How Can You Verify the Authenticity of a Caller in Real Time?

Call center automation has made handling thousands of calls routine, but one problem still trips teams up: caller fraud and caller ID spoofing. How can teams protect customers and maintain trust while keeping pace? How can you verify the authenticity of a caller without adding friction in a live conversation? This article explains practical steps and real-time methods like caller ID verification, voice biometrics, one-time codes, behavioral signals, and CRM checks to instantly confirm identity during a live conversation, preventing fraud, protecting customers, and maintaining trust without slowing the experience.

Bland AI’s conversational AI integrates real-time caller authentication into every call, using voiceprint checks, caller screening, ANI, and CRM signals to verify identity while the agent speaks with the caller. It stops social engineering attacks, reduces call-handling time, and keeps agents focused on helping customers.

Summary

• Caller ID and vocal familiarity are no longer reliable indicators of legitimacy; in 2025, over 50% of phone calls are expected to be scams, meaning agents who rely on those cues are making high-stakes decisions based on forged signals.  
• Scam volume is already hurting customer experience and operations, with businesses reporting a 30% increase in customer complaints from scam calls. In comparison, 68% of customers still prefer to call for help, concentrating risk on voice channels.  
• Adding verification can directly recover pipeline and connect rates, for example, when caller verification was added to an SDR program, the connect rate rose from 12% to 19%, improving downstream pipeline velocity.  
• Automated, layered authentication materially reduces losses: studies show that caller authentication cuts fraud losses by roughly 50%, lowering chargebacks and investigation overhead.
• Passive-first flows and voice biometrics enhance the caller experience while improving security, often reducing average call handling time by 30%.
• Operationalizing verification requires clear rules and monitoring, for example, encoding proofs as tokens with TTL, using thresholds such as proceed above 85%, route to supervised flow at 50 to 85%, escalate below 50%, and validating changes on a 10% traffic slice during a 30-day pilot. 

This is where Bland AI fits in. Conversational AI addresses this by integrating real-time caller authentication into every call, using voiceprint checks, caller screening, ANI, and CRM signals to verify identity while the agent speaks.

Why Caller ID and “Sounding Legit” Are No Longer Enough

Many teams assume that if the caller ID appears correct or the voice sounds legitimate, the call is safe. That belief is dangerous because spoofing, SIM and VoIP farms, AI-generated voices, and social engineering enable attackers to manipulate the very signals humans rely on, rendering those cues no longer indicative of authentication.

How Do Attackers Fake the Signals You Trust?

Attackers can rewrite the metadata your phone displays, route calls through low-cost VoIP systems, and stitch together short audio clips or fully cloned voices to match the person you expect to be. This pattern appears repeatedly across billing, healthcare, and utility contact centers: frontline agents face a flood of calls, where surface cues align perfectly, and they must decide within seconds whether to:

• Escalate
• Hand over data
• Block the call

That pressure is exhausting and error-prone, which explains why, even though 68% of customers still prefer calling when they need help, trust in the channel is collapsing as attackers get better at sounding authoritative.

What Does the Data Say About Risk and Trust?

The stakes are not theoretical; they are financial and reputational. Juniper Research forecasts that global losses from robocalling fraud will exceed $80 billion in 2025, highlighting how costly unchecked voice fraud has become. At the same time, trust has eroded: research from Caller ID Reputation shows that 70% of consumers no longer trust caller ID information, meaning your agents are often judged against a backdrop of customer skepticism before a single verification step occurs.

How Can Verification Actually Stop This?

Caller authentication works by combining multiple signals, so you no longer rely on one fragile cue. Voice biometrics confirms identity through a voiceprint; liveness detection verifies that the audio is from a live human and not a replay or synthetic model; and risk scoring blends call metadata, device signals, and historical behavior to assign a trust level in real time. 

Biometric Risk Routing

Voice recognition algorithms can detect synthesis artifacts that humans miss. When these technologies are integrated into an agent workflow, they enable teams to route, escalate, or require stronger authentication only when the risk justifies it. GDPR and CCPA require this level of rigorous protection, and doing it right reduces customer friction while safeguarding sensitive data.

