How to Secure Omnichannel Payments

In this article, we will explain how Feedzai helps banks protect different payment options from fraud with an omnichannel strategy for payments. We’ll specifically focus on how this approach helps two key teams on the front lines of fraud prevention: fraud analysts and data science teams.

Fraudsters Diversify Their Attack Channels

Traditionally, banks have crafted a risk strategy unique to different types of payments. Configuring the system for each channel individually is easier, but it misses the bigger picture. 

However, analyzing all data together is essential to improve threat detection, alert management, and reporting. The evolution of financial crime has exposed gaps in systems among disconnected channels. 

If banks can’t see fraud attempts and patterns across multiple channels aggregated, they are left with an incomplete view of the fraudster’s tactics and miss the opportunity to identify risk at an earlier stage. Cross-channel attacks require omnichannel payment solutions for detection.

A fragmented view of risk also contributes to increased operational costs and leads to headaches downstream. Financial institutions must address fraud losses because they could not prevent the fraud in time.

Siloed risk analysis by channel puts both fraud analysts and data science teams at a disadvantage.

Fraud Analysts Face Investigation Hurdles

When data is siloed by channel, investigating fraud alerts becomes cumbersome for fraud analysts. They spend more time trying to piece together information from different sources, slowing down investigations and making it harder to identify and stop fraudulent activities. 

Since fraudsters don’t limit themselves to a single channel, focusing solely on channel-specific risk strategies might cause analysts to miss important behavioral and transactional patterns occurring across other channels, increasing the bank’s risk exposure.

Data Scientists Bogged Down by Delays

Fraud metrics and profiles have traditionally been created separately for each channel. However, this approach can miss the broader patterns of fraud that span multiple channels, forcing data scientists to spend too much time connecting dots between different channels. 

When data scientists build metrics to detect fraud, they often do so through nightly batch processes or scheduled metrics jobs (SMJs). SMJs are required when computing metrics with windows exceeding 24 hours. This requires a lot of data storage and memory, which slows down the processing time. Frequently, an SMJ from the previous night bleeds into the following day, delaying the overall fraud system and impeding investigators from rapidly responding to emerging threats and protecting customers.

How Feedzai Secures Omnichannel Payments Differently

Analyzing data from different channels in isolation is no longer an efficient way to detect fraud. Criminals do not limit themselves to a single channel, meaning financial institutions can no longer afford to limit their fraud prevention efforts either. 

Omnichannel payments require an omnichannel fraud prevention strategy that benefits both fraud analysts and data scientists. Here’s how Feedzai delivers an omnichannel view of fraud risk 

Fraud Analysts: Unified View of Customer Activity

Feedzai’s Case Manager offers fraud analysts a unified view of customer behavior, payments, and digital activity across multiple banking channels. This vital capability gives fraud analysts a holistic view of payment risk across multiple channels.

Moreover, the fraud risk insights gathered within Case Manager are aggregated and analyzed in a single user interface. Analysts don’t need to scour different locations for transaction history across a range of payment channels. The interface displays where the alert originated, what triggered it, and the channel where the activity occurred. 

As a result, fraud analysts can make more effective decisions faster and catch more fraud. The key benefit is that fraud analysts spend less time on investigations and more time boosting their bank’s revenues.

Data Scientists: Richer and Faster Analysis

Using Pulse, Feedzai’s risk engine, data scientists can create profiles for omnichannel fraud detection by analyzing metrics from different channels for an enhanced risk decisioning process. The main principle behind omnichannel detection in Pulse is to enable data scientists and analysts to build rules and models that use data from multiple channels while still allowing teams to work on channels in isolation.

Feedzai equips data scientists with an advanced environment for data scientists and analysts to efficiently build, test, and deploy models to adapt to evolving threats – within days, not weeks.

Secure Omnichannel Payments with a 360-Degree View of Risk

Feedzai promotes a comprehensive 360-degree view of risk, enabling banks to monitor fraudulent activities across every available banking channel. Banks can better detect and prevent fraud before it escalates by taking this holistic approach to fraud, monitoring activities across multiple channels.

Feedzai’s approach combines monetary and non-monetary risk signals to identify fraud. Monetary risk signals include card transactions and transfers’ amount, frequency, and velocity. 

It also considers non-monetary digital activities. Monitoring login information, typing patterns, mouse movements, internet connections, and IP addresses is crucial in identifying suspicious activity before any money is taken.

For instance, a fraudster might acquire banking credentials and log into an account from a different device or location. These actions, such as navigating the account and copying details, are critical indicators of a potential account takeover, which may lead to unauthorized transfers.

Feedzai offers real-time omnichannel metrics through its Railgun technology. Railgun is Feedzai’s next-gen streaming engine that enables the calculation of real-time metrics. Unlike traditional scheduled metric jobs that can be slow and siloed, Railgun provides instant, cross-channel analysis with high volume and low latency. 

Data scientists and fraud analysts can access a unified, real-time view of risk, allowing for quicker, more effective decisions. By integrating omnichannel data into a single user interface, banks can develop comprehensive risk strategies across all channels, ensuring robust protection against fraud.