Latest Insights

Can machine learning predict the probabilities of default?

According to Moore’s law, computing power doubles up each two years. This performance increase in computing power makes machine learning increasingly efficient each year, and widely applicable. But does this also apply to credit risk issues?

Machine learning in risk management

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.

Stay focused on objectives

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?

Are more complex models always better?

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.

Risk management in the wake of Basel 3:

The future of banking

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?