Consumers want money to move instantly. Businesses want onboarding with zero friction. And fintech delivers: tap-to-pay, instant loans, crypto swaps, and transfers that clear before you can blink. It feels straightforward, and that’s exactly why adoption has exploded.
But effortless for customers also means effortless for criminals. Last year alone, fraud losses in the U.S. jumped past $12.5 billion, with scammers shifting tactics to exploit instant payments and crypto rails.
In this article, we’ll explore fintech fraud prevention and show how companies can stay fast and user-friendly while shutting the door on scammers.
The evidence is unambiguous: fintech fraud is accelerating at an unprecedented rate. Global loss figures continue to climb year over year, with regulators, banks, and technology providers all acknowledging the scale of the threat.
What was once treated as a series of isolated cases has become a structural risk, undermining consumer trust and putting pressure on institutions to strengthen their defenses.
Here is a closer look at why fintech fraud is evolving:
U.S. financial institutions saw fraud losses jump by roughly 65%, reaching an average of $3.8 million per firm in 2024. Identity fraud alone now represents 42% of all suspicious activity encountered by financial institutions.
AI-powered scams, through deepfake voices, synthetic IDs, and real-time phishing, are exploding. In 2025 alone, AI impersonation scams surged by 148%, with high-profile incidents including a $25 million fraud in Hong Kong via a deepfake video call.
Experts estimate that generative AI could quadruple the cost of fraud by 2027, increasing annual losses at a rate exceeding 30%.
Pig-butchering scams and romance-based crypto fraud saw a nearly 40% increase in 2024, accounting for a substantial share of crypto-related losses (over $12.4 billion in total for that year).
At the same time, Juniper Research projects that global e-commerce fraud losses will climb from $44.3 billion in 2024 to $107 billion by 2029, a staggering 141% increase.
This spike in fraud and the sophistication behind it underscores a critical shift: fintech fraud prevention cannot be reactive or simple rule-based anymore. It must be embedded, intelligent, and evolving.
As a financial institution, you can stay ahead of fraud in 2025 by applying these 10 fintech fraud prevention tactics.
Digital payments move money in seconds, and once it’s gone, recovery is nearly impossible. Batch reviews are no longer enough; fraud detection has to happen in real time.
Every transaction or account event gets a risk score as it happens. Fraud engines use data like user profile, device, geolocation, behavior history, amount, and timing. Rules and machine learning models flag anomalies in milliseconds, allowing platforms to hold or decline suspicious payments before they clear. Regulators stress that the “speed, finality, and 24/7 operation of instant payments” demand equally fast fraud prevention.
By late 2024, 71% of U.S. banks and fintechs were using AI-powered real-time fraud detection, up from 66% in 2023. Still, nearly 70% of 2025 fraud losses come from activity not caught in real time.
VALID Systems’ CheckDetect scores every check deposit in real time, flagging more than 75% of fraudulent checks at the point of presentment. Combining AI scoring with instant decisioning enables fintechs to stop fraud as it happens, without punishing customers.
Synthetic identity fraud, where criminals blend real and fake personal data to create entirely new “people,” has become one of the fastest-growing threats in financial services.
Fraudsters may use a real Social Security number (often stolen from children or deceased individuals) with fake names, dates of birth, or addresses to open accounts, build credit, and eventually cash out.
Massive data breaches have leaked millions of genuine SSNs and personal details onto the dark web, giving fraudsters the raw material to assemble synthetics. The Social Security Administration’s SSN randomization has also made it harder for lenders to spot fake numbers at a glance.
Behavioral analytics spots what static rules miss by learning how real customers normally behave, then flagging anything out of character. It helps catch account takeovers, bots, and coached victims in real time.
Behavioral biometrics analyzes keystrokes, typing speed, mouse or touch patterns, device orientation, and navigation flow to build a user signature. Behavior-based profiling models typically analyze activity such as payee mix, timing, device, and amounts, and surface anomalies that do not fit the customer or peer cohort.
Adoption has accelerated across financial institutions as ATO fraud pressure grows on faster payment rails. Many teams now run continuous behavioral checks at login and during high-risk actions, stepping up authentication only when behavior shifts.