The Verification Bottleneck

Most teams manage verification by leaning on caller ID and scripted challenges because those methods are familiar and fast. That works at low volume, but as call complexity and fraud scale, it breaks down: verification decisions take longer, disputes rise, and agents burn time on false positives and negatives. 

Adaptive Voice Verification

Platforms like Bland AI provide enterprise-grade voice biometrics, liveness checks, and real-time risk scoring that integrate with existing call flows, helping teams reduce manual verification time and route high-risk calls for additional verification without disrupting genuine customers. It’s one thing to list capabilities; it’s another to make verification an operational muscle you can test live, measure, and optimize — and that shift is what separates defensive teams from resilient ones. That sounds decisive, but what happens when verification starts affecting revenue and customer experience in unexpected ways?

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Why Verifying Callers Is Now a Revenue Requirement

Verification failure costs you real money, operational capacity, and the hard currency of trust, and human judgment alone cannot scale to stop it. When caller identity isn’t an operational control, your pipeline leaks at multiple points:

• Valid prospects never connect
• Fraud drains margins
• Agents waste hours on manual checks
• Customers quietly stop believing you

Those are not hypothetical losses; they compound, and dialing is no longer a growth engine.

Why Does Unchecked Calling Erode Revenue?

Most teams treat caller identity as a checkbox rather than infrastructure, and that habit quietly compresses conversion rates. Calls to prospects are often met with suspicion, so each additional friction or security step increases the risk of a hangup or complaint, which in turn prompts carriers to lower your reputation and mute future campaigns. According to LSEG Risk Intelligence, over 70% of businesses report increased revenue after implementing caller verification. This correlation between verification and topline demo that it is a revenue-generating motion, not merely a compliance requirement.

How Does Fraud Show Up Inside Operations?

Fraud is rarely an isolated payment loss; it inflates average handle time, swamps dispute teams, and produces post-call remediation that eats agent capacity. Expect more:

• Callbacks
• Manual identity resets
• Multi-channel investigations when verification is ad hoc

That operational drag slows sales cycles and pushes down rep productivity, turning a two-hour campaign window into a daylong cleanup task.

What Do the Direct Financial Stakes Look Like?

When verification is absent, losses are multi-layered: direct fraud, recovery costs, legal and investigative fees, and the opportunity cost of deals lost to poor deliverability. Implementing authentication mitigates these risks, as research from LSEG Risk Intelligence shows that caller verification reduces fraud losses by an average of 40%. This demonstrates that verification affects both measurable hard costs and less tangible soft costs that must be tracked.

Why Human Judgment Breaks Down at Scale

It’s exhausting for reps to make verification calls in real time and decide within seconds whether to escalate or release data. That fatigue breeds inconsistency: one rep blocks, another shares; one team escalates, another lets it pass. This pattern appears across SDR teams and enterprise contact centers. As volume grows, human-first verification creates variability that attackers exploit and auditors penalize.

Most Teams Do This the Familiar Way, Then Discover the Cost

Most teams handle identity checks with vendor lists, scripted KBAs, and rep judgment because those methods are familiar and require little upfront engineering. That works early on, but as campaigns scale across regions and channels, the approach fragments: audits fail, disputes increase, and routing errors multiply. Platforms like Bland AI offer an alternative path, providing voice biometrics, liveness detection, risk scoring, and native hooks into call routing, enabling teams to automate identity decisions, preserve agent time, and maintain a complete audit trail without disrupting genuine customers.

What Happens to Customer Trust When Verification is Missing?

Customers do not forgive repeated friction or mistakes. A single mishandled verification can create a persistent belief that your calls are risky and untrustworthy, and those perceptions spread faster than any remediation you run. Practically, that means increased call blocking, more “did-not-authorize” disputes, and a measurable erosion in repeat contact willingness that marketing dollars cannot easily restore.

Where Operational Risk Concentrates, and What That Looks Like in Practice

If you map outcomes to points in your call flow, risk clusters where identity is assumed, not proven: number origination, the IVR gate, and agent handoffs. When those points lack automated signals and audit logs:

• Investigations take longer
• Remediation becomes manual

From Perimeter to Pipeline

Picture a handful of cases that each consume hours of engineering, legal, and agent time; multiply that across an enterprise, and the fiscal impact is immediate. It’s tempting to treat verification as a perimeter control, but it needs to be an operational capability you can measure, test, and iterate. 

