Safeguarding Real-Time Payments Infrastructures - The Imperative for Advanced Fraud Risk Management in Banks

As real-time payments grow worldwide, fraudsters are becoming more sophisticated, exploiting vulnerabilities in fast-paced transaction environments. Traditional fraud detection methods are no longer enough. Banks need modern, AI-driven solutions that can detect fraud in milliseconds, across multiple channels, and adapt to emerging threats. This white paper delves into the critical need for advanced fraud risk management (FRM) in banks, highlighting how solutions like RS IntelliEdge™ - Banks can offer real-time, multi-channel fraud detection, leveraging AI, machine learning, and federated intelligence. Read on to discover how your bank can stay ahead of the fraud curve, and learn how to safeguard your payment systems in an ever-evolving landscape.

Introduction

The rapid evolution of financial technology has revolutionized payment systems worldwide, ushering in an era of real-time transactions. While these advancements offer unparalleled convenience and efficiency, they also present significant challenges in the form of sophisticated fraud schemes. Banks are now at the forefront of a battleground where fraudsters exploit system vulnerabilities at unprecedented speeds.

Real-time payments (RTP) are being rolled out in over 80 countries, including the US, UK, and India, with volumes projected to continue growing exponentially. Fraud schemes such as account takeovers (ATO), authorized push payment (APP) fraud, and synthetic identity fraud are on the rise riding on RTP. In 2022 alone, the US accounted for 42% of global eCommerce fraud, while Europe and Latin America faced growing issues with identity theft and social engineering attacks.

The challenge is exacerbated due to absence of a deferred settlement phase in RTP systems. This reduces the window of opportunity to detect and stop fraudulent activities before the funds are transferred, making real-time fraud detection critical. Financial institutions globally report an increasing trend of fraud losses, with fraudsters exploiting vulnerabilities in these fast-paced environments. Moreover, with financial regulations becoming more stringent, banks need fraud risk management (FRM) systems that are both compliant and flexible.

Rising Incidents of Fraud

• Authorized Push Payment (APP) Fraud: APP fraud is surging globally. In the United States, losses reached $1.94 billion in 2022, with projections indicating a rise to $3.03 billion by 2027, marking a 56% increase. In the UK, APP fraud accounted for 40% of total fraud losses in 2022, equating to £348 lost per £1 million of transactions.
• Account Takeover (ATO) Fraud: Fraudsters are increasingly employing sophisticated techniques to gain unauthorized access to customer accounts, leading to substantial financial losses and erosion of customer trust. Industry reported a sharp rise which was largely driven by widespread data breaches, phishing, and the use of automated tools like credential stuffing bots. ATO attacks surged by 354% between 2022 and 2023, with industries such as e-commerce, financial services, and social media being the most targeted.
• Malware and AI-Powered Scams: The prevalence of malware attacks grew by a staggering 4,000% in 2023. The accessibility of AI tools has enabled fraudsters to conduct more convincing scams, making detection increasingly difficult.

Impact on Financial Institutions

• Escalating Fraud Costs: Global fraud losses in payments grew from $3.07 billion in 2021 to $4.61 billion in 2022, a significant increase that directly impacts the bottom line of banks.
• Increased Mule Activity: According to recent surveys, 57% of financial institutions reported a rise in mule account activity, which facilitates the laundering of fraudulently obtained funds.
• Regulatory Pressures: Governments and regulatory bodies are tightening compliance requirements, holding banks accountable for preventing fraud and reimbursing customers in cases of failure. For example, UK payment service providers are mandated to reimburse customers for APP fraud occurring over the Faster Payment system starting in 2024.

The Need for Advanced Fraud Risk Management Systems

FRM systems must achieve high accuracy with low false positives. AI/ML models for detecting fraudulent transactions are probabilistic, with accuracy typically around 70%-80% and false positives around 20%-30%. While effective for unknown fraud patterns, increasing model complexity (more parameters and epoch numbers) leads to overfitting and increased false positives.

