How to Detect and Prevent Payment Fraud: Complete Guide for 2025

Payment fraud has become a structural challenge for financial institutions, e-commerce platforms, and digital lenders worldwide. The global economy moves further toward instant and borderless payments, and the attack surface for fraudsters expands in parallel.

AFP’s Payments Fraud and Control Survey indicates that 79% of organizations were victims of payments fraud attacks or attempts in 2024. From card-not-present (CNP) scams to synthetic identity fraud, criminals exploit both human vulnerabilities and technological loopholes to siphon funds at scale.

For banks, BNPL providers, microfinance institutions, and fintechs, the stakes are clear: unchecked payment fraud erodes margins, increases regulatory exposure, and damages customer trust. Preventing fraud in payments today is not simply about securing a transaction – it is about safeguarding business continuity and maintaining the confidence that underpins digital finance itself.

This guide examines the key forms of payment fraud in 2025, the methods criminals use, and the most effective approaches to online payment fraud prevention.

What Is Payment Fraud?

Payment fraud is any unauthorized transaction in which a criminal gains financial benefit at the expense of a business or consumer. It typically involves stolen or fabricated credentials, manipulated devices, or social engineering tactics to bypass authentication controls.

While traditional fraud relied on stolen physical cards or counterfeit checks, today’s online payment fraud is digital-first. Fraudsters exploit device spoofing, synthetic identities, malware, or account takeovers to execute unauthorized payments – often at scale.

In short, payment fraud is the manipulation of payment systems for illicit gain, and its impact spans financial losses, operational costs, and reputational harm.

Why Payment Fraud Matters in 2025

The volume and velocity of payments fraud are accelerating. Several factors make 2025 a critical year for fraud prevention:

• Real-time payments adoption – Instant settlement schemes like Pix in Brazil, UPI in India, or FedNow in the United States reduce the window for reversing fraudulent transfers.
• Embedded finance expansion – More non-financial companies now facilitate payments, often with lighter compliance structures, creating new fraud vulnerabilities.
• AI-enabled scams – Fraud groups deploy generative AI to create convincing phishing emails, fake documents, or synthetic identities that bypass superficial checks.
• Cross-border growth – The global nature of e-commerce and remittances adds complexity in verifying identities and detecting unusual behaviors.

Types of Payment Fraud in 2025

Understanding the different categories of online payment fraud is essential for prevention.

1. Card-Not-Present (CNP) Fraud

Occurs when criminals use stolen card information to complete transactions without presenting the physical card. With e-commerce continuing to grow, Card-Not-Present fraud remains one of the most frequent attack types. Losses from CNP fraud in eCommerce are projected to reach $28.1B by 2026.

2. Account Takeover (ATO)

Fraudsters gain access to a legitimate user’s account through phishing, malware, or credential stuffing. Once inside, they initiate payments, transfer balances, or change settings to lock out the real user. See our article on account takeover fraud prevention for a deep dive.

3. Synthetic Identity Fraud

Criminals combine real and fake information to create new, seemingly valid customer profiles. These identities are then used to obtain credit, open accounts, or process payments before defaulting. Learn more about synthetic identity fraud here.

4. Chargeback Fraud (Friendly Fraud)

Customers dispute legitimate transactions with their bank, claiming they never authorized the payment. While some cases are genuine, many involve deliberate abuse of consumer protection rules. Access our guide on friendly fraud prevention here.

5. Merchant Fraud

Illegitimate merchants create fake stores or services, accept payments, and disappear without delivering. Payment processors and BNPL providers are particularly at risk of merchant fraud.

6. Device and Session Spoofing

Fraudsters manipulate device fingerprints, use emulators, or deploy virtual machines to disguise their identity. These signals can evade traditional fraud controls unless device intelligence is applied.

7. Money Mule Schemes

Fraudsters recruit intermediaries (money mules) – knowingly or unknowingly – to transfer stolen funds, complicating detection and recovery.

