Passwords, facial checks and two-factor authentication can prove that a customer has the right credentials. They cannot always prove that the person controlling the session is behaving normally—or that a genuine customer is not being coached into sending money to a scammer. Behavioral biometrics is emerging as the layer designed to close that gap.
KEY FINDINGS
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The Fraud Problem Has Moved Beyond Stolen Passwords
The old model of online-account security assumed that the main question was whether the person logging in knew the correct secret. Passwords, one-time codes, device approvals and biometric face checks all strengthen that gate. But modern fraud increasingly begins after the gate has been passed.
A criminal may have stolen valid credentials. A bot may be using a real identity. A victim may be logged into their own account while a scammer on the phone tells them exactly what to click. Or a previously legitimate account may have become a mule that now funnels funds onward. In each case, conventional authentication can look successful even though the transaction is unsafe.
The scale explains why trading platforms are investing in more continuous forms of risk detection. The FBI said Americans filed 181,565 cryptocurrency-related complaints with more than $11 billion of reported losses in 2025. A year earlier, the FBI reported $9.3 billion of losses involving cryptocurrency. Using $11.0 billion as a conservative floor, that is at least an 18.3% increase in a single year. These figures cover a broad range of internet crime and should not be read as losses occurring inside regulated exchanges, but they show the environment in which exchanges and brokers operate.
Figure 1. FBI-reported U.S. cryptocurrency-related complaint losses. 2025 is plotted at the $11.0 billion lower bound. Sources: FBI 2024 and 2025 Internet Crime reporting.
What Behavioral Biometrics Actually Measures
Behavioral biometrics does not usually ask the customer to perform a new verification step. It observes how a person interacts with a device or application and converts those patterns into risk signals. Depending on the system, that can include keystroke timing, mouse trajectories, touch gestures, scrolling, navigation paths, hesitation, copy-and-paste behavior, device orientation and the rhythm with which forms or security prompts are completed.
The distinction from ordinary behavioral analytics matters. A platform may analyze whether a customer usually trades at 8 a.m. or withdraws $500 at a time. Behavioral biometrics goes closer to the physical and cognitive manner in which the session unfolds—how the person types, moves and interacts. In practice, modern fraud engines combine both.
The U.K. Information Commissioner’s Office explicitly recognizes keystroke recognition, handwriting, gait and gaze as behavioral biometric techniques when technical processing is used to recognize a person. That legal definition is important because it means the technology can move from ordinary telemetry into regulated biometric processing depending on how the data is used.
Figure 2. A practical fraud stack. Behavioral biometrics is strongest when combined with identity, device, transaction and network/on-chain context rather than used as a standalone identity oracle.
The Hardest Case: The Customer Is Real but the Intent Is Not
Account takeover is the intuitive use case. If a fraudster logs in with stolen credentials, their typing cadence, navigation path or device behavior may differ from the genuine user’s baseline. But the more strategically important use case may be authorized scam activity, where the customer is genuine and the authentication is valid.
The FBI’s Operation Level Up illustrates the problem. As of December 2025, the bureau had notified 8,103 potential victims of cryptocurrency investment fraud; 77% were unaware they were being scammed. That is a profound weakness for authentication-only defenses: the account owner can pass every challenge while still being manipulated into an economically disastrous transaction.
Behavioral systems try to detect the friction created by that manipulation. A user may pause unusually long, move through unfamiliar menus, repeatedly switch windows, follow a rigid sequence dictated by a caller, or use a remote-access tool. None of those signals proves fraud. Together with device and transaction context, however, they can justify a warning, delay, step-up challenge or human review before funds leave.
Figure 3. Four fraud states that can look similar to conventional authentication but generate different behavioral signals. Original analytical framework.
How Crypto Platforms Are Using the Technology
The public evidence is strongest where vendors or exchanges describe their controls directly. RockWallet / MNEE App, a crypto and fiat platform, says it expanded its Sardine deployment to include behavioral biometrics, device intelligence, rules-based scoring and sanctions screening. Sardine says the implementation analyzes more than 6,000 risk signals at checkpoints spanning onboarding, KYC, transactions and payments, including signals intended to identify guided mouse movements and remote-access tools.
