Machine learning in fraud detection performs better when we know the strengths and limitations of different analytic toolkits, and understand the factors that determine accuracy.

Until now, it has been very difficult for a lender to understand the pressures on any consumer in terms of their ability to absorb more credit and pay off the required instalments, without placing unbearable stress on their finances.

As a result of the rise in application fraud due to EMV adoption in the US, Citi is exploring new authentication protocols and new analytics.

When it comes to application fraud, machine learning analytics must be a critical part of a financial institution’s control strategy.

How is application fraud evolving? And what should fraud leaders be doing to manage fraud risk in a digitally connected world?

It’s raining data left and right, and you need to start by understanding what data you already have to fight application fraud and where there are gaps.

When we talk about brute force application fraud and bot-net attacks, what are we really talking about?

Fraud and security are a top priority for financial institutions — but just stopping fraud isn’t enough. Managing fraud is a balancing act that starts with knowing your fraud risk appetite.

Identity theft, also known as third-party fraud losses, is just the tip of the application fraud iceberg. First-party fraud can be 10 times as much.

Lenders are looking for new ways to connect with the estimated 3 billion people worldwide who fall outside the credit ma...