Description: On behalf of The Actuarial Demography Team of CREAD (Centre for Research in Applied Economics for Development, Algiers, Algeria), Farid FLICI hosted Arpita DAS from Actuaries Without Borders (AWB) belonging to the International Association of Actuaries (IAA) to facilitate a 3 days training on "Data Science for Health & Life Insurance" for actuaries in Algiers. The training was held in CREAD's from 23th to 25th of December 2018 and was attended by actuaries and statisticians from insurance companies, public insurance regulators, social security organizations, and academia.

Description: Ever wanted to do a Kaggle data science competition, but didn't know where to start? This presentation meets the needs of actuaries interested in predictive analytics and competitive data science. The presentation will describe the nature of competitive data science, and provide insights into lessons learned by competing in the Airbus Ship Detection Challenge at Kaggle, as well as other Kaggle competitions. The presenters will provide an introduction to neural networks and describe the nature of the Airbus Ship Detection Challenge and other Kaggle competitions, plus a solution approaches that can be used to obtain top scores using Convolutional Neural networks (CNNs), random forests and boosted trees.

Description: Article about Prompt Engineering Tips.

Description: This Q&A article discusses the use of machine learning to analyze unstructured data. Supriya Ramachandra, a data scientist at SIKKA, explains how her team uses natural language processing techniques, technical aspects of neural networks, LSTM modeling, and natural language processing. Machine learning allows for the classification of large amounts of unorganized data, providing actionable information for actuaries.

Description: Neural Networks are no longer limited to classification and regression solutions to numeric problems. CNNs add the ability to classify images, and GANs can create new images. Learn how these capabilities can provide competitive advantages to early adopters, how they work, and how to implement them.

Description: As the insurance industry continues to evolve due to a pandemic and rapidly changing economic and regulatory conditions, the role of data scientists and actuaries has become increasingly important. In an interview with the Atidot data science and actuarial team, we discuss what it is like to work at an InsurTech, how data scientists and actuaries can collaborate, and look at some concrete technical examples of where the innovation is having an impact. This article explores what collaboration can look like and how the role of data science and actuary may evolve in the InsurTech industry in the future.

Description: Artificial Intelligence (AI) is expected to change the insurance industry more in the next 10 years than it has changed in the past 100 years. Rapid advancements in computing power and the exponential growth of Big Data are creating more opportunities to incorporate AI techniques and methodologies into actuarial work. This session is for the practicing actuary who would like to learn more about AI and how to use it to analyze insurance data. At the conclusion of the session, attendees will be able to understand the fundamentals of AI, to build an AI model and to identify AI opportunities in their work.

Description: Natural Language Process (NLP) techniques can be used to find insights in large unstructured data sets and Word Embeddings are pervasive on a wide spectrum of NLP applications. These word representations improve downstream tasks such as Sentiment Analysis, Recommender System, and Topic Modeling. Learn how NLP transforms the Insurance Industry; and how word embeddings and pre-trained models improve the effectiveness and performance of NLP applications. NLP is increasing in importance for insurance companies. Additional application examples are available in session 36.

Description: Principal component analysis is an unsupervised linear transformation mainly used for dimension reduction. Determine key numerical variables with maximum variances in a dataset, identify the correlation and removal of redundant variables are the key aspects of this exploratory data analysis.

Description: Big data, predictive analytics, machine learning and AI will likely transform the insurance market and the actuarial profession. The actuarial profession is seeing changes many markets, including health and property and casualty. This article describes these trends and provides an introduction into what companies and the Society of Actuaries (SOA) are doing to adapt and capitalize on these trends.