10 Biometric Authentication Methods in Banking [Guide]

A customer opens their banking app with a fingerprint or a quick face scan. No PINs. No passwords. Just instant access that feels secure and effortless.

That convenience explains why 40% of banks now use physical biometrics to fight fraud, up from about 26% five years ago.

The catch is that fraudsters are evolving just as fast. Deepfakes and synthetic IDs can mimic human traits with frightening accuracy.

This guide explores the 10 best biometric authentication methods in banking, examining their strengths, weaknesses, and how to leverage them to prevent early fraud.

Key takeaways about biometric authentication methods

• Biometric authentication relies on unique traits: Banks use physical features like fingerprints, facial scans, iris patterns, and palm veins, along with behavioral markers such as typing rhythm or swipe gestures.
• Each method has trade-offs: Fingerprints are simple and widely adopted, facial recognition is convenient, and iris scanning offers exceptional accuracy. More advanced techniques like palm vein or behavioral biometrics provide strong security but face challenges with cost, accessibility, or privacy concerns.
• AI-driven fraud changes the game: Deepfakes, voice cloning, and synthetic identities can trick biometric systems, proving that even advanced methods are not foolproof. Criminals are evolving fast, which means banks need to think beyond relying on a single form of authentication.
• Biometrics work best when paired with fraud analytics: Biometric authentication methods confirm identity but cannot block every fraudulent transaction. VALID Systems fills this gap by analyzing deposits in real time and sharing cross-bank intelligence to stop fraud before it turns into losses.

What are the biometric authentication methods in banking?

Biometric authentication methods in banking verify “something you are” rather than “something you know.”

This includes physical traits like a fingerprint, facial scan, iris, or palm vein pattern, or a behavioural marker such as typing rhythm or swipe patterns on a mobile device.

Unlike passwords, biometric systems don’t store raw images. Instead, they generate secure mathematical templates, which are compared against new samples to confirm a match.

Because biometric data uniquely identifies individuals, banks must meet stricter obligations when collecting and processing it.

The appeal is clear: stronger security, fewer password resets, and smoother mobile access

Demand is also strong among customers:

• 72% of global consumers prefer biometric authentication over traditional PINs.
• 83% of consumers believe biometric payments are safer than passwords.
• 47% of U.S. consumers say they would use biometric payments if offered.

10 proven biometric authentication methods that stop fraud

Banks face a wave of AI-driven fraud that passwords and PINs can’t stop. 

These 10 biometric authentication methods give institutions the power to verify real customers and shut out impostors before losses occur:

1. Fingerprint authentication

A fingerprint sensor scans the ridges, valleys, and minutiae points of the fingertip. 

Capacitive sensors measure electrical charge differences, while ultrasonic sensors use sound waves to create a 3D map of the fingerprint. The system converts these details into a mathematical template and compares each new scan against the stored version.

Use cases:

• Mobile banking apps (TouchID, Android Fingerprint)
• Cardless ATM withdrawals
• In-branch customer verification

Advantages:

• Fast and easy to use
• Low-cost and widely available
• Highly accurate with negligible chance of duplicates
• Familiar to most consumers

Disadvantages:

• Susceptible to forgery with molds if liveness detection is absent
• Injuries or worn prints can cause failed logins
• Cannot be reset if compromised
• Some users (e.g., manual laborers) have unreadable prints

Security:

Modern devices store fingerprint templates in secure enclaves, not central servers. Advanced sensors include liveness detection to spot fake fingers. Best used alongside another authentication factor for stronger protection.

2. Facial recognition authentication

A camera captures facial features such as eye spacing, jawline, and cheek contours. Advanced systems use 3D depth mapping with infrared dots or structured light to create a “faceprint.”

Algorithms compare the stored template with the live image, while liveness detection checks for movement, blinking, or thermal signals to prevent counterfeiting.

Use cases:

• Unlocking banking apps (Apple FaceID, Android equivalents)
• ATM and kiosk logins
• Remote onboarding via video teller sessions

Advantages:

• Contactless and convenient
• Quick and user-friendly
• Works when hands are full
• Generally accurate

Disadvantages:

• Struggles in poor lighting or with masks
• Vulnerable to imitation through photos, videos, or deepfakes
• Raises privacy concerns under GDPR and similar laws

Security:

Banks use 3D cameras, liveness checks, and thermal sensors to ensure scans are real. NIST requires banks to pair face recognition with another factor for high-assurance authentication.

3. Iris recognition authentication

An infrared camera scans the intricate patterns of the iris, capturing rings, furrows, and freckles invisible in normal light.

The system encodes these features into an “iris code.” During login, the system compares the live iris scan with the stored code.