Limitations of Traditional Fraud Detection

Traditional fraud detection systems often rely on static rule-based models and manual interventions, which are insufficient in the face of rapidly evolving fraud tactics. These systems typically:

• Lack Real-Time Capabilities: They are unable to process and analyze transactions instantaneously, leading to delays in fraud detection.
• Operate in Silos: Customer profile information is often fragmented across different systems and branches, hindering a holistic view of customer behavior.
• Are Reactive Rather Than Proactive: They identify fraud after it has occurred, rather than predicting and preventing it in real-time.

FRM can be classified into the following generations:

• Generation 1: Rule Based: These were predominantly simple rule based FRM. Typically, they were executed post-facto and tried to look at some simple pattern that did not rely on any historical information.
• Generation 2: Rule + Statistics: These were also done post facto but used historical data in form of statistics pivoted for a payment instrument like card, account, customer-id etc.
• Generation 3: Rule + Statistics + AI Model: The 3rd generation saw the introduction of AI, and FRM was applied in-flight i.e. in real-time. The parameters of the AI were very limited though.

Essential Features of 4th Generation FRM Systems

With the rise in real-time payment, availability of user behaviour statistics captured via mobile device, and maturity of AI/ML technology, it paved the path for 4th generation FRM system that brought in Rules, AI/ML, real-time FRM execution and brought AML closer to FRM creating a new approach to fraud management termed as FRAML.

Modern FRM systems must incorporate:

1. Real-Time Monitoring and Decision Making: The ability to assess transaction risk within milliseconds to prevent fraudulent transactions before they are completed.
2. Collaborative Intelligence: Facilitating knowledge sharing across branches and institutions to enhance fraud detection capabilities collectively.
3. Adaptive AI and Machine Learning Models: Utilizing advanced analytics to identify emerging fraud patterns that static rules may miss.
4. Cross-Channel Integration: Monitoring customer activity across all channels—online banking, mobile apps, ATMs, and point-of-sale systems—to detect cross-channel fraud schemes.
5. Federated Architecture: Allowing decentralized rule creation and management while maintaining centralized oversight to adapt to local fraud trends without compromising global security standards.
6. Faster AML: With increase in real-time payment, it is essential to bring the AML process near to FRM process so that money laundering can be detected much faster.

Implementing a Federated Approach to Fraud Risk Management

A federated FRM system empowers individual branches or departments within a bank to develop and implement their own fraud detection rules based on localized patterns and threats. The rule parameters for a customer of a rural branch could be different from the customer of a branch in metro city. This approach offers several advantages:

• Customization: Branches can tailor fraud prevention strategies to their specific customer base and regional fraud trends.
• Enhanced Detection: Localized intelligence contributes to a more comprehensive detection system, capturing fraud patterns that may not be evident at a central level.
• Collaboration: Sharing rules and insights across the network strengthens the overall defence mechanism of the bank.

Federated FRM in Action

Consider a scenario where a particular region experiences a spike in a specific type of fraud, such as SIM swap attacks. The local branch can quickly develop and implement rules to detect and prevent this fraud. These rules can then be shared across other branches, which can adapt them as needed, ensuring a rapid and coordinated response to emerging threats.

Advancements in Technology Driving FRM

Artificial Intelligence and Machine Learning

When a customer uses bank assets like bank portal, bank mobile app, etc., bank can observe the activities, behaviour pattern which can build a precise profile. AI and ML algorithms can analyse vast amounts of transactional and profile data to identify patterns and anomalies indicative of fraud. These technologies enable:

• Predictive Analytics: Anticipating fraudulent activities based on historical data and trends.
• Behavioural Biometrics: Assessing user behaviour patterns, such as typing speed and mouse movements, to detect anomalies.
• Continuous Learning: Models that evolve as new data is ingested, improving accuracy over time.

Multi-channel FRM

Modern FRM systems need to support FRM for multiple channels. Hence, it must seamlessly integrate with various payment channels, including Mobile Payments, Online Banking, ATMs, Point-of-Sale Systems, and E-commerce Platforms.

This integration ensures a unified view of customer activity and enables cross-channel fraud detection.

Why FRM Systems are Critical for Banks

In many jurisdictions, central infrastructure FRM systems cannot use customer profile information directly from banks, meaning profiles must be built based on transactional intelligence, which would invariably be in silos. Real-time payments and the nature of fraud demand an approach that moves beyond traditional models of static rules and manual interventions. Predictive analytics, AI/ML models, and cross-channel data integration are needed to prevent, detect, and mitigate fraud risks in real time. However, AI/ML has challenge of explainability, which Rules can mitigate. Having said that formulating Rules for complex scenarios takes time while AI/ML models may be trained faster contingent to reported frauds.

