Machine learning (ML) models have already been around for decades. The exponential growth in computing power and data availability, however, has resulted in many new opportunities for ML models. One possible application is to use them in financial institutions’ risk management. This article gives a brief introduction of ML models, followed by the most promising opportunities for using ML models in financial risk management.
Technological developments are changing the world around us at an ever-increasing pace. This speed of change has become the new reality – and it won’t be slowing down any time soon. What opportunities does this present to insurers and banks?
A part of the curriculum of the Econometrics & Mathematical Economics master’s degree given in the VU University Amsterdam is the course Time Series Econometrics. In this course, students are taught how to analyze time series with the aid of ‘state-space models’, on the assumption that observations over time (such as the content of the Nile, for example) are driven by non-observed factors.
Much has been done to define new regulations for the banking sector since the financial crisis. The prudential rules of Basel 3 with the so-called ‘final reform’ of December 2017 (commonly referred to as Basel 3.5 or Basel 4), for example, are as good as ready. So what can banks expect during the coming years?
Similar to other industries, the insurance industry is subjected to a wide array of changes driven by both business and technological forces fueled by innovation. This wave of change and innovation in the insurance industry, commonly cited as InsurTech, refers to the use of technology to squeeze out cost savings and effi ciency from the current insurance industry business model.