Streamline fraud detection and enhance compliance with automated, scalable workflows designed for Payment Service Providers.
Streamline operations and enhance security by unifying data and automating processes, preventing losses and improving fraud detection.
PSPs juggle thousands of SMBs across multiple payment rails. Data sits in silos, so abnormal refund spikes or chargeback loops often remain invisible until losses snowball.
Onboarding volume outpaces analyst capacity. Spreadsheet-driven checks miss shell companies, letting bad actors monetize stolen cards before vetting is complete.
Many PSP teams rely on daily batch files. Without instant insight, fraud rings exploit authorization windows, driving higher interchange claw-backs.
Alerts, evidence, and notes live in disconnected tools. Analysts duplicate effort, mis-prioritize cases, and burn time preparing audits for regulators.
Streamline oversight and automate processes for enhanced security and efficiency across your payment operations.
We don’t just promise better fraud control—we deliver tangible improvements that protect your business.
Approve more valid transactions confidently.
Experience double-digit reductions in fraud-related chargebacks
Save time and resources while securing your revenue.
With an integrated platform designed for precision, agility, and impactful results, enabling your team to make smarter decisions, improve operational efficiency, and fuel your business growth.
No-code rules engine, flexible dashboards, and tailor-made machine learning models that are designed to adapt seamlessly and scale alongside your business.
Unify fraud detection, compliance, and risk management into one powerful solution, saving valuable time and streamlining your operations.
Reduce false positives, detect and prevent more fraud, and mitigate risk with highly accurate, real-time risk scoring and anomaly detection you can trust.
Leverage advanced analytics, comprehensive reporting, and our Global Anti-Fraud Network to make faster, smarter decisions on the spot.
The first step in a PSP fraud investigation workflow typically involves the detection of suspicious activity through automated systems or alerts. These systems analyze transaction patterns and flag anomalies based on predefined criteria. This initial detection is crucial for initiating further investigation and helps in prioritizing cases that require immediate attention to prevent potential financial losses and customer dissatisfaction.
Machine learning enhances fraud detection by analyzing vast amounts of transaction data to identify patterns and anomalies that may indicate fraudulent activity. It employs algorithms that learn from historical data to predict and flag potential fraud in real-time. This adaptive learning capability allows PSPs to continuously improve their detection accuracy, reduce false positives, and adapt to new fraud tactics, thereby enhancing the overall security of payment systems.
Human investigators are crucial in PSP fraud investigations for interpreting complex cases that automated systems cannot resolve. They conduct in-depth analysis of flagged transactions, verify the legitimacy of suspicious activities, and interact with customers for additional information. Their expertise is essential for understanding nuanced fraud patterns, making judgment calls in ambiguous situations, and ensuring that legitimate transactions are not wrongfully blocked, thereby balancing security with customer satisfaction.
Data privacy is vital in PSP fraud investigations to protect sensitive customer information and comply with regulations like GDPR. Investigators must ensure that data is handled securely, shared only on a need-to-know basis, and stored in compliance with legal requirements. Maintaining data privacy helps build customer trust, prevents unauthorized access to personal information, and avoids legal penalties. It also ensures that investigations are conducted ethically and transparently, respecting customer rights.
PSPs can reduce false positives by continuously refining their fraud detection algorithms and incorporating machine learning models that adapt to new fraud trends. They should also use a multi-layered approach, combining data from various sources to improve accuracy. Regularly updating rule-based systems and incorporating feedback from human investigators can further fine-tune detection criteria. Additionally, engaging in industry collaboration for shared insights and emerging threats can enhance the precision of fraud detection systems.
Cross-border PSP fraud investigations face challenges such as differing legal and regulatory environments, language barriers, and varying data protection laws. These factors can complicate information sharing and collaboration between international entities. Additionally, fraudsters exploit these complexities to conduct sophisticated schemes. To overcome these challenges, PSPs must establish robust international partnerships, ensure compliance with local laws, and leverage global fraud intelligence networks. This collaborative approach helps in effectively addressing cross-border fraud incidents.