In the evolving cybersecurity landscape, everyone agrees that protective measures should be proactive to combat sophisticated fraudulent schemes. However, financial platforms that offer crypto services often lack the necessary data to act in advance, allowing fraudsters to exploit gaps in security and compliance. Is it possible for crypto platforms to implement proactive fraud prevention notwithstanding the specifics of their operations? Let’s break down this idea into key points to find the answer.
Reactive vs Proactive Fraud Prevention Explained
We all know basic methods to counter fraudulent transactions in finance. Users may report a fraudulent seller or contact that sends out fraudulent links, set controls over their spending limits and patterns, while banks implement additional verification layers. Dedicated tools also detect suspicious activity while someone attempts to make a money transfer or payment. A new device is used for authorisation, a user is choosing a non-typical payment method or shops at an unusual location, perhaps, trying to transfer an uncommonly large sum. All these actions might be perfectly legal, but are still red flags for the financial institution, urging for extra vigilance.
Additional security measures are welcome as long as they’re not overzealous and hindering daily operations. The only problem with fraud prevention techniques is that most of them are reactive – identifying and responding to fraud after it has already occurred or is in progress. These methods detect suspicious activities post-event and take corrective actions like blocking users, reversing transactions, or updating rules to prevent similar incidents in the future. Today, it is not enough. As industry leaders themselves acknowledge: “If you’re detecting fraud when it happens, you’re already too late.”
Proactive fraud prevention, on the other hand, spots the fraudulent activity before it happens. It requires more complex and sophisticated systems, advanced tech tools, including AI and biometrics, real-time monitoring, and predictive analytics. This combination helps to assess risks and block suspicious activity in the very moment of the attempt — or even preemptively. Let’s see the difference.
In a reactive fraud protection model, a credit card company might flag and block a transaction as fraudulent only after a customer reports it as unauthorised. Proactive security measures would double-check with the customer if this transaction is legit, asking for additional confirmation, be it a password, one-time code, or fingerprint scan.
A reactive bank adjusts its risk model after identifying a new phishing scheme that has compromised several accounts. A proactive one puts its finger on the pulse of the cybersecurity space in real time, introducing prevention tactics even if novel phishing methods haven’t reached the community it serves just yet.
Fraud and Crypto Go Hand in Hand: Myth or Reality?
The Chainalysis 2025 Crypto Crime Report sheds some light on the state of fraud in the cryptocurrency industry. The analytics firm estimates that $40.9 billion in crypto was received by illicit addresses and stolen in crypto hacks. However, that amount is true only when we take into account illicit addresses known today. The research team believes the total may be closer to $51 billion, once they update the numbers during 2025 as more fraud sources are identified, given historical trends where annual estimates of illicit activity have grown by an average of 25% between annual reporting periods.
These numbers are big, yet they constitute only a tiny 0.14% fraction of the total on-chain transaction volume. Nevertheless, the report also shows a bigger picture which is a bit troublesome.
Alarming Crypto Fraud Trends
In 2024, stolen crypto jumped 21% year-over-year, hitting $2.2 billion. Most of it came from DeFi platforms, but centralised services were hit hardest in Q2 and Q3. The biggest issue? Private key compromises, which made up 43.8% of all stolen funds. North Korean hackers were especially active — they took $1.34 billion, or 61% of the total stolen. Some attacks were tied to North Korean IT workers infiltrating crypto and web3 companies, using advanced tactics to break in, which can’t be ignored.
Fraud and scams were everywhere in 2024. It is true both for traditional finance and innovative decentralised services. High-yield investment schemes and “pig butchering” scams were the most lucrative. We also saw scammers using AI capabilities for the worst, to personalise attacks, including creepy sextortion campaigns. AI is showing up more and more in cybercrime, including tools that help bypass KYC checks for all types of financial services. Fraudsters are also using shady “guarantee services” like Huione, and crypto ATM scams — especially ones targeting older victims — are becoming a real issue.
Crypto’s gone mainstream lately, and so has crime tied to it. It used to mostly be about cyberattacks, but now it’s being used to fund everything from scams to national security threats. As adoption grows, so does the variety of shady uses.
For instance, some criminals today stay off-chain most of the time, but move money on-chain just to launder it quickly. Although the report tracks common categories like stolen funds, darknet markets, and ransomware every year, crypto crime is evolving, and some of the cybercriminal techniques may be yet unknown. It’s more professional now, with organised networks and dedicated services built to help bad actors move dirty crypto around more efficiently.
