the total cost to banks and credit unions for each $1 in direct fraud losses


growth in deposits for the top 25 US banks over the past decade, despite a 15% decline in branches

$14.7 Trillion

in value will be gained by AI’s early adopters by 2030, especially by banks and financial services companies

Retail Banking

New Applications and Transactions

De-risking Digital Banking

The Challenge

Retail banking services have become immensely more popular due to mobile applications and their associated convenience. Inherent in these apps is an immense increase in risks due to remote access and new technology.

The Solution

Early AI adopters have higher sales growth and profit margins — and the performance gap with the laggards is expected to widen further.

Customizations of models can further expose previously-undetected fraud or optimize for any risk-related outcome.  Furthermore, multiple models can be applied to different transaction streams for maximum accuracy and flexibility.  

Recommended Products & Solutions

To the Victor Go the Spoils

Early experimentation with AI-enhancements in consumer banking is critical.  Fraud.net makes the complex process of applying AI in risk-based use cases much easier.  With minimal seed data about the transaction, Fraud.net can greatly increase the quantity and quality of the data, automatically test thousands of model algorithms and parameter combinations, and generate an optimized model, helping the bank make better decisions more quickly.


Make Better Decisions

Professional fraud rings have long been attacking merchants, shipping high-value goods to vacant homes, changing neighborhoods and IPs to circumvent existing detection efforts.
Professionals and opportunists can both be quickly exposed using machine learning, collective intelligence and ‘linked-entity’ analyses, all available in Fraud.net’s real-time enterprise detection and analytics platform.