Understanding Modern Transaction Monitoring: Fight APP Fraud Before It Happens

Authorized Push Payment (APP) fraud is a growing challenge for the financial industry. In the UK alone, APP fraud resulted in a nearly £500 million loss in 2022 which is 40% of total fraud losses. 

APP fraud is particularly challenging because it involves payments authorized by the account holder but carried out under the influence of fraudsters using misleading tactics. Traditional monitoring systems struggle to detect these fraudulent transactions, as they rely on customer consent to initiate payments.

In response, Financial institutions are adopting advanced transaction monitoring techniques that analyze customer behavior and transaction history to detect unusual patterns.  In this blog, we’ll explore how transaction monitoring can mitigate APP fraud and examine how advanced tools like Lucinity’s solutions empower institutions to respond to changing fraud tactics with accuracy and efficiency.

What is APP Fraud?

In APP fraud, scammers manipulate victims into transferring funds to fraudulent accounts. Fraudsters employ advanced social engineering to convince victims to make transactions by acting as legitimate entities such as banks, service providers, or friends.

APP fraud exploits user-authorized transactions, creating a major challenge for traditional security systems that typically rely on unauthorized activity as a warning sign. It is often based on convincing social engineering tricks that target an individual’s sense of urgency or familiarity.

APP Fraud Tactics and Their Financial Impact

Fraudsters implement multiple manipulative techniques that make APP fraud especially difficult to detect and mitigate. Common APP fraud schemes include:

• Impersonation Scams: Fraudsters pretend as trusted entities, such as banks or suppliers to pressure victims and create urgency in transferring funds.
• Invoice Scams: Businesses receive fraudulent invoices that appear legitimate, causing them to pay scammers rather than actual vendors.
• Romance Scams: Victims develop online relationships with fraudsters who eventually request money under pretenses.
• CEO Fraud: Fraudsters pretend to be high-level executives and request immediate wire transfers.
• Other Impersonations: Acting as a government representative, tech support, or a trusted acquaintance of the victim.

These tactics used by fraudsters are costly. In the UK, consumers lost over £240 million to APP fraud in just the first half of 2023. Worldwide losses from APP fraud are expected to reach $6.8 billion by 2027.

The speed and irreversible nature of real-time payments leads to irrecoverable losses. This makes advanced monitoring systems important for detecting fraud as early as possible.

How Modern Transaction Monitoring Can Prevent APP Fraud

Transaction monitoring acts as a defense system in detecting and preventing APP fraud. Advanced monitoring systems can alert compliance teams to potentially fraudulent activity before it results in a financial loss by analyzing payment transactions in real time and identifying suspicious patterns. Effective transaction monitoring can include:

Real-Time Alerts and Notifications: 

The high speed of digital payments has increased demand for AI-driven AML systems that can quickly analyze large volumes of data, even in real time. Michael Hsu, the acting US Comptroller of the Currency stated that faster digital payments are leading to digital fraud pushing banks to build the right brakes for a more real-time financial system.

When suspicious transactions are detected, the compliance team can use immediate alerts to allow quick intervention to prevent potential fraudulent transfers from being finalized. 

These systems trigger additional verification steps by setting thresholds based on transaction amounts, frequencies, or other anomalies giving compliance teams the tools to act quickly in a dynamic financial environment.

Machine Learning and AI Analytics

Advanced data analytics powered by machine learning enable systems to detect anomalies based on historical transaction patterns. These systems become increasingly accurate at identifying complex changes in behavior that may signal fraud in past data.

Selecting an AI system suited to specific use cases is essential, the system should offer real-time monitoring and incorporate tools like machine learning and natural language processing (NLP) for customer onboarding and KYC processes.

Additionally, predictive analytics equips banks to evaluate unusual behaviors proactively, helping compliance teams focus on genuine threats more efficiently.

Behavioral Analytics: 

Monitoring user behavior such as typing patterns, transaction timing, and transactions between accounts helps distinguish legitimate from fraudulent activity. Behavioral analytics are useful in recognizing deviations from a user’s normal actions.

Advanced behavioral analytics are important for distinguishing legitimate activity from potential fraud by tracking patterns in user behavior, such as login frequency, transaction timing, and even typing habits.

This technique enables banks to detect hidden relationships within data and capture emerging fraud tactics, providing a proactive defense mechanism against unusual behavior indicative of APP fraud.

Geolocation Tracking: 

Tracking the geographical origin of transactions can help flag potentially suspicious activity. For example, a transaction request from a high-risk region or a sudden shift in the location of transactions can trigger an alert for compliance review.

Tracking the geographic origin of transactions becomes important especially when transactions come from high-risk areas or display sudden changes in location.

Data visualization techniques such as relationship graphs and geographic mapping visualize these patterns allowing analysts and nontechnical users to observe changes in risk levels, geographic distributions, and relationships among entities involved in suspected fraudulent activities. 

Proactive Strategies for Strengthening APP Fraud Prevention for Organizations

APP fraud can deteriorate an institution’s reputation and customer trust, making it urgent to use proactive measures to counter it:

Real-Time Protection Against Phishing and Website Impersonation

APP scams often involve phishing links that direct users to realistic-looking fake websites. Real-time digital risk protection can help detect these phishing attempts as they occur. 

