Beat Transaction Fraud with Proactive Detection

Digital identities have taken on new importance during the global pandemic. But as we’re expanding on the ways in which we interact with people and companies, the surface area for transaction fraud to occur continues to grow.

In a recent webinar, Managing Director at Crowe Global Financial Crime Analytics and former HSBC Financial Crimes Threat expert Haibo Zhang joins DataVisor’s Fang Yu and Tom Shell to illustrate how FIs can overcome transaction fraud with proactive fraud detection. 

The 3 Challenges in Transaction Fraud Detection for FIs

Zhang has noticed an increasing trend in transaction fraud throughout the pandemic, specifically in terms of phishing and malware being used to steal credentials and commit fraud. Knowing how transaction fraud occurs is only a small part of the problem. Three greater challenges, according to Zhang, are on the transaction fraud detection side:

Limited Real-Time Capabilities

Traditional fraud tools typically follow a damage control path, in which an event occurs and you’re left to handle the aftermath. Fraud teams lack early signals that indicate an attack is about to occur, and therefore cannot make decisions in real time.

Lack of Proactive and Adaptive Solutions

Technology is ever-evolving, and transaction fraud detection methods must evolve, too. Otherwise, companies will be vulnerable to new risks and new methods of attack. Existing models struggle to accommodate new tactics. They tend to decay quickly because fraudsters are continually changing their patterns, allowing them to stay at least one step ahead of fraud prevention teams.

Limited Data and Legacy Infrastructure

Fraud prevention tools rely on the integrity of their data. When data is imperfect or dirty, or when existing infrastructure cannot easily process or integrate new data sets, the integrity of your data suffers.

How UML Detects New Fraud Patterns

Rules-based and supervised machine learning (SML) are commonly used to detect known fraud patterns. However, because fraud tactics evolve so quickly, these tools cannot effectively touch unknown fraud. 

Unsupervised machine learning (UML) helps to fill in this gap by analyzing all user behaviors and data without labels in real time and identifying suspicious patterns. This allows fraud teams to detect attacks early and prevent them instead of doing damage control after the fact. 

For example, SML may review single transactions, such as a canceled ACH transfer. On the surface, this cancellation might appear legitimate. But by reviewing data on a holistic level, FIs might find that many similar transactions are occurring from a single user or IP address in a short span, which could indicate fraud.

A fraud detection platform that includes functionality for data validation and integration such as DataVisor’s Feature Platform puts centralized intelligence to work to establish data quality early. Once a user uploads data and maps the fields, the platform automatically analyzes data quality and flags potential issues, all while seamlessly integrating data from multiple sources.

Fraud detection built on a modern infrastructure enables rapid and accurate detection of known and unknown attacks, improving operational efficiency, and providing frictionless customer experiences at the same time. 

How DataVisor’s Proactive Fraud Detection Can Mitigate the Challenges

DataVisor’s comprehensive fraud detection platform takes a multi-layered approach to fraud detection that utilizes both supervised and unsupervised machine learning to stop fraud in its tracks. Built on a modern infrastructure, our platform is designed to help you make better decisions with your data by breaking down silos and enabling you to connect all user activities in real time. By integrating data from multiple sources and analyzing it in real-time via a clean interface that doesn’t require extensive data labeling and retraining, DataVisor can help you evolve alongside fraud patterns so that you can stay ahead of scammers.