With the trend towards increasing computational resources and larger datasets, the application of machine learning (ML) in finance has gained attraction. Financial Institutions are interested in how and where ML models can be of added value in their business model.
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 (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.