How Payment Fraud Works: Methods and Tactics

Most payment fraud schemes follow a structured process:

1. Data Harvesting – Personal or financial data is stolen via phishing, dark web purchases, or malware.
2. Identity Manipulation – Fraudsters create synthetic profiles or hijack real accounts.
3. Evasion Techniques – Device spoofing, VPN masking, and randomization help bypass detection systems.
4. Execution – Fraudulent payments are initiated, often in small test amounts before larger attempts.
5. Monetization – Funds are laundered through money mules, crypto wallets, or layered transfers.

The sophistication of each stage makes prevention increasingly complex. For instance, even if a stolen credential is identified, fraudsters may already have several fallback profiles ready to deploy.

Payment Fraud Prevention: What Works in 2025

Preventing online payment fraud requires a combination of technology, processes, and strategy. The most effective institutions adopt a holistic model that blends fraud prevention with customer experience.

Key Elements of Payment Fraud Protection

• Device Intelligence – Detects anomalies at the device and session level, identifying emulators, virtual machines, or randomized fingerprints.
• Behavioral Analytics – Evaluates how users interact with platforms (typing speed, navigation patterns) to spot automated or scripted fraud.
• Alternative Data Scoring – Goes beyond credit bureau records by using telco, utility, and device metadata to assess trustworthiness.
• Adaptive Authentication – Adjusts security levels dynamically, requiring step-up verification only when risk signals are present.
• Transaction Monitoring – Continuous, real-time analysis of payment flows to catch irregularities before they settle. This includes payment screening – pre-transaction checks that evaluate payment details against fraud signals, sanctions lists, and internal risk models. Screening adds a crucial layer of defense by stopping high-risk or suspicious payments before they are executed.

JuicyScore’s device intelligence solutions are designed precisely for this environment. By analyzing over 220 non-personal parameters and generating a unique device ID independent of manufacturers, JuicyScore enables institutions to identify fraud patterns that would otherwise go undetected.

Balancing Fraud Prevention and Inclusion

For digital lenders and microfinance organizations, fraud prevention must also support financial inclusion. Overly strict controls can exclude thin-file customers or increase false positives.

A modern payments fraud prevention strategy therefore emphasizes:

• Explainability – Models must be auditable and compliant with regulations such as GDPR in Europe or LGPD in Brazil.
• Privacy-Safe Signals – Using non-personal device and behavioral data avoids reliance on sensitive information.
• Segment Sensitivity – Adjusting fraud controls to account for underbanked or gig-economy customers who may lack traditional credit history.

Institutions that strike this balance unlock growth opportunities while keeping fraud within sustainable limits.

Industry Outlook: Payment Fraud in 2025 and Beyond

Looking ahead, payment fraud is expected to remain one of the fastest-evolving risks in finance. Several trends stand out:

• AI-driven fraud factories – Groups using automation to scale fraud campaigns across borders.
• Fraud-as-a-Service (FaaS) – Organized groups offering turnkey fraud kits, lowering the barrier to entry for criminals.
• Cross-channel attacks – Fraudsters blending social engineering, account takeover, and payment fraud into multi-stage campaigns.
• Regulatory intensification – Authorities mandating stronger authentication, transaction monitoring, and fraud reporting.

For institutions, the implications are clear: payment fraud protection cannot be treated as a one-time project. It must be embedded as an ongoing capability, continuously adapting to new fraud vectors and regulatory mandates. Leaders in this space are:

• Investing in adaptive fraud models that can recalibrate in real time.
• Integrating device intelligence and behavioral analytics to capture subtle signals beyond traditional KYC.
• Balancing speed and security by applying step-up authentication only when risk signals warrant it.
• Collaborating with regulators and peers to strengthen systemic resilience.

This regulatory momentum is visible worldwide, and recent examples in India illustrate this direction:

• Digital Payment Intelligence Platform (DPIP) – Initiated under the Reserve Bank of India (RBI), DPIP is designed as a national-level infrastructure to aggregate fraud intelligence across banks and payment providers. By using AI/ML, the platform will enable real-time data exchange on emerging fraud patterns, giving institutions faster visibility into cross-bank schemes and coordinated attacks. The goal is to move fraud detection from siloed monitoring to sector-wide intelligence, ensuring risks spotted in one institution can be quickly flagged across the ecosystem.