Ransomware is still bringing criminals hundreds of millions of USD, even though law enforcement crackdowns and fewer victims paying up have slowed things down a bit. Attack volumes stayed high, but payouts dropped.
On the bright side, darknet markets (DNMs) and fraud shops are both shrinking. DNMs brought in $2 billion (down from nearly $2.3B in 2023), and fraud shops made just $220.1 million, which is more than 50% less than last year. One of the main reasons is the takedown of the Universal Anonymous Payment System (UAPS) by U.S. and Dutch authorities, which supported tons of fraud shops like Brian Dumps and Faceless.
Which Digital Assets Are Popular Among Fraudsters?
Notably, not only do the used fraudulent tactics, but also the types of crypto assets commonly targeted by criminals rapidly change.
Up through 2021, Bitcoin was the go-to for cybercriminals, probably because it’s easy to trade. But since then, we’ve seen a clear shift — stablecoins now make up the majority of illicit crypto transactions, accounting for 63%. This change reflects a broader trend: stablecoin use across the entire crypto world is booming, with activity growing about 77% year over year. Besides, transactions linked to sanctioned entities have mostly moved to stablecoins, since these actors want access to dollar-like stability, but are blocked from the actual U.S. financial system.
Still, Bitcoin isn’t out of the picture. Crimes like ransomware and darknet market sales are still mostly BTC-based. Monero — a privacy coin — is gaining traction in those circles too, although it wasn’t included in the Chainalysis comparison.
How Fraud Prevention Works on Crypto Platforms
Crypto platforms face the same sophisticated scams and hacks, and money laundering schemes that all the financial services are plagued with. So, how do they fight back?
KYC & Identity Checks
Before you even start trading, most legit platforms make you go through Know Your Customer (KYC). That means verifying your identity with things like an ID, a selfie, or proof of address. It helps stop fraudsters from making anonymous accounts.
Risk Scoring & Monitoring
Every transaction, login, or withdrawal gets run through risk models. The platform scrutinises user location, IP address, device fingerprint, transaction size or speed, past behaviour and more. If something looks odd (e.g. a login from a new country coincides with a big withdrawal), it gets flagged or blocked.
For instance, Binance uses automated systems to monitor for suspicious login attempts, weird withdrawal patterns, and high-risk trades
Machine Learning & AI
Platforms use AI to spot suspicious patterns, like weird trading behaviours or possible bot activity, and flag them in real time. These models get smarter over time by learning from past fraud cases.
Thus, Coinbase uses machine learning to spot unusual behaviour, including transaction laundering or multiple accounts tied to the same person.
Blacklist & Sanction Screening
Crypto platforms screen wallet addresses against known sanctioned entities, terrorist groups, or addresses linked to scams. If there’s a match, they freeze the funds or shut it down.
Being one of the most regulated exchanges, Coinbase checks all users against sanctions lists and PEP (politically exposed person) databases.
Transaction Tracing Tools & Collaboration With Law Enforcement
Crypto’s public, so platforms use blockchain analytics tools (like Chainalysis, TRM, etc.) to trace the flow of funds. If stolen or suspicious crypto lands on their platform, they can track it and work with law enforcement.
For example, Binance repeatedly worked with global agencies to freeze stolen assets, as in the case when they helped seize $450K linked to a Curve Finance exploit.
Manual Review & Human Analysts
Not everything is automated. High-risk or complex cases often go to fraud teams who manually review the activity, contact users, or escalate to legal teams.
Besides, crypto exchanges like Kraken employ human experts to test their own systems against all possible attack vectors and use a bug bounty program for a wider community of security researchers to spot some errors and security deficiencies.
Security Features for Users
Most platforms offer tools to help users protect themselves, because not everything depends on the company. Crypto investors can typically additionally secure their assets with 2FA (two-factor authentication), withdrawal whitelists, account alerts, and time locks on big transfers.
Kraken also educates users about trending scams (like fake tech support or giveaway frauds) to raise not only the in-house security teams’ but also public awareness of new threats.
