Description: Early in his career, Ehren Nagel, FSA, MAAA, scoured through podcasts and articles about predictive analytics, long suspecting this was a field with potential for him.

Description: A significant fraction of insurance risk is characterized by the tail of the claims distribution, where we have much less data to model probabilities accurately. Ignoring the tails can lead to underpricing of tail risk and mispricing. Estimation of the tails can be complex due to a bias-variance tradeoff--the further out we go into the tail the more accurately we can model the extreme tail (lower bias) but the less data we have (increased variance). The statistical tools of Extreme Value Theory can be leveraged to more accurately estimate both the incidence and the severity of extreme events given that they occur. using the Generalized Pareto Distribution.

Description: While many know that machine learning is a big deal and even have a general idea of what it is, many do not know what machine learning can do for them. As an alternative to citing achievements or explaining algorithms, this article outlines the challenges that occur in statistics and the solutions that machine learning offers. Some of those challenges include correlated predictors, missing data, data interaction, and prediction intervals. Corresponding solutions include regularization technics, missing value imputation with KNN, tree based models, and cluster analysis for backtesting.

Description: In this video, Dave Ingram FSA MAAA, a SOA board member and podcast host, discusses the ten AI risks that insurers face and the specific mitigations that can be used for each of them. He also explains the importance of having an AI Use Policy that covers various aspects of data governance, ethical guidelines, transparency, fairness, human oversight, and compliance. He concludes by noting that AI will evolve and insurers will need to monitor and remediate their AI applications accordingly. Contributors: Dave Ingram FSA, MAAA; Chis Lombardi, FSA, MAAA; Jon Forster, ASA, MAAA

Description: In this video Jing Lang emphasizes the importance of ethical considerations in AI utilization, particularly focusing on privacy, bias, transparency, and creativity. It highlights the need for collaboration among various stakeholders to address these concerns through regulations, diversity promotion, and ongoing discussions. Sources of bias in AI, such as underrepresented datasets and historical biases, are identified along with strategies to mitigate bias, including careful assessment of inputs and retaining algorithms. Contributors: Jing Lang, FSA, FCIA, MAAA FLMI; Scott Allen, FSA, MAAAA, Henry Chen FSA, FCIA, MAAA; Jon Forster ASA, MAAA

Description: Yi-Ling Lin, FSA, MAAA, FCA, started the SOA’s Predictive Analytics Certificate Program to keep pace with the impact big data and predictive modeling is having on the health care industry

Description: In recent years, vast amount of data from emerging experiences has helped insurers gain a deeper understanding of their policyholders and the factors driving their behaviors. However, due to limitations in traditional experience study methods, this understanding typically remains at the cohort level. This series of articles proposes a novel machine learning-based model that analyzes and predicts policyholder behaviors at the individual policy level. Policyholder embedding representations are learned using a Recurrent Neural Network (RNN) that takes into account the sequence of historical transactions associated with a policy. Additionally, a Neural Network with a multi-head architecture is trained to predict the policyholder's next action based on the policyholder embedding and other policy attributes. The resulting model offers several advantages over traditional experience studies and assumption-setting methods, including the ability to set policy-level assumptions that improve reserve accuracy, predict multiple behaviors simultaneously while capturing the interrelationships between them, and generate policyholder embedding representations that are useful for segmentation.

Description: Actuarial concepts of credibility can be extended to encompass additional information provided by new machine learning estimates. New statistical ideas also indicate how to generalize to blend arbitrarily many estimators, while not double-counting common information. For example, historical claim costs and risk scores both leverage diagnosis code information. We introduce two new estimators, one based on partial correlation and the other based on the machine learning-derived Shapley statistic, to address these limitations.

Description: Generative AI is a frontier in artificial intelligence that can create new data from existing data sets, with applications in risk modeling, product development, data analysis, and more. This video presented by Chris Smith, ASA, MAAA introduces the fundamentals, technologies, and implications of generative AI for actuarial science, as well as the ethical considerations and limitations that actuaries must address. Learn how to harness the power of generative AI ethically and innovatively in your actuarial practice. Contributors: Chris Smith, ASA, MAAA; Jason Reed, FSA, MAAA; Sam Behrend, FSA, MAAA ; Jessica Calafell, FSA, MAAA; Jon Forster ASA, MAAA

Description: In this video, Joe Long ASA, MAAA, a consulting actuary and data scientist at Milliman, explains the concept of the bias-variance trade-off and its connection to actuarial credibility theory. He demonstrates how machine learning techniques like penalized regression can help actuaries balance bias and variance in their projections, using long-term care experience studies as an example. He also discusses the broader applicability and future directions of integrating advanced analytics into actuarial practice. Contributors: Joe Long ASA, MAAA; Jess Calafell, ASA, MAAA; Jon Forster ASA, MAAA