Table of Contents
India is projected to lose ₹1.2 lakh crore to cyber fraud by this year, accounting for nearly 0.7% of its GDP. This alarming statistic highlights the rapidly evolving threat of digital fraud, particularly in the payment ecosystem. Fraudsters are no longer lone opportunists as they are leveraging cutting-edge tools to outpace traditional security measures.
As an organisation or individual, we must identify and assess digital fraud to ensure a trustworthy and reliable payment system. To do so, we must deeply understand how digital fraud operates. This blog offers an in-depth exploration of the core tactics, evolving fraudster archetypes, and the latest trends shaping the digital payment fraud landscape.
The Anatomy of Payment Fraud
Payment fraud is a growing threat that evolves alongside advancements in digital transactions. Fraudsters continuously refine their tactics to exploit vulnerabilities in payment systems, targeting individuals, businesses, and payment aggregators. Understanding the mechanics of payment fraud is crucial for developing effective prevention strategies.
Understanding digital payment fraud
In the payment system, whenever dealing with fraud, people always talk about a bad IP address, bad credit card, or blacklisted mobile number, referring to the provided entity being linked in the past to fraudulent activity. But, in most cases, the entities considered as so-called bad can be legitimate; the actual card user is still using their credit card legitimately, even if it’s also being used fraudulently. As we continue to identify fraud, our ability to think about fraud should align with thinking about the identity behind a transaction or an action taken online, and these strategies are set to the common traits that fraudsters possess.
Fraudster Traits
- Impersonation and Deception
Impersonation is pretending to be someone else by using stolen or fake identities to gain access. This often involves using stolen PII, such as phone numbers, emails, or credit card details, to pass verification as a real customer, while deception is about creating false data or behavior to mislead systems or victims. Fraudsters may send fake OTP requests, phishing emails, or falsify transaction details to manipulate security measures. - Obfuscation and Anonymity
Obfuscation involves hiding or altering real information to avoid detection while interacting with a system. Fraudsters achieve this by using VPNs, IP masking, and device spoofing to appear as if they are in a different location, whereas anonymity focuses on removing all identifiable traces to remain untraceable. Fraudsters use Tor browsers and residential proxies to operate without leaving a digital footprint. - Social Engineering
Fraudsters exploit phishing, vishing, and smishing to trick victims into revealing credentials. Tactics like man-in-the-middle (MITM) attacks and session hijacking allow them to intercept session tokens, OTPs, and authentication cookies before they reach secure endpoints. - Persistence and Adaptability
Fraudsters continuously refine their attack methods using automated scripts and brute-force techniques to test credential combinations. Credential stuffing attacks leverage breached databases, where bots attempt logins across multiple platforms, while session replay attacks mimic legitimate user behavior to bypass fraud detection. - Automation and Scalability
Large-scale fraud operations utilise botnets for card testing, fake account creation, and DDoS attacks. Fraudsters deploy headless browsers like Puppeteer to automate human-like interactions and evade detection. - Collaboration
Fraudsters operate in dark web marketplaces, Telegram channels, and underground forums to share stolen data and fraud techniques. Fraud-as-a-service (FaaS) platforms provide ready-made tools, while money mules help launder stolen funds through layered transactions.
Let us look at some of the recent payment fraud types that exploit the abovementioned traits.
Recent Types of Payment Fraud
- Stolen Card Fraud—Unauthorised use of compromised card data
Fraudsters use stolen card details to conduct unauthorised transactions without a physical card.
For example, a scammer purchases stolen credit card data from any dark web platform and places high-value orders on an e-commerce site with the same amount at different intervals to convince the payment system of a genuine customer. These can vary from refund fraud to coupon fraud. Since only card details are verified, the transaction succeeds. Later, the legitimate cardholder disputes the charge, causing financial losses to the merchant and bank. - Account Takeover (ATO) – Unauthorised access
Fraudsters gain unauthorised access to user accounts using stolen credentials obtained through phishing attacks, data breaches, the dark web, and credential stuffing.