For example, when a customer clicks on a phishing link, immediate alerts are triggered to notify the institution of the attack details. Customers also receive warnings on the fake site and any exposed data is masked to prevent further misuse.

AI-Powered Transaction Monitoring and Verification

A multi-layered AI approach to monitoring and verification helps identify suspicious transactions. Key components include:

• Scenario-Based Systems: Flags transactions deviating from expected patterns based on the predefined scenarios.
• Machine Learning: Detects fraud patterns and subtle anomalies missed by traditional methods.
• Human Review: Ensures that flagged transactions undergo an additional layer of scrutiny.
• Enhanced Verification: High-risk transactions should require out-of-band confirmation or biometric checks.

Strengthen Email Security

Fraudsters frequently create fake email addresses to deceive customers. Strong email security measures such as verifying email domains and monitoring for signs of fraud are important features of transaction monitoring. 

Providers can track domain reputation, while user email clients apply spam filters and display cues to help users recognize legitimate emails.

Monitor Social Media with AI

Fraudsters are increasingly using social media to impersonate brands. AI-driven tools can conduct adverse media screening or fraudulent conversations to flag potential impersonation attempts. 

This allows institutions to respond quickly and reduce the likelihood of social media-based fraud.

Enforce Strict Controls for Business Accounts

Account Takeovers (ATOs) often occur due to unauthorized internal or external access. To mitigate this risk, organizations should:

• Use Multi-Factor Authentication (MFA) to secure system access.
• Apply the Maker-Checker Principle for transaction approval, which mandates that there must be at least two individuals for a transaction to complete.
• Implement Protocols for Urgent Requests with strict verification.
• Conduct Regular Access Reviews to ensure access levels are appropriate.

Robust Customer Authentication, Education, and Reporting

Strengthen customer authentication and education to empower customers against APP fraud. Implement:

• Multi-factor authentication (MFA) and biometric verification.
• Behavioral biometrics is used to analyze user actions, such as typing patterns.
• Customer Education programs to inform about common fraud tactics.
• Efficient Reporting Mechanisms to enable quick identification and response to potential fraud.

How Lucinity and Resistant AI Help Financial Institutions Prevent APP Fraud

Lucinity’s solutions provide financial institutions with powerful tools to detect and prevent APP fraud, using real-time AI insights and customizable interfaces to protect assets and customer trust.

• Transaction Monitoring through Partnerships: Lucinity partners with Resistant AI, Sift, Neterium, and other trusted tools to empower financial institutions with integrated data-driven tools that strengthen transaction monitoring, compliance, and APP fraud detection and prevention.
• Scenario Builder for Transaction Monitoring: Lucinity’s configurable scenario builder allows compliance teams to adjust detection strategies as fraud tactics grow. Customizable parameters ensure that alerts are contextually relevant and focused, helping reduce false positives and prioritize genuine threats.
• Case Manager: The Case Manager unifies data from various sources, including Resistant AI monitoring tools, into a centralized platform. This integration allows compliance teams to review and manage all alerts in one place, improving investigative efficiency and enabling quick, consistent responses to APP fraud.
• Luci Plugin: The Luci Plugin enhances real-time monitoring capabilities across web-based applications, detecting suspicious activity and anomalies. The Luci Plugin enables proactive intervention before fraudulent transfers are finalized by flagging high-risk transactions and providing alerts directly within the systems teams already use.
• Customer 360: Customer 360 creates a dynamic view of customer profiles by consolidating transaction history, behavioral data, and risk indicators. This profile-focused approach detects patterns and anomalies associated with APP fraud, providing compliance teams with deeper insights into customer behavior for more accurate risk assessment.

Final Thoughts

APP fraud is an increasingly growing threat that uses social engineering to deceive customers and compromise financial security. An effective transaction monitoring strategy, supported by high-quality data, enables financial institutions to protect customers proactively. 

Here are key takeaways to enhance APP fraud prevention:

1. Focus on high-quality data for deeper insights, enabling proactive fraud detection based on ‘fraud signals’ like transaction patterns and behavior.
2. Leverage advanced analytics and machine learning to predict fraud trends, helping institutions stay ahead of evolving fraud tactics.
3. Continuously improve customer verification processes to ensure all transactions are validated and securely authenticated.
4. Invest in customer education and collaborative efforts across the industry to strengthen collective defenses against fraudsters.

To learn more about effective monitoring tools and a proactive approach that can help financial institutions reduce APP fraud risk, check out Lucinity.

FAQS

1. What is APP fraud?

APP fraud occurs when fraudsters trick people into authorizing payments to them. It’s hard to detect because the payments appear legitimate.

2. How does transaction monitoring help with APP fraud?It detects unusual transaction patterns, such as high-value or out-of-character transfers, allowing banks to flag suspicious activity in real time.

3. Can AI improve fraud detection accuracy?Yes, AI learns from data to recognize subtle fraud patterns, reducing false positives and adapting to new fraud tactics.

4. How does Lucinity help prevent APP fraud?Lucinity integrates with Resistant AI to provide real-time monitoring and customizable scenarios, helping banks detect and prevent APP fraud.