This article will explore the ways in which data science is driving change in finance, focusing on risk management, fraud detection, and other innovative applications.

Amidst the ever-evolving landscape of data-driven industries, the battleground against fraud and money laundering remains relentless. However, a ray of hope emerges from the pioneering research of ...

Generative AI can produce similar payment sequences, simulating a fraud attack on a card, which augments training data to assist the fraud detection ML tools and help them to perform better.

As companies continue to innovate and embrace new technologies, utilizing geolocation data will be key to advancing fraud detection efforts.

How have AI and machine learning improved fraud detection in the fintech industry? What specific applications does this technology touch, and what mechanisms complement it?

The increasing digitalization of banking services has led to a surge in financial fraud, necessitating advanced detection systems.

Bespoke fraud ML models are powered by algorithms that learn from historical data, picking up on behaviors and characteristics commonly associated with fraud.

It also protects multiple payment channels from sharing data between them. The platform supports the import and export of fraud model components, which allow fraud analysts and data scientists to ...