• Financial Fraud Risk Indicator (FRI) – In parallel, the RBI has directed banks to integrate the Department of Telecommunications’ FRI into their systems. FRI classifies mobile numbers into risk categories (medium, high, very high) based on signals such as past links to scams, complaints logged in India’s National Cybercrime Reporting Portal, or data from the DoT’s Chakshu platform. Banks can use these classifications in real time to block or delay suspicious transactions, issue customer alerts, or apply additional authentication layers.

Both DPIP and FRI represent a strong regulatory push toward collaborative fraud prevention. They provide valuable tools for flagging suspicious identifiers and sharing intelligence more quickly across the sector. At the same time, they highlight a structural gap: fraudsters increasingly hide behind anonymizers, emulators, and randomized devices. A mobile number may appear “clean” in FRI, but the session itself could still be malicious.

This is where JuicyScore adds an essential layer. By analyzing device integrity, virtualization traces, and behavioral signals, our solutions complement frameworks like DPIP and FRI. We help institutions move beyond static identifiers – detecting not just whether a number looks suspicious, but whether the device and behavior behind the transaction can be trusted. This combination of systemic intelligence and device-level analysis is what allows financial institutions to stay ahead of fraud that adapts faster than regulations.

Fraud Prevention as the Foundation for Digital Finance

Ready to strengthen your payment fraud prevention strategy? Book a demo with JuicyScore and see how device intelligence can help your business detect hidden risks, reduce fraud losses, and grow securely.

Key Takeaways

• Payment fraud is structural, not episodic – 79% of organizations reported attacks in 2024, making fraud prevention a core business function rather than an occasional project.
• The fraud landscape is diversifying – from card-not-present and account takeover to synthetic identities, device spoofing, and money mule schemes.
• Real-time payments raise both opportunity and risk – systems like Pix, UPI, and FedNow accelerate settlement but shorten the window for fraud detection and reversal.
• Technology is reshaping fraud tactics – generative AI enables realistic phishing, deepfakes, and synthetic identities, forcing institutions to rethink prevention methods.
• Device intelligence is essential in 2025 – it detects emulators, virtual machines, and randomized fingerprints that static KYC or credit bureau checks cannot uncover.
• Prevention must balance growth and inclusion – overly strict controls risk excluding thin-file or underbanked customers, while adaptive, privacy-safe models support financial access.
• India’s regulatory initiatives (DPIP and FRI) show how national-level collaboration can improve intelligence sharing, but device-level analysis is needed to detect fraud hidden behind “clean” identifiers.
• Fraud prevention is the foundation of digital finance – combining systemic intelligence, device-level insights, and adaptive models enables institutions to protect margins, customer trust, and long-term growth.

FAQs: Payment Fraud and Prevention

What is payment fraud in simple terms?

Payment fraud is when someone makes a payment without authorization, usually by stealing or faking identity details.

How does online payment fraud happen?

It usually starts with stolen credentials or fake profiles. Fraudsters then disguise their devices or use social engineering to bypass security checks.

What are the most common types of payment fraud?

The main ones include card-not-present fraud, account takeovers, synthetic identity fraud, chargeback fraud, and device spoofing.

How can I prevent online payment fraud?

Use layered protection – device intelligence, behavioral analysis, real-time monitoring, and adaptive authentication.

Why is payment fraud prevention so important now?

With real-time payments and digital banking growth, fraudulent transfers settle faster, leaving less time to reverse losses.

What role does device intelligence play in payments fraud prevention?

Device intelligence prevents fraud by detecting hidden risks like emulators, VPN masking, or session spoofing that traditional fraud checks miss.

Can fraud prevention exclude legitimate customers?

Yes, if controls are too rigid. That’s why explainable, data-driven, and privacy-safe models are key for balancing inclusion and protection.

What is the future of online payment fraud detection?

Expect more AI-driven fraud, cross-border scams, and stricter regulations – requiring continuous investment in fraud prevention systems.