Operationalizing Outreach Integrity

When we build calling programs at SalesHive, we treat identity, consent, and carrier reputation as core infrastructure, not a rep-by-rep habit; making verification operational reduces variance, accelerates routing decisions, and converts handoffs into measurable slivers of risk rather than open-ended investigations. That sounds decisive, but the next question is the hard one — how do you actually confirm a caller in real time without slowing every interaction down?

How Can You Verify the Authenticity of a Caller in Real Time?

In a B2B sales agency context, phone call verification isn’t one feature—it’s a layered framework. You’re verifying the phone number itself (is it valid and reachable), the caller’s identity (is this really your business), the recipient’s permissions and preferences (can you call, and should you), and the purpose of the call (does the script and flow match what’s allowed).

Unified Compliance Architecture

The most common mistake we see in outsourced sales team builds is treating verification as a “dialer setting” rather than a workflow that starts in list-building services and ends with clean activity logging. If consent lives in one spreadsheet, DNC in another, and the dialer pulls from a third system, reps will inadvertently bypass safeguards, especially under quota pressure.

The Accountability Framework

A simple way to operationalize this is to define each verification layer, who owns it, and how it is proven later. That clarity is what keeps B2B cold-calling services compliant while still moving fast. While it can be frustrating for customers to confirm their identity, verifying identity over the phone is crucial. In fact, it might be the most critical thing your contact centre does! Getting it wrong can lead to serious problems like fraud, data breaches, and a complete erosion of customer trust.

The Verification Mandate

With scams becoming more sophisticated, verifying who’s on the other end of the line is no longer just a box to tick – it’s a critical step you must complete every time to safeguard your business, protect your customers, and maintain your company’s reputation. So, here’s some advice on how to get it right – in ways that protect all involved, whilst keeping the process:

• Smooth
• Respectful
• Secure

How Does Caller Authentication Work?

Caller authentication requires additional knowledge and actions from users. The complexity and effort required depend on the verification methods you use. In general, caller ID authentication is either passive or active.

Passive authentication

Passive authentication is primarily handled in the background. This type of authentication uses seamless verification processes that require minimal customer action. Some types of passive authentication include ANI and biometric voice authentication.

Active Authentication

Active authentication requires customers to take direct action. Identification and validation occur only after the user has satisfied the requirements. Multi-factor authentication works by requiring an OTP or something the customer knows. Token-based authentication is another example of active authentication. After the user verifies their identity, they’re given a unique access token that grants them access to the relevant account.

What is the Caller Authentication Process?

The global cloud-based contact center market is anticipated to grow from $26.70 billion in 2024 to $147.10 billion by 2034, at a CAGR of 14.27% during the forecast period. You may be like many organizations investing in business communications, and caller authentication is critical for you. However, it's vital to understand the authentication process before choosing a verification system.

Initiation

When someone contacts your contact center team, an auto-attendant or IVR system greets them. In some cases, they may reach an agent directly. During initiation, authentication steps are performed, including ANI matching and validation. At this stage, the user's number is compared against customer records.

Verification

After initiation, the automated system or support team member begins the verification process. Now is when active and passive verification methods come into play. This includes:

• Knowledge-based authentication
• OTP
• Voice recognition

Approval or Denial

Once the verification data has been submitted, the system or agent cross-references it with records on file. Active authentication methods, such as MFA, are handled by the system with little to no effort from a contact center team member.

The Authentication Pivot

The identity verification is either approved or denied. If access is granted, the interaction moves forward as usual. If the customer is unable to authenticate, several outcomes may occur. They are notified of the denial. Next, you can allow them to reattempt or use an alternative method. For example, instead of sending an OTP via email, they can answer a security question. If authentication is fully automated, you can escalate denials to live agents, who can implement dynamic KBA or other verification methods.

10 Best Methods to Verify the Identity of Your Callers

1. App-Based or Secure Messaging Channel Verification

If a customer calls from within your app (e.g., via a secure call or chat button), they’ve already been authenticated through the app’s login process – often using biometrics like fingerprint or face ID. This gives you a high level of certainty that the user is who they say they are. WhatsApp and other secure messaging platforms can also be used for initial contact, with two-factor authentication (2FA) or in-app verification links sent before a live conversation.

