In April, we published an article showing that Machine Learning (ML) can improve the prediction accuracy of the Cure Rate (CR) in Loss Given Default (LGD) models. This is the second part of our two-part series blog. We test several ML methods on a dataset in the context of LGD Modelling and evaluate the potential […]

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How Artificial Intelligence (AI) can Steer Sustainable Smart Cities, like Amsterdam.

Why do Financial Institutions (FI) have a major role to play in the transition to sustainability (ESG)?

Design great customer experiences with predictive consumer insights.

Can the microbes inside us help us decide what foods to eat, predict our age, or even tell us what cosmetics to use? Trillions of microorganisms live on and inside the human body, in what is known as the human microbiome. Over the past decade, the microbiome has become one of the hottest areas in […]

New Economy and Amsterdam Data Collective have entered into an inspiring and ongoing conversation, discussing the potential role of governments and financial institutions in the transition to a more sustainable economy. What kind of data-driven strategies need to be incorporated to lead the way into a more robust and sustainable future? What opportunities will arise […]

Amsterdam Data Collective continues to grow and expands its product and service offering for Retail clients and Marketeers. Our new colleague Hans van Avendonk joins us as Senior Manager, with a special focus on data science for marketing and customer experience. Let’s meet him. First, can you tell me a little bit about yourself? Since […]

Integrating Machine Learning methods into your credit risk modelling landscape is a great idea. It allows the user to find new, potentially non-linear, patterns in the data. However, simply applying Machine Learning methods onto a data set is not a guaranteed success. Most methods give fairly good results from the get-go. However, to get the […]

Why are marginalized groups disproportionately affected by the negative aspects of Artificial Intelligence (AI)? And (how) can we move towards the other side of the medallion: benefiting vulnerable groups in society – vulnerable or marginalized meaning perceived and made vulnerable by institutionalized discrimination and through the outdated misconception of the survival of the fittest- through […]