Digital payments have already redefined the financial industry. Instant transfers, mobile wallets, and seamless transactions are no longer innovations. They're now the standard for how fintechs run and stay competitive.
However, with that growth comes an increasing risk. AI-driven fraud, synthetic identities, and social engineering now target every weak point in the system.
Traditional defenses can't keep up, and fintechs must treat digital payment fraud prevention as foundational infrastructure.
In this article, we will explore how digital fraud is evolving, where conventional defenses fall short, and what fintechs must do to build proactive systems that support secure and sustainable growth.
Key Takeaways
- Fraud is evolving faster than traditional defenses can keep pace: Fintechs face increasing threats from AI-powered scams, social engineering, and synthetic identity fraud. Old tools like rule-based systems and static KYC checks are no match for today's agile fraudsters.
- Real-time prevention is now a must: Instant payment rails like FedNow and RTP demand fraud detection that works in milliseconds. Delayed reviews or batch monitoring simply can't stop money from vanishing in seconds.
- AI and machine learning offer enhanced detection capabilities: Machine learning models can identify subtle patterns, detect emerging types of fraud, and reduce false positives when combined with human expertise.
- A layered strategy works best: No single tool is enough. The strongest fraud defenses combine real-time scoring, biometric verification, behavioral analytics, and consortium data sharing to cover every entry point and tactic fraudsters use.
What is digital payment fraud?
Digital payment fraud encompasses any unauthorized or deceitful transaction conducted through electronic payment channels.
It ranges from stolen card data used on e-commerce sites to fraudsters hijacking accounts or tricking customers into sending money.
In today's connected economy, digital payments have become dominant, and fraud has surged in tandem.
The global digital payment fraud market reached $50 billion by the end of 2024, with approximately 1 in 120 online transactions flagged as suspicious. Additionally, U.S. customers reported losing over $12.5 billion to fraud in 2024, representing a 25% increase from the previous year.
It's clear from these figures that digital payment fraud has grown into a systemic issue, putting pressure on users, companies, and the financial sector as a whole.
Emerging fraud threats in digital payments
Today's fraud threats are diverse and ever-evolving. Some of the common fraud types afflicting digital payments include:
Account takeover (ATO)
In an account takeover (ATO) attack, attackers gain unauthorized access to legitimate user accounts, typically by using stolen credentials or malware, and then initiate transactions or purchases without permission.
Industry data show that account takeover fraud has increased by approximately 28% recently, particularly affecting online banking and retail accounts.
Social engineering and authorized push payment scams
Rather than hacking systems, fraudsters increasingly exploit human trust. Phishing emails, SMS ("smishing") texts, and phone impostors trick victims into authorizing payments or divulging one-time passcodes.
In 2024, these scams surged to become the leading form of fraud, with scam-related fraud incidents increasing 56% and losses rising 121% compared to prior years.
For example, a criminal might pose as a bank representative and convince a user to send money to an account labeled as "safe" (which is actually the fraudster's account).
Fintech platforms facilitating instant peer-to-peer (P2P) transfers, such as Zelle and Venmo, have been prime targets for authorized push payment (APP) fraud, where funds move quickly and irreversibly.
Synthetic identity fraud
This fast-growing threat involves criminals creating fake identities by blending real and fictitious information (for instance, a real Social Security Number with a false name and birthdate).
They use these synthetic IDs to open accounts or obtain loans and credit, then default or cash out.
Losses from synthetic identity fraud are staggering, over $10 billion to date, and this fraud type has grown 80% since 2022, making it one of the most rapidly expanding forms of financial crime.
Card and CNP (Card-Not-Present) fraud
Payment card fraud remains a fundamental threat in digital commerce. Stolen card numbers and account details are sold on the dark web and then used to make unauthorized online purchases or payments. This type of fraud still accounts for approximately 38% of all digital payment fraud globally, the largest single share by fraud type.
Despite the rollout of EMV chip cards and 3-D Secure authentication, criminals find workarounds through phishing for card details, installing malware on merchant sites (to skim card info), or using brute-force algorithms to guess card numbers and CVVs.
Payment app and deposit fraud
As fintechs offer new ways to move and store money, fraudsters have adapted traditional fraud schemes to these platforms.
Peer-to-peer payment fraud is on the rise (up ~19% recently). For instance, scammers might infiltrate online marketplaces and trick buyers into paying via a P2P app for goods that never arrive.
