Card Fraud Detection and Prevention

By 2031, card industry losses due to fraud are predicted to collectively amount to over $400 billion. Unfortunately, running fraud investigations for every transaction is both costly and inefficient. Innocent customers can also view such investigations which can lead to negative customer experience and potential customer churn.

FinTechs are fighting the constant evolution of fraudulent techniques. Fraudsters are continuously trying to stay one step ahead by finding new ways to exploit vulnerabilities—making it difficult for FinTechs to keep up and detect emerging fraud patterns effectively.

With ML Studio, you can build precise predictive models that identify and prioritize potentially fraudulent activities. This approach empowers the fraud units of card companies to then architect data-driven queues—only focusing on incidents that genuinely warrant attention. Besides wasting resources on unnecessary investigations—you will not be bothering customers with unnecessary messaging.