Use cases:

• Mobile banking apps and ATMs (common in Asia)
• High-security corporate or government facilities
• Border control systems

Advantages:

• Extremely accurate and stable for life
• Works with glasses or contact lenses
• Fast and non-invasive

Disadvantages:

• Specialized hardware is costly
• Requires careful eye alignment
• Less effective in bright sunlight
• Limited adoption in everyday banking

Security:

Iris templates are encrypted and often include liveness checks, such as pupil response to light. Its high accuracy makes it suitable for sensitive banking operations when banks can justify the costs.

4. Voice recognition authentication

The system analyzes both physiological features (vocal tract shape, larynx) and behavioral features (tone, rhythm, accent). The system extracts the frequency spectrum, pitch, and resonance to form a “voiceprint.”

The live voice sample is matched to the stored template, often using challenge-response phrases to prevent replay attacks.

Use cases:

• Telephone banking and call centers
• Mobile banking apps with voice login
• Fraud prevention in customer service

Advantages:

• Requires no extra hardware beyond a microphone
• Easy and natural to use
• Works remotely over phone or app
• Can be combined with other checks

Disadvantages:

• Accuracy suffers with noise, illness, or poor connections
• Vulnerable to deepfake or cloned voices
• Privacy concerns about storing voiceprints
• Less reliable in critical situations

Security:

Banks use fraud-prevention prompts and inaudible signals to confirm liveness. Some pair it with device fingerprinting or caller ID.

Nonetheless, banks must remain alert: FinCEN reports that fraudsters are “using deepfake voices” in family-emergency and corporate phone scams, so voice ID alone should be part of a layered defense.

5. Palm vein recognition authentication

Near-infrared light penetrates the skin, absorbed by hemoglobin in the blood, revealing unique vein patterns. 

The captured image is turned into a vascular template and compared with stored data.

Use cases:

• ATMs and branch services in Japan
• Hospitals and hygienic high-security facilities
• Experimental use in laptops and access systems

Advantages:

• Extremely hard to forge since veins are internal
• High accuracy with low false accept rates
• Contactless and hygienic
• Proven adoption in Asia with millions enrolled

Disadvantages:

• High hardware costs
• Limited global adoption outside Asia
• Requires correct palm positioning
• Not suitable for smartphones

Security:

Palm vein’s inherent security is remarkably strong. To counterfeit it would require an elaborate biological replica. For extra safety, systems encrypt the vein image during transfer and storage. Given its track record, palm vein remains one of the most secure biometric methods available, albeit at a higher cost.

6. Hand geometry authentication

A user places their hand on a scanner with guide pegs.

Cameras measure finger length, width, thickness, and hand proportions. The system converts these into a numerical profile for comparison.

Use cases:

• Legacy access control for bank branches
• Secure facilities and staff time-tracking

Advantages:

• Quick and easy to use
• Durable technology for high-traffic areas
• Works in any lighting conditions

Disadvantages:

• Less unique than fingerprints or iris patterns
• Higher error rates and security risks
• Requires bulky hardware
• Rarely used in modern banking

Security:

Provides only moderate security. Often paired with ID cards or PINs for staff access, but has largely been replaced by more precise biometrics.

7. Behavioral biometrics

Rather than a static physical trait, behavioral biometrics analyzes how a user interacts with devices.

This method includes patterns like:

• Keystroke rhythm and pressure
• Swipe speed and angle
• Mouse movement curves
• Navigation habits on websites and apps

Machine learning models create a “behavioral profile” of each user. The system monitors each session and flags the session if patterns deviate sharply, suggesting a fraudster may be in control.

Use cases:

• Online and mobile banking logins
• Continuous monitoring during transactions
• Fraud detection in account takeovers

Advantages:

• Frictionless and invisible to users
• Difficult for fraudsters to mimic
• Works in the background
• Proven effective in reducing fraud rates

Disadvantages:

• Privacy concerns around monitoring behavior
• Needs extensive user data to be accurate
• Can trigger false positives when behavior changes
• Still relatively new in banking

Security:

Behavioral biometrics act as a risk-scoring layer, complementing static checks. FFIEC recognizes them as an advanced authentication method, especially for detecting account takeovers.

8. Dynamic signature verification

A digital pen or tablet captures how the user writes the signature on the tablet by recording:

• Speed of the strokes
• Pressure applied to the surface
• Order in which strokes are made
• Rhythm and flow of the writing

The system converts these dynamics into a biometric profile. It then compares new signatures against stored profiles and flags differences in movement to detect forgeries.

Use cases:

• Teller desks and in-branch check processing
• ATMs with signature pads
• Mobile apps requiring digital signing

Advantages:

• Builds on the familiar practice of signing
• More reliable than static signature checks
• Quick to capture and verify

Disadvantages:

• Signature style can vary based on writing conditions or speed
• Skilled forgers may mimic dynamics
• Requires specialized hardware
• Lower accuracy than newer biometrics

Security:

Banks often use it as part of multi-layer fraud detection for check processing. It works best when combined with manual review or additional authentication methods.