Remote deposit fraud and check fraud have also seen a resurgence. Fintechs that enable mobile check deposit or ATM deposit need to screen for altered or fake checks and monitor for repeated attempts by the same users.
Likewise, refund fraud and chargeback abuse (where a person makes a purchase, then falsely claims it was unauthorized or never delivered) cost businesses billions. Refund-related fraud rose about 25% in recent reports.
These threats show that whether it's a digital wallet, a neobank account, or an online lending platform, fraud can take many forms.
Why traditional prevention falls short in digital payment fraud prevention
If fraud is skyrocketing despite widespread awareness, it begs the question: Why aren't legacy defenses enough?
Several specific factors explain why older approaches are falling short:
Evolving tactics vs. static rules
Today, fraud rings operate like tech startups, agile and innovation-focused. They use tools such as automated scripts, deepfake technology, and "fraud-as-a-service" kits to refine their methods. A rule that worked last year can be obsolete next year.
For instance, once institutions fortified against card cloning, criminals shifted to scams targeting customers directly.
Traditional systems can't easily detect scams where a legitimate user is tricked into sending money. The result: In 2024, 40% of financial institutions reported increased fraud loss dollars, up from 29% the previous year, as criminals outmaneuver static defenses.
High volume and false positives
Fraud attempts bombard fintechs and banks. No manual review team can realistically sift through this volume one by one without help.
Traditional rule-based systems generate an overwhelming number of alerts, many of which are false positives that drain analyst resources and frustrate customers.
Customers become frustrated when fraud systems wrongly block their valid transactions. Traditional prevention often fails to strike the right balance between blocking fraud and allowing normal activity.
Lack of adaptability and speed
With modern payment rails, such as peer-to-peer instant transfers, wires, or the FedNow instant payment service, money moves in seconds, and transactions are irreversible.
If a fintech relies on overnight batch fraud monitoring or manual case review, it will be too late, and the funds will be gone.
Traditional after-the-fact approaches (e.g., chasing fraud after it has occurred) are no longer sufficient when prevention must occur at the moment of the transaction.
Resource and technological gaps
Some institutions remain stuck with legacy tech due to the cost or complexity of upgrading. A recent industry report found that 83% of financial institutions cited cost as a barrier to improving their fraud prevention systems.
Fraudsters now use advanced tools, such as AI-generated voices and synthetic identities, to circumvent outdated verification methods.
Yet many firms still rely on knowledge-based authentication, like security questions, even though these are easily guessed or stolen. Use of such questions rose from 37% to 50% among financial institutions recently, despite their known weaknesses.
Most effective approaches to digital payment fraud prevention
As a fintech, you should build your fraud prevention strategy around these core pillars:
1. Shift to real-time transaction monitoring
With instantaneous transactions now commonplace, fraud defense must operate in real time. The faster your detection, the smaller the window for criminals to do damage.
Real-time risk scoring is especially critical for instant payment channels (such as RTP or FedNow), where there's no lag to fall back on.
Action steps:
- Embed real-time fraud scoring into payment workflows.
- Use event-driven systems to analyze transactions as they happen.
- Create automated escalation paths to pause or hold high-risk activity.
VALID solution:
VALID Systems Real-Time Loss Alerts (RTLA) model uses an extensive network of payer and account data to flag high-risk check deposits instantly. It has been able to alert on over 75% of potentially fraudulent check losses with pinpoint accuracy, allowing banks to intervene before those deposits turn into charge-offs.
2. Use AI and machine learning for cleverer detection
Artificial intelligence (AI) and machine learning (ML) are game-changers in fraud prevention. These technologies can sift through massive datasets to detect subtle anomalies, patterns of behavior, or device fingerprints that often elude human-designed rules.
To get started:
- Train models on confirmed fraud, edge cases, and safe behaviors.
- Run models in parallel with existing rules to benchmark effectiveness.
- Retrain regularly to stay ahead of emerging threats and reduce false positives.
Pro tip:
Don't treat AI as a black box add-on, instead integrate it with expert domain knowledge. The best results come from a hybrid approach: let AI cast a wide net for anomalies, then refine its outputs with human-driven rules for known fraud patterns and regulatory compliance.