What Obstacles Crypto Platforms Face on Their Way to Proactive Protection
Some of the fraud prevention techniques used by crypto platforms are reactive. Thus, many platforms implement real-time monitoring tools that trigger alerts when transactions deviate from typical patterns. Once suspicious activity is confirmed, immediate steps are taken to halt further damage by freezing affected accounts or restricting transactions. Although these systems do detect the fraudulent incident and a response team mobilises to assess the situation, it may already be too late.
In a crypto world, assets move quickly. It’s not about legacy banks that hold money in escrow accounts for days. Here time and money flies by the second. Fraudsters often use crypto mixers to obscure further transactions so that blocking affected accounts may not help. Even if the damage stops spreading, it was already done to the initial victim(s), so that is hardly a proactive prevention measure.
Of course, the reactive approach is necessary for limiting damage and recovering from incidents. Post-incident analysis as well as collaboration with law enforcement is crucial for avoiding similar situations in the future, mending system vulnerabilities and punishing the criminals. However, it only underscores the importance of introducing long-term preventive strategies so that users’ assets and platform integrity are intact in the first place.
Balance Between Transparency and Anonymity
One of the main features of the blockchain applications and cryptocurrency solutions is user anonymity. Unlike traditional financial systems, blockchain offers transparent, secure asset exchange without the feeling that Big Brother is constantly watching you. That is what crypto owners cherish and expect to be present unconditionally.
However, when it comes to fraud prevention, the pseudonymous nature of blockchain transactions poses significant challenges. Tracking the true user identity behind wallet addresses requires sophisticated data analytics and often cross-referencing with off-chain data sources, which complicates fraud detection, often making it impossible.
The dilemma is that while users want their privacy, crypto platforms often lack in-built mechanisms to provide it in a way that also allows them to clearly dissociate from other users, like those involved in illicit activities, if necessary. Besides, there is always an aspect of legal requirements to be considered.
As regulators continue to catch up with the rapid pace of crypto innovation, crypto platforms must continuously adapt their security measures to meet new regulatory demands, which can include more stringent customer verification, enhanced reporting requirements, and increased oversight. Anti-Money Laundering (AML) and Know Your Customer (KYC) policies require extensive monitoring of transactions and users’ identities, which conflicts with blockchain privacy expectations.
To enable more transparency, crypto platforms often process large amounts of data from multiple sources, including transaction records, user behaviour patterns, and external threat intelligence. Integrating these varied datasets into a single fraud detection system presents tangible challenges. Ensuring seamless interoperability among diverse and sometimes siloed systems is a technical hurdle that requires complex infrastructure. Besides, without industry-wide data standards, it is difficult to exchange and compare threat intelligence between platforms. This absence of common formats slows down efforts to develop a strong, collective approach to proactive fraud protection.
Adapting Proactive Fraud Prevention for Crypto Platform Needs
Adapting proactive fraud prevention to meet the unique needs of crypto platforms involves a strategic blend of cutting-edge technology, continuous risk assessment, and fluent operational practices. A key consideration here is striking a delicate balance between preserving the inherent anonymity of blockchain transactions and implementing effective fraud prevention measures.
The pseudonymous nature of blockchain offers privacy benefits that many users value, but it also complicates the tracing of illicit activities. To address this, platforms must leverage aggregated analytics of anonymised data and indirect indicators of suspicious activity. This would require sophisticated behavioural pattern recognition techniques to preserve user identities without sacrificing the integrity of threat detection. Integrating behavioural biometrics into existing systems demands significant IT investments, along with continuous calibration to adapt to evolving user behaviour.
By adopting interoperable data formats and fostering greater collaboration, crypto platforms can enhance collective security. Complementing innovative AI-driven technical solutions with adaptive legal and compliance frameworks, as well as continuous staff training, ensures that proactive fraud prevention measures in the crypto space evolve in tandem with technological advances and regulatory reforms.
Bottom Line
Whether traditional finance or decentralised blockchain applications, proactive fraud prevention today is not possible without fine-tuned, sophisticated artificial intelligence. Powered by machine learning, AI-driven security systems evolve together with the growing cyber threats landscape.
What sets crypto platforms apart is their inherent anonymity. While both traditional money services and DeFi need to analyse tons of aggregated data to detect suspicious activity, blockchain tools may mostly rely on pseudonymous transaction information. That requires considering additional user behaviour patterns and using more third-party intelligence. Striking a balance between anonymity and security might be hard, but not impossible, especially when the best minds in the crypto industry set upon the task.