Furthermore, an Association for Financial Professionals report noted that 79% of organizations experienced attempted or actual payment fraud in 2024.
Fintech fraud often spans multiple channels. Sophisticated criminals will probe for weaknesses by toggling between web portals, mobile apps, call centers, email, and even physical points of contact.
Cross-channel correlation is the tactic of linking and analyzing activity across all these channels to detect when disparate signals add up to a fraudulent modus operandi.
Historically, banks and fintechs often had separate fraud monitoring for each channel: one system watched online banking, another watched ATM transactions, another for wire transfers, etc. This siloed approach no longer works because fraud is no longer a one-bank (or one-channel) problem. Criminals operate across institutions, exploiting the lack of cross-visibility.
Technology alone cannot stop all fraud, especially when social engineering scams prey on human trust.
A crucial defensive tactic in 2025 is scam education - actively educating and alerting customers about common scams, and intervening at points of risk to prevent mistakes.
In 2024, imposter scams were the most-reported category with nearly $3 billion in losses, while investment scams cost $5.7 billion. Scammers mostly reach victims by email, phone, and text, then steer them back into the app to push transactions or capture codes.
No fintech or bank fights fraud in isolation. One of the most effective tactics to emerge in recent years is leveraging consortium intelligence, which involves pooling fraud data and insights across multiple institutions to detect and prevent attacks collectively.
In the U.S., banks have long cooperated via networks like Early Warning Services (which, among other things, maintains a database of account closures for fraud that can be checked during account opening). Now fintechs are joining or forming data-sharing alliances too.
Regulators and financial institutions once treated financial fraud and money laundering as separate domains: fraud was about preventing theft and scams. In contrast, AML (anti-money laundering) focuses on stopping the flow of illicit funds. Today, that line is blurring, giving rise to FRAML - the fusion of Fraud and AML programs.
Integrate because scammers defraud victims and launder the money through exchanges and wallets. Fraud teams that review the victim’s transfers while AML tracks crypto outflows separately miss the full chain. FinCEN identifies identity-related fraud and cybercrime as major illicit-finance threats that appear frequently in SARs. Unifying fraud and AML connects the dots and improves SAR quality.
Fraud patterns evolve rapidly (criminals even use AI themselves, from deepfakes to AI-written phishing texts), so the adaptive AI and machine learning models defending fintech platforms must be able to learn and adapt in near-real time as well.
By 2024, over 70% of financial institutions reported using AI/ML for fraud detection. This includes fintechs deploying models for everything from anomaly detection in transactions to image recognition on ID documents.
Embrace emerging AI, such as deep learning, for image, voice, and text analysis to detect deepfake IDs, synthetic voice fraud calls, and phishing messages. Some fintechs are now using NLP (natural language processing) AI to scan customer support chat logs in real time for signs of social engineering (phrases that indicate a scammer is coaching a user).
Strong customer authentication remains important to fraud prevention. Most schemes target account or payment authorization. Strengthen logins and approvals with multiple factors and contextual checks to block account takeover and unauthorized access.
Requiring customers to present two or more independent factors to access accounts dramatically improves security. These factors are typically something the user knows (password/PIN), something they have (mobile device, hardware token), or something they are (biometrics like fingerprint or facial recognition).
Finally, a holistic fraud prevention strategy in 2025 requires aligning with industry standards and regulatory frameworks. This is not a single technical control, but rather a best practice that underpins all the tactics discussed.
Fraud prevention is as much about process and communication as technology. Common classification frameworks like the Fed’s FraudClassifier SM (launched 2020) and ScamClassifier SM (2024) provide a standardized way to categorize fraud incidents across the industry. The FraudClassifier model, for instance, distinguishes fraud by method and whether the customer authorized the transaction, which helps pinpoint if controls failed at authentication vs. social engineering.
We can conclude that fintech fraud prevention in 2025 is about being fast, smart, and in sync with the ecosystem.
VALID helps teams shift from after-the-fact reviews to real-time decisions that stop losses before funds move.
Where VALID Systems fits:
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