Building trust in digital payments: Need for predictive fraud prevention

Trust is the backbone of real-time digital payments.  The rapid expansion of UPI  payments has brought convenience and efficiency, but it has also opened the door to increasingly sophisticated fraud. 

As Unified Payments Interface (UPI) transactions surge and e-commerce continues its upward trajectory, fraudsters are finding new ways to exploit vulnerabilities at an alarming rate. While transaction-level fraud is largely mitigated by security measures like two-factor authentication (2FA), merchant-level fraud poses a much greater threat, carrying significant financial and reputational risks for payment service providers.

A secure merchant ecosystem is crucial to safeguarding payments , yet detecting fraudulent merchants is becoming increasingly difficult. With just a few clicks, bad actors can set up convincing digital storefronts to sell counterfeit goods, launder money, or conduct other illicit activities under a facade of legitimacy. 

From fake businesses and misclassified merchants to transaction laundering and identity fraud, merchant-level fraud takes many forms, threatening both consumers and acquirers with severe financial and reputational damage.

Acquirer's dilemma: Balancing growth and risk
Acquirers navigate a dual role—supporting legitimate merchants while safeguarding against fraud, compliance violations, and financial crimes. The exposure to these risks depends on their operating environment and risk assessment maturity. However, the acquiring model presents a challenge: while revenue comes from transaction fees, losses from fraud, fines, or non-compliance are based on total transaction value.

To sustain a secure ecosystem, acquirers must implement stringent underwriting and fraud prevention measures that comply with regulations and payment schemes. To effectively combat merchant fraud, acquirers must integrate advanced risk assessment tools with comprehensive regulatory governance frameworks and a deep understanding of payment scheme rules to reduce exposure to fraud while maintaining a smooth operational flow.

Shortcomings of traditional fraud detection
Digital merchant transactions generate massive volumes of data daily. Given the scale, speed, and complexity of modern payments, traditional fraud detection methods—primarily reliant on static rule-based systems—often fall short. These systems have several critical limitations:

Limited adaptability: Static rules quickly become ineffective as fraud tactics evolve, leaving systems vulnerable to emerging threats.

High false positive rates: Fixed rules can trigger unnecessary alerts, burdening organizations with unnecessary investigations and disrupting legitimate transactions.

Inability to integrate diverse data sources: Traditional systems struggle to incorporate signals from multiple sources, weakening their detection capabilities. These systems lack the profiling methods to assess complex transaction behaviors, potentially missing subtle fraudulent activities.

Shift to AI-powered fraud detection
Traditional rule-based systems operate on fixed criteria, which can become ineffective as fraud tactics evolve. In contrast, advanced AI and ML tools can discern underlying patterns by dynamically adjusting rules to enhance fraud detection, reduce chargebacks, and increase the approval of legitimate transactions. 

For example, a rule-based system may set up 100 rules, but an AI-based system may learn from data to identify new fraud patterns and create new rules.

Pre-emptive monitoring
AI-ML models detect complex, nonlinear relationships, improving risk assessment accuracy. For instance, AI can dynamically adjust transaction thresholds based on observed volume trends, proactively preventing fraud. Similarly, while traditional systems flag dormant accounts, AI predicts dormancy likelihood, enabling pre-emptive intervention.

Optimized variable selection
Data-driven models integrating ML algorithms with Big Data analytics efficiently handle vast amounts of data, enhancing decision-making. AI can automate fraud case management by flagging suspicious transactions for review or blocking potentially fraudulent activities. This automation speeds up detection and response, reducing fraud mitigation time.

Richer data segmentation
Machine learning enables granular data segmentation to analyze numerous attributes. Utilizing unsupervised learning techniques, such as clustering, allows for the identification of subtle patterns and anomalies, thereby improving model accuracy and explanatory power.

Scalability and efficiency
As transaction volumes increase, traditional systems may struggle to keep up. AI and ML-based solutions offer unparalleled scalability, efficiently handling large datasets without compromising performance. This scalability ensures that fraud detection systems can accommodate growth in transaction volume while maintaining effectiveness.

Strengthening merchant portfolio monitoring
Regulatory guidelines require acquirers to review merchants at set intervals, but periodic checks create exploitable gaps. Continuous AI-driven monitoring closes these gaps, boosting portfolio health and revenue by assigning dynamic trust scores. These models analyze KYB documents, transaction data, watch lists, and social sentiment to assess merchants from onboarding through their lifecycle.

Fraudulent merchants can bypass Know Your Customer (KYC) measures by setting up storefront websites classified as “low-risk” to avoid acquirer scrutiny. Such deception allows illegal businesses, including unlawful gaming operations, to hide behind fake identities or bogus online storefronts to secure merchant accounts. Once approved, they retail illegal products and services. 

The explosion of micro-merchants and data overload makes it difficult to detect mismatches between Merchant Category Codes (MCC) and actual business activities. Integrating AI into KYC processes can streamline verification, enhance efficiency, accuracy, and security, and reduce costs, providing significant savings, ultimately benefiting both financial institutions and their customers.

Tackling identity swap and transaction laundering
Transaction laundering, where unauthorized businesses funnel transactions through legitimate merchant accounts without the acquirer’s knowledge, is a growing threat. These entities may initially appear legitimate, avoiding immediate complaints and chargebacks, but regulators require acquirers to conduct thorough due diligence to prevent such fraud. Failure to detect it can lead to hefty fines and reputational damage.

AI-driven risk intelligence platforms tackle transaction laundering by analyzing mystery shopping data merchant information from diverse sources and transaction records. By tracking key parameters like time, frequency, and value, AI can flag anomalies—such as high-value transactions at odd hours—helping acquirers identify and shut down illicit activity.

Transaction monitoring and scoring
Acquirers can proactively identify anomalies by monitoring multiple risk indicators, including transaction volume, velocity, time of day, and merchant category. Sudden spikes in transaction amounts or frequency, unusual transaction timings, and discrepancies between a merchant’s registered business type and transaction patterns can signal potential fraud. 

AI-driven behavioral models analyze these parameters to assign a trust score to each transaction, enabling acquirers to mitigate fraud risks while reducing false positives and ensuring a seamless payment experience for legitimate merchants.

Staying a step ahead of fraud
To stay ahead of evolving fraud threats, acquirers must go beyond traditional in-house tools and adopt advanced fraud prevention strategies. Partnering with a specialized PayTech firm provides the expertise needed to combat increasingly sophisticated schemes. As digital payments grow, fraudsters adapt, making reactive rule-based systems insufficient. 

A proactive, AI-driven approach—powered by predictive analytics and machine learning—is essential to detecting and preventing fraud before it occurs, safeguarding both merchants and the payments ecosystem.

-- Deepak Chand Thakur, Co-founder and CEO, NPST.