3. Build a multi-layered defense strategy
No single tool or data source is sufficient to catch every fraud threat. The strongest defenses layer multiple controls and collaborate across the industry.
Best practices:
- Combine MFA, biometrics, device fingerprinting, and behavioral analytics.
- Score transactions based on risk factors, including location, timing, device, and amount.
- Employ identity verification and post-transaction monitoring as additional safety measures.
4. Strengthen onboarding to block fraud early
Fraud prevention in fintech should begin at customer onboarding this is your first and best chance to keep bad actors out of your ecosystem. Implement robust Know Your Customer (KYC) and identity verification workflows that leverage modern tools.
Best practices:
- Use government ID verification with liveness detection.
- Validate phone and email ownership.
- Apply behavioral analytics to catch bots or inconsistent data.
- Check against fraud consortiums and sanction lists.
5. Leverage AI and consortium data for broader fraud intelligence
Newer fintechs may lack deep historical data, but they don't have to fight fraud alone. Joining fraud consortiums and using third-party data expands visibility across the ecosystem.
Action steps:
- Join fraud networks to access shared signals like device fingerprints, synthetic IDs, and mule accounts
- Feed third-party signals (email, phone, IP reputation) into your ML models
- Use unsupervised machine learning to spot hidden patterns and coordinated fraud
- Apply graph analysis to uncover fraud rings connected by shared attributes
Example:
If one neobank flags a fake ID, a connected fintech can block that fraudster at onboarding using consortium-shared data.
6. Implement "Know your transaction" controls
In addition to knowing their customers, fintechs must also thoroughly understand their transactions. This strategy means analyzing payments in context to spot anomalies or known fraud patterns.
Action steps:
- Use ML-based transaction scoring to flag unusual activity (e.g., high-velocity transfers or high-risk destinations)
- Configure rules specific to your product, like detecting mule patterns in digital wallets
- Add "friction hooks" in your UI, like payee confirmation prompts for large or first-time transfers
VALID Systems in action: Adaptive solutions for digital payment fraud prevention
Recent years have shown that fintechs must stop treating fraud prevention as a minor feature or afterthought.
At VALID Systems, digital fraud prevention is our focus, protecting financial institutions and fintechs across the U.S.
VALID Systems is an AI-powered account risk and transaction-decisioning company with over 20 years of experience in fraud prevention.
We've partnered with some of the nation's largest banks and cutting-edge fintechs alike, and our solutions process millions of transactions, using machine learning to catch fraud that others miss.
Here's how VALID helps fintechs stop fraud at scale, without sacrificing customer experience:
Real-time machine learning models
VALID's Real-Time Loss Alerts (RTLA) deliver sub-second fraud scoring directly within client transaction flows. RTLA instantly analyzes the payer's history, cross-bank behavior, and dozens of other signals as soon as the system detects a check deposit or payment initiation, then assigns a fraud risk score.
Impact:
- Flagged over $225M in fraudulent check deposits before losses occurred
- Accurately identified 75%+ of potential charge-offs
- Helps clients focus on truly risky transactions while reducing false positives
Instant funds with built-in fraud control
VALID's InstantFUNDS© and InteliFUNDS© models work in conjunction with RTLA to enable instant access to deposits without compromising safety. Each deposit is processed in milliseconds, determining whether funds can be released immediately.
Benefits:
- Offers immediate access to deposited funds (e.g., check or ACH) with an optional convenience fee
- IFA model assesses risk in real time and coordinates with RTLA for dual-layer protection
- Approves ~99% of deposits instantly and covers the small percentage of charge-offs
Data intelligence and scoring
The VALID Edge Consortium provides access to fraud insights drawn from over 420 million accounts and $6 trillion in transaction data annually, fueling predictive models that improve fraud detection, credit risk management, and customer health analysis from day one.
VALID's MPI Score and Financial Health Score employ this data to evaluate account performance, attrition risk, and overall customer financial health in real-time.
Impact:
- Powers account scoring and risk assessment from the moment of onboarding
- Improves fraud detection and retention strategies using cross-industry insights
- Enables real-time decision-making with customizable business intelligence dashboards
Don't wait for fraud to strike. Invest in your fintech's digital payment fraud prevention and stay one step ahead.
Is your fintech equipped to outsmart today's fraud threats? Don't leave it to chance.
Contact VALID Systems and start building a safer, smarter future with a proven digital payment fraud prevention tool.