relevant to data science and AI work. There is a balance to
strike between specific standards to ensure the safe and responsible use of AI
by actuaries, and amendments to existing professional and ethical guidance
material. There is a risk that setting specific standards will be overtaken by
events, given the ongoing high-paced development of data science and AI. - At present there is material in parts of the underlying core curriculum for
students. Additionally, there have been lifelong learning opportunities, for
example through the IFoA Data Science certificate. There are current plans to
develop both of these strands to help ensure our members continue to be
well-placed to contribute to this field. - The IFoA has previously collaborated successfully with stakeholders in this
field. There exist wide-ranging opportunities to continue this, seeking out new
avenues to influence future paths for data science and AI. - There are a wide range of materials and sources available to actuaries to learn
more and seek views on this topic.
IFoA risk alert september 2023
Here are the key points from the IFoA Risk Alert (September 2023) summarized to demonstrate a clear understanding of the issues:
Emerging Risks with AI and Data Science:
- The alert highlights the growing use of artificial intelligence (AI) and machine learning (ML) in actuarial practice, emphasizing both opportunities and risks.
Key risks include algorithmic bias, lack of transparency (black box models), and potential misuse of AI in decision-making.
Ethical Considerations: - Actuaries are advised to uphold ethical standards, ensuring fairness, accuracy, and accountability when using AI-driven tools.
Transparency in the application of AI is critical to maintaining trust with stakeholders.
Professional Responsibility: - Actuaries must validate and document the methodologies behind AI tools, ensuring they align with professional standards and regulatory requirements.
Ongoing education and skill development are necessary to competently handle these technologies.
Governance and Oversight: - The alert stresses the importance of robust governance frameworks to oversee AI implementations.
Actuaries should collaborate with interdisciplinary teams to mitigate risks and ensure appropriate use of AI systems.
Focus on Bias and Data Quality: - Bias in data or models can result in unfair outcomes, particularly in insurance pricing or claims decisions.
Actuaries must critically assess data quality, address gaps, and evaluate the potential for discriminatory outcomes.
Regulatory and Public Interest Implications: - Regulatory bodies are increasingly scrutinizing AI applications in sensitive domains like insurance.
Actuaries are encouraged to prioritize the public interest, balancing innovation with ethical and societal considerations.
“EAGER-B” = acronym
Ethics
AI Risks
Governance
Education
Regulation
Bias
Key Points from What’s in the Box? Useful Findings on Black Box Models by The Actuary
Key Points from What’s in the Box? Useful Findings on Black Box Models:
Challenges of Black Box Models:
Random forests and other complex models can be as opaque as deep learning models due to input data complexity.
Key issues include identifying relevant features and presenting understandable explanations.
Techniques for Explainability:
SHAP (Shapley Additive Explanations) and global surrogate models were popular methods for interpreting black box predictions, though each has limitations like computational cost or scalability.
Iterative and Multidisciplinary Approach:
Successful model projects combined agile development with expertise in data science, engineering, and visualization.
Actionable vs. Non-Actionable Explanations:
Teams debated whether to include variables that consumers or decision-makers could influence versus fixed attributes like age.
Context and Audience Tailoring:
Effective explanations depend on domain knowledge and presenting information in ways suitable for different stakeholders.
Governance and Human-Machine Interaction:
Governance frameworks should address the role of algorithms alongside human decision-making.
“CRAISE”
Context and audience tailoring
Relevance of actionable explanations
Algorithms and governance
Iterative, multidisciplinary approach
Scalability of techniques
Explainability challenges
Key Points from Unfamiliar Territory for Artificial Intelligence-Related Risk by The Actuary
Unique Challenges of AI in Insurance:
AI introduces risks not addressed by traditional insurance frameworks, such as reputational, conduct-related, and regulatory risks.
Unlike static models, AI systems utilize dynamic, high-velocity data that can complicate validation and oversight.
Differences Between AI and Traditional Models:
AI models are often non-linear, making them harder to interpret or explain.
Data for AI models is diverse, less structured, and harder to validate compared to traditional datasets.
New infrastructure and code libraries needed for AI may be unfamiliar to traditional IT and actuarial teams.
Limitations of Current Validation Frameworks:
Traditional risk-based validation methods struggle with AI’s dynamic parameters and broader domains (e.g., chatbots).
Bias, interpretability, and non-quantifiable risks like reputational harm are underrepresented in existing practices.
Model validation teams may lack skills in programming and non-linear statistical methods required for AI.
Applications and Opportunities:
AI can enhance processes like policy lapse prediction by utilizing advanced techniques (e.g., gradient boosting, random forests).
These models require proper licensing, data preparation, and advanced statistical skillsets to ensure accuracy and compliance.
Monitoring model performance in dynamic environments is critical for maintaining accuracy over time.
Steps for Effective AI Risk Management:
Build multidisciplinary teams with skills in software engineering, data science, and advanced analytics.
Develop long-term strategies for adopting AI rather than reacting to immediate challenges.
Employ advanced analytical tools to address bias, explainability, and model performance.
Stay updated on global regulations and standards for AI ethics and risk management.
Framework for AI Risks:
Create an AI-specific risk framework that includes model inventories, risk scoring, independent assessments, and AI-specific KPIs.
Ensure robust governance with clear ownership and accountability for AI-driven decisions and models.
Acronym for Easy Recall:
“DREAMS”
Differences between AI and traditional models.
Risk frameworks for AI.
Ethics and regulations.
Applications and opportunities in AI.
Multidisciplinary skillsets.
Steps for managing AI risks.
key points from the article “The Price is Righter” from The Actuary
Here are the key points from the article “The Price is Righter”:
Sophistication in Pricing: Life insurance pricing has become more sophisticated, especially in the UK protection market, with a focus on granular analysis and demand models that consider customer behavior for better profitability understanding.
Shift from Traditional to Data-Driven Pricing: Traditional pricing methods based on sales volumes, competitiveness, and spreadsheets are being replaced by more data-driven approaches using advanced analytics, which allow insurers to adjust prices rapidly and more accurately.
Customer Behavior Modeling: Insurers are increasingly using analytics to model customer demand and elasticity, allowing better targeting of price changes and segmentation. This helps insurers predict how price changes will affect customer purchasing behavior and pricing margins.
Advanced Analytics and Machine Learning: The use of advanced techniques such as Gradient Boosting Machines (GBMs) is growing, though their complexity raises concerns around interpretability. Many insurers combine them with Generalized Linear Models (GLMs) for better understanding and deployment.
Fairness and Conduct Risks: The use of segmentation may lead to unfair pricing for certain market segments. The Financial Conduct Authority (FCA) considers price optimization acceptable if it is applied appropriately, with a focus on fair value in product governance.
Future Trends: The use of data and analytics in pricing will continue to expand, especially in protection products. These techniques will likely spread to other areas like annuities, underwriting, claims processing, and customer journey optimization, enhancing customer experience and market responsiveness.
For memory aid, consider this acronym: SACAF
Sophistication
Advanced Analytics
Customer Behavior
Acceptability of Fairness
Future Trends
