Description: Ways of closing the mortality protection gap, by leveraging some of the new technology, data and analytics to reduce the friction in the buying process.

Description: The article explores the possibility of using the emerging technology for life insurers to build a monitor system for better risk and profitability management.

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: The article focuses on the use of predictive analytics in the development of experience studies, pros and cons of using predictive analytics versus traditional techniques, and the implications for setting assumptions and improving profitability.

Description: This addresses the changing role for property-casualty actuaries as companies are looking toward actuaries to play a bigger role in moving the business forward.

Description: Highlights of two 2018 health events organized by the Colombian Association of Actuaries and sponsored by the Colombian Government. Seminar on Risk Adjustment and Predictive Modeling in Health. Seminar on Risk Management for the Health Insurers

Description: First article in a series that walks through the progression from developing LTC assumptions using traditional methods to doing so using predictive analytics.

Description: It talks about the major areas of data science and how to learn data science with the actuarial background.

Description: We compare two predictive analytics methods - GLM and GBM in terms of predictiveness vs. interpretability.

Description: How actuaries can become comfortable accessing, integrating and building complex data sets from big and broad sources of information to study the value of multidimensional drivers of change.