Pros

• Customers are already verified by the time they speak to someone
• Easy and convenient for mobile-first users
• Strong encryption and security features
• Reduces time spent on authentication during the call

Cons

• Only works for customers who’ve downloaded and logged into the app
• May exclude older or less digitally confident demographics
• Security still depends on the integrity of the user’s device
• Best used when: Your audience is mobile-savvy, and you want to combine customer service with streamlined, app-based access.

2. Single Verification Across Channels (No Repeats!)

Let’s say a customer passes verification in the IVR or with an agent and is then transferred to another department. Rather than making them repeat the same process, your systems store a “verified” status or token that carries over, so other agents know the identity check has already been completed. This can be done using CRM flags, call-tracking metadata, or integrated case-management systems.

Pros

• Respects the customer’s time and effort
• Reduces frustration and repetition
• Helps agents pick up the conversation where it left off

Cons

• Requires good integration across platforms and departments
• Needs clear internal policies on when and how to reverify
• If overused, it may leave security gaps if not reviewed regularly
• Best used when: You want to create a seamless, joined-up customer journey – especially for more complex or multi-step enquiries.

3. Manual Verification via Agent-Led Questions

With this method, a human agent asks the caller a set of predefined security questions. These are usually based on:

• Personal details (postcode, date of birth)
• Known transactions or account activity
• Memorable word or secret question
• Good agents are trained to spot hesitation, inconsistent answers, or signs of deception – and to escalate suspicious calls accordingly.

Pros

• Doesn’t require fancy tech – accessible for all budgets
• Flexible – agents can tailor questions to the situation
• Still a critical backup, even in tech-heavy contact centres

Cons

• Prone to human error and social engineering
• Answers can be guessed or stolen (mainly if data leaks occur)
• Slower and more intrusive for the customer
• Best used when: You want a cost-effective, tried-and-tested method – but you’ll need strong agent training and policies in place.

4. AI-Powered Call Routing and Voice Biometrics

AI-powered contact centre systems, such as Amazon Connect and Talkdesk, use advanced technologies to enhance call routing and caller verification. For instance, Amazon Connect can analyze conversations in real time by detecting sentiment, keywords, and compliance triggers. Talkdesk, on the other hand, uses predictive routing to match callers with the most suitable agents based on past interactions and context. Some of these systems also incorporate voice biometrics, which analyze unique vocal traits such as:

• Tone
• Cadence
• Speech patterns

In some cases, the caller’s voice is compared to a stored “voiceprint” to automatically verify their identity. Others focus on behavioural cues, such as hesitation, inconsistent answers, or signs of stress, and flag suspicious calls for further investigation.

Pros

• High-tech, passive, and seamless for the caller
• Extremely difficult for fraudsters to fake voiceprints
• Reduces reliance on knowledge-based authentication (which can be leaked or guessed)

Cons

• Can be expensive to implement and maintain
• Requires a large enough sample size to establish a trusted voiceprint
• Doesn’t always work well with accents, illness, or background noise
• Best used when: You have the budget and scale to support AI, and want to future-proof your security process.

5. One-Time Passcodes (OTPs) and Secure Links

When customers are required to use a one-time passcode, it is sent to their registered mobile number or email address. The caller must read this code back or enter it into the system to proceed. In other cases, a secure link is sent (e.g., for ID verification or account updates) that expires after a short period and can only be accessed by the intended recipient.

Pros

• Highly secure – codes are single-use and time-limited
• Valid for high-risk or sensitive transactions
• Easy to integrate into digital journeys

Cons

• Only works if contact details are up to date
• Can cause delays if customers can’t access their email or phone
• Still vulnerable if their inbox or phone has already been compromised
• Best used when: You need an extra layer of protection for account changes, payments, or access requests, especially over phone or digital channels.

6. Automated Checks via IVR (Interactive Voice Response)

An interactive voice response (IVR) is an automated phone system that interacts with callers via voice prompts or keypad inputs, often used to collect information (such as account numbers or dates of birth) before connecting the caller to a live agent. When a caller first connects, the IVR system asks them to input identifying information – usually via the keypad or speech recognition.

This could include things like:

• Date of birth
• Account number
• Last digits of a payment method
• A PIN or memorable word
• Advanced IVRs can perform initial checks before a human agent is involved, verifying against the customer record in your CRM or database.