Key reasons why 4th Generation FRM systems are critical include:

1. High-Speed Fraud Detection
Real-time transactions occur in milliseconds, and so must fraud detection. Banks need real-time monitoring and decision-making systems that can instantly score transactions for potential fraud.

2. Cross-Channel Intelligence
Fraud often spills over from one channel (e.g., mobile apps) to another (e.g., web banking). Without a system that integrates multiple payment channels, banks risk missing fraud signals.

3. Decentralized Fraud Patterns
In a federated banking system, fraud patterns vary across different branches or geographies. FRM systems should allow branches to tailor fraud detection rules while maintaining centralized oversight.

4. Bringing AML closure with Fraud Management
Instead of waiting for a month for banks to share SAR (Suspicious Activity Report) information with FIU (Financial Intelligence Unit) and then arriving at the suspect money launderers once a month, real-time payment necessitates having AML process moving closer to FRM process to detect mule accounts and money laundering at a quick clip.

RS IntelliEdge – Banks™

An Advanced Solution

RS IntelliEdge - Banks™ is an innovative multi-channel fraud risk management platform designed specifically for the complex landscape of real-time payments. Unlike traditional solutions, RS IntelliEdge - Banks™ leverages a federated architecture that enables collective intelligence sharing across branches. This collaborative model empowers each branch to create and contribute its own rules, enhancing the collective ability to detect and prevent fraud.

Key Features of RS IntelliEdge – Banks™

1. Federated Rule Creation and Collaborative Intelligence: Each branch can customize fraud detection rules based on local fraud patterns. These localized rules can be shared across branches, enabling collective fraud intelligence that strengthens the entire banking network’s defences.
2. Adaptive AI/ML Models: RS IntelliEdge – Banks™ uses adaptive AI/ML models trained on transactional data to detect emerging fraud patterns. This reduces reliance on static rules, offering dynamic fraud detection based on evolving threats.
3. Real-Time Scoring and Monitoring: The system can assess transaction risk in under 100 milliseconds, ensuring real-time fraud detection across multiple payment channels, including mobile payments, ATMs, and e-commerce transactions.
4. Cross-Channel Fraud Detection: RS IntelliEdge - Banks™ protects against cross-channel fraud by utilizing customer IDs across all channels to detect anomalous behavior that may indicate fraudulent activity. This ensures that no channel becomes a weak link in the security chain.
5. Scalability and Flexibility: The platform supports high transaction volumes without sacrificing performance, making it suitable for large banks handling significant payment traffic. Its flexible rule system also allows banks to adapt quickly to new fraud tactics without major reconfigurations. Rapid creation and deployment of fraud detection rules enables banks to respond quickly to emerging fraud tactics.
6. Enhanced Data Privacy: RS IntelliEdge - Banks™ ensures data privacy by processing fraud detection data locally within a bank's infrastructure, which is critical for regulatory compliance in many regions.
7. AML Assist: RS IntelliEdge - Banks™ transforms the payment information into a graphical flow of money from source account to destination account and uses graph analytics to detect outlier flow patterns. This is used to feed the AML process. If the AML process has specific flow queries, AML Assist can provide deeper insight on these flows as well.

The Future of Fraud Risk Management

In an era where real-time payments dominate, and fraudsters are becoming more sophisticated, RS IntelliEdge – Banks™ offers a powerful, scalable, and adaptive FRAML solution that integrates collaborative intelligence, real-time scoring, and cross-channel fraud detection.

By empowering branches to contribute to fraud prevention rules and fostering knowledge sharing, RS IntelliEdge™ - Banks™ creates a stronger, collective defence against fraud threats. The platform’s federated architecture, combined with AI/ML-driven analytics, ensures that banks are well-equipped to detect and mitigate fraud in an ever-evolving digital payments landscape.

With proven success in large-scale centralized implementation, RS IntelliEdge™ - Banks™ is the ideal solution for financial institutions looking to protect themselves from the growing threats posed by real-time fraud.

For more information, please visit RS Software.