Real-time Monitoring: The Future of Fraud Prevention

Moving money today happens in real-time nearly all the time. Services like Zelle, FedNow, RTP, and others have not only given customers the ability to transfer funds in real time—they’ve made it the standard. This major shift in payments has also caused a major shift in transaction fraud. The best way to combat these new fraud vectors? Real-time monitoring.

What is real-time monitoring?

Real-time transaction monitoring offers a proactive approach to fraud detection and finding fraudulent activities as they occur. Rather than relying solely on post-transaction analysis and manual reviews, real-time fraud detection can spot fraudulent activity in milliseconds.

This strategy involves continuously analyzing data, transactions, or user behaviors in real time and is one of the most effective security measures in a comprehensive fraud prevention strategy. It’s often used along with other measures like device fingerprinting, identity graphing, and supervised machine learning.

How real-time monitoring works

Real-time monitoring is often integrated within machine learning-based fraud platforms. It operates by continuously reviewing transactions to immediately detect and respond to potential fraudulent behavior as it happens. There are a few steps that go into making this process work.

Data Collection

Fraud detection systems aggregate transaction data from sources like transaction records, user profiles, device characteristics, geographical location data, and more. This real-time data forms a picture of overall customer behavior and forms the base on which monitoring takes place.

Data Ingestion

Once relevant data is collected, it’s ingested into the real-time monitoring system. During this phase, the data undergoes transformations, enrichments, and structuring to render it suitable for analytical purposes. Data ingestion pipelines are established to ensure the seamless flow of information into the monitoring platform.

Pattern Recognition and Analysis

Real-time fraud detection leverages a few techniques

• Baseline Establishment – A baseline establishes normal behavior. It encapsulates typical transaction patterns and user behaviors.
• Anomaly Detection – Sophisticated machine learning algorithms, statistical models, and rule-based systems continuously scrutinize incoming data streams in real-time. These systems compare present data points against the established baseline, actively searching for statistically significant deviations or anomalies.
• Behavior Analysis – Real-time monitoring systems scrutinize user behavior, transaction patterns, and interactions with systems or platforms. They also incorporate contextual information, such as the user’s location and device fingerprint, to discern potentially suspicious activity.

Alert Generation and Decisioning

When the system identifies an anomaly or detects a suspicious pattern in transaction activity, it promptly triggers an alert. These alerts are flagged for further investigation by the fraud detection team or, depending on predefined rules and thresholds, the system may take automated action to prevent potential fraud.

For example, the system could temporarily freeze the account, block the transaction, or initiate multi-factor authentication (MFA) to verify the user’s identity. Such real-time alerts help minimize the financial impact of fraud, ensuring that suspicious activities are addressed swiftly.

In more sophisticated systems, machine learning algorithms may continuously adapt and refine fraud detection patterns, autonomously adjusting responses to evolving threats, thereby improving the efficiency and accuracy of fraud prevention efforts.

Documentation and Reporting

Their outcomes, are meticulously documented. This includes details such as the transaction type, timestamp, account numbers, IP addresses, and any associated login credentials or phone numbers involved in the suspicious activity. The documentation also covers the specific steps taken to mitigate the fraud, such as account freezes, transaction reversals, or the activation of multi-factor authentication (MFA). Outcomes such as the success of the fraud prevention efforts or any financial losses incurred are also recorded.

This thorough documentation serves multiple purposes, including record-keeping for internal audits, regulatory compliance with laws such as the Gramm-Leach-Bliley Act (GLBA) or PCI DSS, and as a resource for fraud investigators. Comprehensive reports are generated to provide valuable insights into emerging fraud trends based on historical data, identify vulnerabilities in the system, and guide the creation of new fraud detection rules. These reports help improve the accuracy of the machine learning models by providing real-world examples to refine future fraud detection efforts, making the system more resilient against evolving threats.

Feedback Loop and Continuous Improvement

Real-time monitoring for fraud prevention operates as a dynamic and adaptive process within financial services organizations. They utilize a feedback loop to evaluate the effectiveness of their fraud prevention measures. This includes assessing the accuracy of alerts, the impact of actions taken, and the evolving tactics employed by fraudsters.

Financial services institutions then refine their fraud monitoring and prevention strategies by adjusting alert thresholds, rules, and machine learning models to enhance accuracy and reduce fraud risk, while minimizing false positives.

