This bibliometric review examines the research state of artificial intelligence (AI) and
machine learning (ML) applications in the BFSI (Banking, Financial Services and Insurance) …

Explainable Artificial Intelligence (XAI) models allow for a more transparent and
understandable relationship between humans and machines. The insurance industry …

The COVID-19 pandemic has affected different sectors of the economy, including insurance,
and has become a problem and a clear catalyst for innovation. The pandemic has …

Insurance is a data-rich sector, hosting large volumes of customer data that is analysed to
evaluate risk. Machine learning techniques are increasingly used in the effective …

Accurate forecasting of insurance claims is of the utmost importance for insurance activity as
the evolution of claims determines cash outflows and the pricing, and thus the profitability, of …

We developed a methodology for the neural network boosting of logistic regression aimed at
learning an additional model structure from the data. In particular, we constructed two …

Machine learning, a subfield of artificial intelligence, emphasizes the creation of algorithms
capable of learning from data and generating predictions. However, in actuarial science, the …

Ensuring food security against climate risks has been a growing challenge recently. Weather
index insurance has been pointed out as a tool for increasing the financial resilience of food …

Recent years have seen rapid increase in the application of machine learning to insurance
loss reserving. They yield most value when applied to large data sets, such as individual …

Tree-based models have gained momentum in insurance claim loss modeling; however, the
point mass at zero and the heavy tail of insurance loss distribution pose the challenge to …