9. Device-native biometrics (passkeys)

Device-native biometrics verify identity directly on the user’s smartphone or laptop without sending biometric data to the bank.

How it works:

1. The user scans a fingerprint or face on the device.
2. The device compares the scan with the biometric template stored in its secure enclave (e.g., Apple Secure Enclave, Android TEE).
3. If the match succeeds, the device unlocks a private cryptographic key.
4. The device uses this key to sign a one-time challenge sent by the bank.
5. The bank verifies the signed challenge but never sees or stores the biometric itself.

Use cases:

• Mobile banking app logins
• Browser authentication using WebAuthn/FIDO2
• Passwordless access to online accounts

Advantages:

• Fast and convenient
• Resistant to phishing and credential theft
• No biometric data leaves the device
• Supported across major devices and browsers

Disadvantages:

• Device-bound users must re-enroll if the device is lost
• Older devices may lack secure enclaves
• Some users may need fallback authentication

Security:

Meets FIDO and NIST standards for cryptographic authentication. Highly resistant to copying since private keys never leave the device.

10. Multi-modal biometric authentication

Combines two or more biometrics in one flow. Examples include:

• Fingerprint + face scan
• Voice + behavioral biometrics
• Iris + face recognition

The system processes each modality, generates scores, and combines them to make a final decision. This method makes copying much harder, since an attacker must bypass multiple systems at once.

Use cases:

• High-security banking pilots
• ATMs requiring dual biometrics for large withdrawals
• Government and border security systems

Advantages:

• Stronger security than single biometrics
• Reduces false accepts and rejects
• Compensates for the weaknesses of individual methods

Disadvantages:

• More complex and costly to deploy
• It can feel inconvenient for users
• Limited adoption in everyday retail banking

Security:

Considered the “gold standard” in fraud prevention. Especially valuable against deepfakes and AI-powered spoofing.

Comparison of biometric methods

Here is a comparison table that shows how the 10 biometric authentication methods stack up in terms of ease of use, accuracy, spoof-resistance, cost, and adoption:

Emerging fraud trends and regulatory guidance

While these 10 biometric methods each have unique strengths and weaknesses, banks must also navigate a changing fraud landscape and evolving regulations.

Recent trends include:

• Deepfake and AI-generated IDs: In Nov 2024, FinCEN warned banks that fraud rings are using GenAI to fabricate images, videos, and documents to defeat onboarding and authentication controls. A real-world example hit headlines in 2024 when a deepfake video conference misled a Hong Kong finance employee at a UK firm, Arup, into sending about USD$25 million.
• Voice cloning and social engineering: Scammers use voice cloning to impersonate family members or executives, tricking victims into wiring funds. A deepfake “video call” from a boss or bank official could fool an employee or customer. As a result, regulators now warn banks to treat such AI-enabled social engineering as a serious risk.
• Regulatory pressure: Authorities worldwide have tightened rules on biometrics and authentication. In the U.S., NIST’s new SP 800-63-4 (2025) imposes strict requirements: for high-assurance logins, presentation-attack detection is mandatory, facial recognition can’t stand alone, and a second factor must accompany biometrics.

Improve biometric security with VALID Systems

In conclusion, biometric authentication methods are diverse, each with unique benefits for banking security and user experience.

VALID Systems helps banks bridge biometrics and fraud controls. While biometrics like fingerprint or face confirm who the user is, VALID’s platform ensures that every transaction or deposit aligns with that identity.

Integral ways VALID complements biometrics include:

CheckDetect® - Check fraud detection

Even if biometrics verify a customer, a fraudulent check deposit can still slip through.

CheckDetect uses AI to score every deposited check in real time by analyzing depositor behavior, payee history, and consortium data. It flags more than 75% of potential check fraud losses at the moment of deposit, far outperforming static rules and protecting banks from synthetic or stolen check schemes.

InstantFUNDS® - Instant deposit decisions

Once identity is confirmed, InstantFUNDS applies machine learning and shared fraud intelligence to decide which deposits qualify for immediate availability.

VALID approves over 90% of check items for instant access, while the platform covers any items returned later. This approach gives customers real-time liquidity and allows banks to generate revenue through opt-in fees, without taking on added fraud risk.

Edge Data Consortium - Shared intelligence

Fraudsters often test schemes across banks. VALID’s Edge Data Consortium connects signals from 420M+ accounts and $4T in annual transactions. If a synthetic identity cashes checks at one bank, others see the pattern instantly. This privacy-preserving network expands each bank’s defenses beyond its own data, catching mule networks and account takeovers that biometrics alone might miss.

Looking to improve your biometric authentication methods?

Partner with VALID Systems to secure identity verification, stop fraudulent deposits, and protect customer trust.