Benefits of real-time monitoring

1. Immediate detection: Real-time monitoring rapidly detects fraud and helps financial institutions (FIs) minimize potential damage and financial losses.
   Enhanced customer experience: Legitimate customers benefit from real-time fraud detection as it reduces the likelihood of inconveniences like false positives or delayed transactions.
2. Preventative action: Real-time monitoring enables FIs to take immediate and decisive action to prevent fraud. For instance, they can block suspicious transactions or freeze compromised accounts before serious damage occurs.
3. Improved accuracy: Real-time machine learning’s advanced algorithms and statistical models evolve and improve over time. This significantly bolsters the accuracy of fraud detection while minimizing false positives.
4. Scalability: Market-leading real-time machine learning systems are designed to scale seamlessly, accommodating large volumes of data and transactions. This scalability renders them suitable for organizations of varying sizes, from small enterprises to large corporations.
5. Adaptability to new threats: Fraudsters are constantly evolving their tactics. Real-time monitoring systems counter this by adapting to emerging fraud patterns and threats through continuous learning from incoming data.
6. Minimal Operational Disruption: Real-time monitoring operates unobtrusively in the background, ensuring that day-to-day business operations remain uninterrupted. This seamless experience maintains a high level of customer satisfaction.
7. Cost Savings: FIs using real-time fraud detection can save on high costs associated with investigations, legal actions, and compensation to victims in the event of a successful attack.

Types of Fraud Detectable by Real-Time Monitoring

Payment fraud

Real-time monitoring is exceptional adept at spotting payment frauds. It swiftly spots irregular spending patterns like unusually large or rapid transactions, transactions originating from unfamiliar locations, or multiple unsuccessful authorization attempts.

Synthetic IDs

Real-time monitoring systems can raise flags for suspicious activities related to account creation or login attempts. For example, if there is a huge rush of login attempts with incorrect passwords or login attempts occurring from geographically distant locations within a condensed time span, it may signify identity theft.

Account takeover (ATO)

Real-time monitoring can thwart ATO attempts by catching abrupt shifts in user behavior, such as logins from a new device, modifications to account settings, or unusual purchase patterns.

Phishing and social engineering

Both these tactics are an especially crucial piece of real-time payment fraud. In general, many frauds begin through phishing or social engineering scams. Real-time monitoring can expose accounts created through stolen information and adept fraud teams can use it to spot unusual sender activity that indicates coercion.

Credit and debit card fraud

Using known information from data breaches or spotting credential stuffing are ways real-time monitoring stops card fraud. It also sees unusual spending patterns in real-time and can block them immediately.

ACH fraud

Real-time monitoring detects ACH frauds early by scouring transactions to spot irregularities. It can look at both sender and receiver to see if one or both are engaged in fraud.

Money laundering

Money launderers use tactics like smurfing, where they deposit stolen money via many small transactions, and money mules, where a victim transfers stolen money on behalf of a criminal. Real-time monitoring spots these behaviors and links financial transactions together to reveal money laundering.

Crime rings

Using it’s linkage analysis ability, real-time monitoring systems piece together fraudulent activity and can reveal crime rings working together to apply for credit cards and loans, launder money, or trick victims into authorizing payments to fraudsters.

E-commerce Fraud

Real-time monitoring is pivotal in pinpointing fraudulent online transactions. It delves into factors such as shipping addresses, billing particulars, and purchasing behaviors. Any aberrations, such as high-value orders from nascent or infrequently used accounts, can serve as triggers for alerts.

How to get real-time monitoring for your fraud platform

If you have a machine-learning-powered fraud platform already, you can add real-time monitoring yourself or select a fraud platform vendor with experience in the best ways to combat fraud. Building the fraud detection solution yourself requires a full team, a budget for testing and fixes, and fraud investigators to manage the platform.

On the other hand, choosing a real-time monitoring platform to add into your fraud strategy can save time and still have fast implementation. DataVisor’s platform installs in just weeks and quickly offers FIs real-time monitoring and the best combination of advanced technologies such as supervised and unsupervised machine learning — powered by artificial intelligence — on the market.

DataVisor can help you combat fraud without affecting the customer experience or impacting customer trust. Read a case study or see a customized demo of how this platform can integrate into your fraud prevention plan, schedule a time to chat with our team.