India's payment ecosystem exploded in 2025, with UPI transactions surpassing 15 billion monthly and digital wallets powering fintech, e-commerce, this surge unleashed AI-wielding fraudsters launching unpredictable attacks including deepfakes, synthetic identities, and adaptive phishing that outpace static defenses. Traditional rule-based systems falter under "rule sprawl," triggering false positives, alert fatigue, and blocked legitimate transactions. Enter artificial intelligence: by analyzing vast real-time data for subtle anomalies, AI delivers predictive, scalable fraud prevention; elevating security into a growth engine.
Srinivasasundar Bandepalli, Head of Cards & Payments at Expleo, champions the shift: “Artificial intelligence significantly enhances real-time fraud detection in payment gateways by moving beyond the limitations of traditional rule-based systems. As cybercriminals use AI to launch rapid, unpredictable attacks, uniform rules are struggling to keep pace, often resulting in false positives, alert fatigue, and poor customer experiences. AI and ML models analyse vast volumes of historical and real-time transaction data for subtle, multidimensional patterns and anomalies that rules sometimes fail to catch.”
“These models keep learning new tactics of fraud, enabling predictive detection rather than mere reactive responses. In milliseconds, AI can assess hundreds of risk signals, such as device behaviour, transaction velocity, and contextual data, to raise fraud catch rates and reduce unnecessary declines. At scale, AI systems handle millions of transactions effortlessly, enabling instant payments and approvals. Although rules still serve as guardrails for high-confidence checks, an "AI-first, rules-second" approach enables smarter, faster, more customer-friendly fraud management in modern payment gateways.”
Pankaj Tripathi, Co-founder and CEO of Vernost Tech Ventures, ties it to business impact: “Artificial intelligence has become central to real-time fraud detection in payment gateways by enabling systems to analyse transactions instantly and identify suspicious behaviour. Unlike rule-based models, AI uses machine learning to study spending patterns, device behaviour, location data, and transaction history in real time. This allows payment platforms to flag anomalies such as unusual purchase values, sudden location changes, or abnormal transaction frequency within milliseconds. AI models continuously learn from new data, helping gateways stay ahead of evolving fraud tactics without disrupting genuine customer transactions. Advanced techniques like behavioural biometrics and predictive analytics further strengthen accuracy by reducing false declines. For businesses, this means improved security, faster approvals, and better customer trust, while consumers benefit from seamless, protected digital payments in an increasingly cashless economy.”
This "AI-first" paradigm shines in transaction scrutiny. Sarika Shetty, Co-founder and CEO of RentenPe, explains “Artificial Intelligence improves real-time fraud detection in payment gateways by analysing each transaction as it occurs. AI evaluates the transaction amount, frequency, location, device details, and payment history to detect unusual or high-risk activity. It compares transactions against learned customer behaviour and global fraud patterns, assigning a risk score in milliseconds. Suspicious transactions are instantly blocked or verified, reducing fraud losses while allowing legitimate transactions to be processed smoothly and securely.”
Dr. Neeraj Sharma, Dean of Computer Science Engineering at Lovely Professional University, highlights AI's learning edge over rigid rules: “Artificial Intelligence (AI) has made fraud detection smarter and more effective by moving away from rigid, rule-based systems. Traditional fraud checks follow fixed rules, so when criminals try new or unexpected tricks, these systems often fail to detect them. As online payments and digital services grow, this limitation becomes more serious. AI-powered fraud detection works differently. Instead of relying on a few rules, it quickly looks at many details at once—such as where a transaction is happening, the device being used, and how fast actions are performed. All of this happens in a fraction of a second, allowing decisions to be made almost instantly.”
“AI systems also learn from experience. They study past fraud cases to recognize familiar patterns, while at the same time watching for unusual behavior that doesn't match normal user activity. Another important feature is behavioral biometrics, which observes how a person types, swipes, or moves a mouse. These small habits are unique to individuals and hard for fraudsters to copy. Each transaction receives a risk score. Safe payments go through smoothly, while suspicious ones are checked further. This reduces false alarms and keeps users secure without unnecessary inconvenience.”
In 2026, Indian enterprises adopting AI-driven gateways will slash losses, boost approval rates by 20-30%, and foster loyalty in high-volume sectors like rentals and retail. By prioritizing behavioral biometrics, predictive scoring, and continuous learning—with rules as mere guardrails—businesses turn fraud defense into a competitive moat, powering trust and scale in the cashless era.