The vendor-reported results are material enough to show why exchanges care about the technology: RockWallet says chargeback activity fell about 35% year over year, more than 23,000 fraudulent signups were blocked during one coordinated bot attack, and more than $1 million was saved by blocking high-risk transactions before execution. These are customer/vendor case-study figures rather than independently audited performance metrics, so they should be treated as evidence of deployment and potential—not as a universal expected return.
A separate LexisNexis Risk Solutions case study describes an unnamed cryptocurrency exchange using behavioral biometrics to distinguish human from automated traffic during account-takeover attacks. The simple finding is revealing: bot login speeds were more homogeneous, while genuine humans displayed small timing variations. Fraud models often win not by identifying one dramatic red flag but by combining many weak signals that are difficult for automation to reproduce consistently.
Binance publicly describes a broader behavioral approach. Its 2024 anti-fraud report said more than 50 machine-learning models were used on Binance P2P and that AI-based behavioral profiling helped distinguish legitimate activity from potential scams. Its April 2026 research described more than 100 AI models across anti-fraud controls and specifically cited behavioral monitoring for rapid logins, unusual locations, repeated failed attempts and large payments that deviate from typical behavior. Binance also reported $6.69 billion of fraud and scam attempts blocked in fiscal 2025 and another $1.98 billion of user funds safeguarded in Q1 2026. Those are Binance’s own risk-team figures and include more than behavioral biometrics alone.
| Platform / evidence | What is publicly disclosed | Evidence standard |
| RockWallet / MNEE App | Explicit behavioral biometrics + device intelligence across KYC and payments. | Direct customer/vendor case |
| Binance | Behavioral profiling/monitoring; 100+ anti-fraud AI models disclosed. | Direct company disclosure |
| Unnamed crypto exchange | Behavioral biometrics used to separate human from bot login behavior. | LexisNexis vendor case |
| Kraken | Online activity, devices, trading data and behavioral inferences used for fraud/security; biometric deployment not confirmed. | Privacy disclosure |
| Robinhood | Usage/device data, inferred fraud risk and ML fraud protection; typing/mouse biometrics not confirmed. | Privacy/security disclosure |
| eToro | Monitors deviations from expected behavior and fraud/trading alerts; biometrics not confirmed. | Operational evidence |
Why Brokers Need the Same Layer—But Public Disclosure Is Thinner
Retail brokers face many of the same identity and payment risks as crypto exchanges: credential theft, account takeover, card and bank-transfer fraud, hacked email or phone accounts, mule activity and unauthorized withdrawals. They also face trading-specific abuse, where an attacker can alter account settings, place unusual trades, liquidate positions or move cash before the victim notices.
Public broker disclosures show the underlying telemetry even when they do not reveal the exact fraud models. Robinhood says it automatically collects device identifiers, feature usage, timestamps, transaction information, location and tracking-technology data, and receives inferred fraud risk from identity and fraud-prevention partners. It also says it may use machine learning to protect accounts from fraud. Kraken—now spanning crypto, securities and other financial products—collects online activity, trade execution data, device information and behavioral inferences and uses personal data to detect malicious, deceptive and fraudulent activity.
eToro’s current financial-crime hiring materials describe daily monitoring of transactions and account activity for unusual or suspicious behavior, assessment against expected customer behavior and fraud-alert queues that can include hacked or stolen accounts. That is not proof of a typing-cadence biometric system. It is evidence of the same architectural shift: from one-time authentication toward continuous risk scoring across the customer journey.
That distinction is important for rigorous reporting. Security stacks are intentionally opaque because publishing every signal would help attackers reverse-engineer controls. A platform can therefore collect the telemetry needed for behavioral risk without publicly naming its vendor, model or thresholds. The absence of a product announcement is not proof that no behavioral model exists—but it is also not a license to claim one does.
Why Crypto Makes Behavioral Detection More Valuable
Crypto creates a particularly unforgiving combination of speed, global access and transaction irreversibility. A fraudulent card payment may move through a chargeback process. An on-chain withdrawal can be transferred across multiple wallets within minutes, bridged to another chain or converted into an asset that is harder to freeze. That compresses the time available to detect a bad transaction.
It also creates an unusual collision between legitimate high-risk behavior and fraud. A real crypto trader may use a VPN, switch devices, trade at odd hours, move large balances, interact with self-custody wallets and use APIs or automation. Rules that simply flag “unusual behavior” can therefore generate enormous false positives. Behavioral biometrics is attractive because it adds context about *how* the session is being conducted, rather than assuming that unusual transaction characteristics are automatically fraudulent.