For example, a hacker obtains login details from a breached e-commerce platform or dark web and tests them to access a victim’s payment wallet on another platform called credential stuffing (in general, for ease of access, people tend to use the same credentials across most platforms). The hacker then changes the registered email or mobile, locking out the original user before transferring funds or making the purchases. - Friendly Fraud—High number of chargebacks
Most seen in e-commerce businesses, customers falsely dispute legitimate transactions to receive refunds, often keeping both the purchased product and the refunded amount.
One good example is a buyer who orders an expensive pair of shoes online and later claims they never received it or replaced the same product that was purchased from another seller who sells exact duplicate products. The bank/payment aggregator issues a chargeback, refunding the money while the fraudster keeps the original product. - Fraud Rings—Unidentified patterns in transaction history
Fraudsters collaborate in organised groups, repeatedly using stolen cards and fake accounts to conduct fraudulent transactions, even when some linked data has already been flagged as fraudulent, making detection more challenging.
For example, a fraud ring may use a blacklisted email for one transaction while conducting another fraudulent transaction with the same device ID or bank account but a different email. This makes it challenging for traditional fraud detection systems to recognise the links between user identifiers—such as shared device fingerprints, IP addresses, or payment instruments—as individual transactions may not appear suspicious with the provided information. - Synthetic Identity Fraud—Fake identities
Fraudsters use real and fabricated personal information obtained from different sources to open accounts, make fraudulent transactions, or sell illegal goods.
For example, a scammer may open a merchant account using a genuine Aadhaar number but fake credentials and get onboarded to any payment aggregator. After building trust with small transactions for months, they attempt high-value transactions or raise frequent chargebacks. - Fake Links/Calls Fraud—Social engineering to steal credentials and OTPs
Fraudsters often impersonate bank representatives to trick victims into revealing sensitive information, using stolen data and social engineering tactics to gain trust.
A typical example of fraud is when a fraudster pretends to be a bank employee. They may have already obtained credit card details from an insider, a data breach, or the dark web. To gain the victim’s trust, they call and impersonate a legitimate bank representative. Once the victim is convinced, the fraudster sends a fake link/app, claiming it is for verification or security purposes. The victim is then tricked into entering sensitive information, including passwords and OTPs, through the fake link. With this access, the fraudster can withdraw money or make unauthorised transactions. - Website Fraud—Fake job portals, investment scams, and work-from-home schemes
Fraudsters create deceptive websites that mimic legitimate businesses, tricking users into registering and paying for the goods.
For example, a fraudulent job portal advertises high-paying work-from-home opportunities, charging applicants a “registration fee.” Once payments are collected, the scammers disappear, leaving victims without a job or refund. Similarly, fake investment platforms promise high returns on cryptocurrency investments over social media platforms before vanishing with investors’ money.
The list will continue to grow, as the examples above show that fraudsters are not uniform; some are low-level opportunists. In contrast, others are highly skilled professionals operating at scale. These fraudsters can be broadly categorized into fraudster archetypes.
Fraudster Archetypes
- The Opportunistic Fraudster
Individuals with limited technical skills rely primarily on social engineering rather than advanced methods. They are primarily engaged in friendly chargeback fraud or refund abuse. Sometimes, the actual customer can be an opportunist who takes advantage of loopholes in the system. - The Script Kiddie
These tech-savvy attackers often purchase data from dark web markets and use automation tools, scripts, and bots to exploit weaknesses in the payment system. They primarily use brute force attacks, credential stuffing, and card testing. - The OrganiSed Fraud Ring
These individuals are coordinated in groups that aim to plan and execute large-scale fraud across platforms. They use networked infrastructure with compromised accounts or money mules and often engage in identity theft, money laundering, and ATO attacks. - The Insider Threat
These can be the current or former employees with access to sensitive data, exploiting it for personal gain or selling it to external fraudsters. They are often involved in manipulating KYC data, transaction approvals, or account verification.
Conclusion
Identifying fraudster archetypes isn’t just about understanding the bad actors, rather, it’s about staying ahead of them. By recognising these patterns, businesses can build smarter fraud detection systems, reduce risks, and keep up with evolving threats.
And we’re just getting started! In Part 2 of this series, we’ll dive into the latest fraud prevention technologies—think graph databases, powerful rule engines, and dynamic machine learning models. Plus, we’ll show you how we, at Cashfree Payments are leading the charge in protecting merchant businesses.
