The financial services industry is using machine learning to revolutionize its processes and rapidly improve financial outcomes, and its potential seems limitless. That’s why the University of ...

Anomaly detection is one of the more difficult and underserved operational areas in the asset-servicing sector of financial institutions. Broadly speaking, a true anomaly is one that deviates from the ...

This report from Feedzai, 'Demystifying Machine Learning for Financial Institutions', looks at how machine learning operates, and how it can impact the way banks detect and prevent known types of ...

About 2.5 billion people around the world are underserved by traditional financial institutions. For traditional banks and loan companies, these individuals technically don’t even exist: According to ...

Artificial intelligence and machine-learning technologies have evolved a lot over the past decade and have been useful to many people and businesses, especially in the realm of finance, banking, ...

WEST LAFAYETTE, Ind. — Purdue University is offering a new all-online master’s degree in data science in finance with a concentrated curriculum focus on machine learning to solve modern financial ...

What is the role of artificial intelligence in the financial services industry? AI is proving to be a powerful tool for financial institutions looking to improve their operations, manage risks, and ...

The financial sector is anticipated to experience a notable surge in fraudulent activities, leading to projected losses exceeding $40 billion by 2027. This increase marks a significant uptick from ...

My company, Kickfurther, has carved out a niche by connecting businesses in need of funding for their retail inventory with buyers of that inventory. A key component of this business model is the ...

Technology in financial services can be somewhat of a double-edged sword. On one side, new technological innovations, like artificial intelligence (AI) and machine learning (ML), are striving to make ...

Learn what overfitting is, how it impacts data models, and effective strategies to prevent it, such as cross-validation and simplification.