The same logic applies to brokers serving active traders. A customer who suddenly sells a portfolio, changes bank details and requests a withdrawal is risky—but may also be acting legitimately. A model that layers device continuity, session behavior, historical trading patterns and transaction context can route only the highest-risk cases into friction.
Original Calculation: The Basis-Point Economics of Fraud Prevention
Fraud-control economics are easy to underestimate because loss rates can look tiny as percentages. But exchange and brokerage payment flows are large enough that basis points matter.
One basis point is 0.01%. On $1 billion of annual outbound transfers, one basis point of fraud equals $100,000. On $10 billion, it is $1 million. On $100 billion, it is $10 million. That means a behavioral layer does not need to eliminate a dramatic share of fraud to become financially relevant—especially once chargebacks, manual-review labor, customer-support costs and reputational damage are included.
Figure 4. Original sensitivity model. The calculation does not estimate any specific platform’s fraud rate or withdrawal volume; it shows how small loss-rate changes scale with transaction value.
The second economic benefit is false-positive reduction. Blocking genuine users has a real cost: abandoned deposits, missed trades, angry high-value clients, support tickets and manual investigations. RockWallet’s emphasis on keeping chargebacks low while continuing to onboard customers captures the real optimization problem. The goal is not “block more.” It is “separate risky behavior from legitimate behavior with enough precision to add friction only where the expected fraud loss justifies it.”
Why 2FA and Face ID Are Not Enough
Two-factor authentication answers whether someone possesses two authentication factors. Face verification answers whether a face matches an enrolled identity. Neither necessarily answers who is directing the transaction, whether malware or remote-access software is present, or whether the customer is acting under social pressure.
This is why behavioral biometrics is better understood as continuous authentication plus intent inference rather than a replacement for MFA. If the user passes a face check but then navigates to withdrawal settings in a way that differs sharply from their history, copies a wallet address from a remote-support session and initiates an unusually large transfer, the behavioral layer provides a reason to slow the transaction even though identity checks passed.
The opposite is also true. A customer can behave differently for benign reasons: injury, a new keyboard, travel, a new phone, accessibility software or simple stress. Behavioral models therefore should not be treated as a deterministic lie detector. Their value comes from combining weak signals and calibrating the response to the stakes.
The Privacy and False-Positive Trade-Off
The same properties that make behavioral biometrics useful also make it sensitive. The technology can continuously observe subtle motor and interaction patterns that users may not realize are being measured. Under U.K. and EU data-protection frameworks, behavioral characteristics can become special-category biometric data when specific technical processing is used for the purpose of uniquely identifying a person.
The ICO’s current guidance is explicit that biometric systems are probabilistic. Lower thresholds catch more potential matches but produce more false positives; higher thresholds reduce false positives but increase false negatives. It also says organizations should test for bias, understand real-world error costs and provide safeguards where an incorrect decision could harm the user.
For an exchange or broker, that creates a design question: what should happen when the model is uncertain? A low-risk login might simply receive silent monitoring. A password change might trigger a second factor. A new withdrawal address could be delayed. A large transfer with multiple scam indicators might go to human review. The larger the potential harm of a false block, the stronger the case for a reversible intervention rather than an unexplained permanent denial.
What Would Prove the Thesis Wrong?
Behavioral biometrics is not automatically a superior fraud control. The thesis weakens if any of four things happen.
- Attackers learn to replay or synthesize realistic interaction patterns cheaply enough that typing, mouse and touch signals lose discriminating power.
- False positives remain high for legitimate traders whose behavior naturally changes across devices, locations, accessibility tools or high-volatility markets.
- Privacy and biometric-data rules make continuous collection too costly or legally restrictive relative to the fraud losses prevented.
- Device intelligence, passkeys, transaction graph models and on-chain analytics capture nearly all of the incremental fraud that behavioral biometrics would otherwise catch.
The most likely outcome is therefore not a world in which behavioral biometrics replaces identity verification. It is a world in which it competes for marginal predictive value inside a larger risk engine. If it stops catching unique fraud after accounting for cost and friction, platforms will reduce its weight.
What Retail Traders Should Watch
For users, the most visible sign of behavioral risk systems will often be adaptive friction: an extra confirmation that appears only on certain withdrawals, a warning that interrupts a transfer, a temporary hold after a device change or a call from the platform when a transaction looks inconsistent with normal behavior.
That can be inconvenient, but the important question is whether the controls are explainable and reversible. A strong platform should give users a route to challenge false positives, disclose the categories of data it collects, separate fraud-prevention telemetry from unrelated advertising uses where possible, and apply more friction to high-value irreversible actions than to ordinary browsing.
Retail users should also understand the limitation: behavioral biometrics protects an account only while the platform can observe the session. It cannot stop a customer from voluntarily transferring assets from an exchange to a scammer after ignoring warnings, nor can it recover funds once they have moved beyond reachable counterparties. It is a detection layer, not insurance.
Bottom Line
Crypto exchanges and brokers are moving from proving identity at login to continuously estimating trust throughout the session. The reason is straightforward: modern fraud can involve the wrong person with the right password, a bot with a valid identity, or the right customer acting under a scammer’s direction.
Behavioral biometrics gives platforms another way to distinguish those states by measuring *how* a session unfolds. The most credible deployments do not use it alone; they fuse it with device intelligence, transaction history, account networks and on-chain context, then choose a proportionate response.
The strongest evidence is not that behavioral biometrics can identify a user with mystical precision. It is that, at scale, even small improvements in fraud detection and false-positive reduction are economically valuable. The hard part is preserving that value without turning every unusual trader into a suspect—or every mouse movement into an unquestioned biometric verdict.
Methodology
Research was updated through 8 October 2026. The article prioritizes FBI, FTC, ICO, company privacy/security disclosures, current company hiring materials and direct vendor/customer case studies. Public disclosure of fraud stacks is incomplete by design, so the article distinguishes confirmed behavioral-biometric deployments from broader behavioral monitoring, device telemetry and inferred fraud-risk systems. Vendor case-study performance figures are labeled as vendor/customer-reported rather than independently audited results.
The 2025 versus 2024 crypto-loss comparison uses the FBI’s “more than $11 billion” 2025 figure as a conservative $11.0 billion floor against the FBI’s $9.3 billion 2024 figure; therefore the calculated 18.3% increase is a minimum. The basis-point model is illustrative: one basis point equals 0.01% of annual outbound value. It is not an estimate of any named platform’s transaction volume or fraud rate.
Sources
1. FBI — Cryptocurrency and AI Scams Bilk Americans of Billions (Apr. 6, 2026)
2. FBI — 2024 Internet Crime Report release (Apr. 23, 2025)
4. FTC — 2025 imposter-scam and total-fraud loss data (June 2026)
5. Binance Research — AI-Powered Crypto Security (Apr. 30, 2026)
6. Binance — From Detection to Recovery: Anti-Fraud Efforts in 2025
7. Binance — Anti-Fraud Refund Initiative 2024 Year-End Report
8. Sardine — RockWallet / MNEE App customer story
9. LexisNexis Risk Solutions — Behavioral Biometrics Success Stories (crypto exchange case)
10. BioCatch — Advanced Behavioral Biometrics
11. BioCatch — Behavioral Insights
12. Feedzai — Digital Identity and account-takeover prevention
13. Feedzai — Scam prevention and behavioral biometrics
14. Mastercard — What Are Behavioral Biometrics?
15. Kraken — Global Privacy Notice, updated Sept. 16, 2026
16. Robinhood — U.S. User Privacy Statement
17. Robinhood — Device Monitoring
18. eToro — Financial Crime Operations Specialist (current careers disclosure)
19. eToro — Credit Fraud Specialist, FinCrime Operations
20. ICO — Biometric Recognition Guidance
21. ICO — Fairness, accuracy and false-positive guidance for biometrics
22. ICO — Lawful processing of biometric data
23. CGAP — Solutions to Protect Consumers from Fraud in Digital Finance (2026)
Johan Shamshad is a financial markets writer at Dave Finances covering cryptocurrencies, trading platforms, brokers, fintech, financial regulation, and developments across global markets. He previously worked at Gulf News, adding newsroom experience to his coverage of fast-moving financial and digital-asset markets.
His work focuses on identifying market-moving events, company developments, regulatory changes, product launches, and shifts in trading and financial infrastructure.
Johan contributes news and analysis designed to help readers understand not only what happened, but why a development matters and how it may affect the wider financial landscape. You can reach out to him via his social media accounts:
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