Pros

• Reduces call handling time
• Can be accessed 24/7
• Frees agents up to focus on more complex or sensitive tasks
• More secure than verbal questioning (less susceptible to social engineering)

Cons

• It may be frustrating for older customers or those who are less tech-savvy
• Not always suitable for customers with speech or hearing difficulties
• If the system fails to recognize input, it can loop and cause frustration
• Best used when: You want a frictionless, front-loaded way to handle large call volumes while reducing agent workload.

7. CRM-Integrated Verification

If you have a customer relationship management (CRM) system, you can use it to automatically pull up a caller’s details—like their name, account history, and previous interactions—based on their phone number or ID input. This allows agents to verify the caller more quickly and accurately without relying on lengthy security question lists.

Intelligent Identity Matching

When a call comes in, your telephone system pulls up the customer’s profile in your CRM using the incoming number (caller ID), IVR input, or a secure code. Agents can then verify the caller using known data. 

Example:

• Recent purchase history
• Past service interactions
• Registered address or email

This allows for more tailored, confident questioning and faster authentication.

Pros

• Creates a seamless, personalized experience
• Reduces the need to ask blanket questions
• Gives agents more context to spot fraud or unusual behaviour

Cons

• Only works if your CRM is regularly updated and accurate
• Caller ID spoofing is possible – so CRM matching should never be the only method
• Can lead to data silos if systems aren’t properly integrated
• Best used when: You want to empower agents to make quicker, smarter decisions without relying solely on scripted security questions.

8. Multi-Factor Authentication (MFA)

Multi-factor authentication requires more than one unique factor to verify someone’s identity. When using two forms of verification, it’s known as two-factor authentication (2FA). MFA is commonly used when logging in to online banking accounts or accessing health records.

What Are the Multiple Factors Used to Authenticate a Customer?

One of the most common MFA methods is to send a one-time password via SMS or email to the customer. Without access to either option, the user cannot verify their identity.

• Something you know: Customers can select predetermined questions and answers, such as “What was the name of your first pet?”
• PIN: An additional factor can be as simple as a personal identification number. This requires scammers to gain two different pieces of information to gain access.

Silent Security, Active Protection

Platforms like Vonage offer silent authentication, an improved 2FA user experience that doesn’t require additional customer input (no OTPs), along with stronger security for the business. MFA isn’t only for your customers. It’s also a great way to secure your business operations. A 2024 JumpCloud Survey found that 83% of IT professionals require employees to use MFA to access system resources.

9. ANI Matching and Validation

ANI stands for automatic number identification. It’s a telecommunications technique that streamlines phone number validation for contact centers. Much like caller ID, an ANI tool identifies an inbound phone number. An additional step is taken to verify that the displayed number matches a phone number in a database of known customer numbers. If a customer calls from an unusual phone number, such as a payphone number, the system would be alerted to a potential scammer. ANI verifies known numbers, so your team can begin resolving issues immediately.

10. Knowledge-Based Authentication (KBA)

Knowledge-based authentication uses information only the customer knows to verify their identity. This type of authentication falls strictly under the “something you know” category mentioned above.

Contextual Identity Gates

Security questions that involve a mother’s maiden name, place of birth, or pet name are only one type of KBA. You can also tailor questions to the business. For example, financial institutions often use loan amounts or credit card details for verification. KBA usually falls into one of two categories:

• Static KBA: This involves predetermined questions and answers that customers encounter when creating user accounts.
• Dynamic KBA: This involves real-time, changing information, such as transaction history or a previous address. Dynamic authentication adds a higher level of security. An example question would be, “From which retailer did you make a purchase of $__.__ on [date]?”

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Best Practices for Effective Caller Authentication

Use a Layered Security Approach

What layers should you chain together, in what order, and why? Start with lightweight, automated signals that block most attacks without human friction. Implement ANI and carrier-reputation checks at pre-dial and answer times to filter spoofed numbers, and apply passive voice biometric scoring during IVR or the first 5–10 seconds of speech to detect synthetic voice artifacts. If confidence falls below a predetermined threshold, escalate automatically to an active second factor such as an OTP delivered via an app or an in-call verification token, not additional KBA that leaks PII. 

Adaptive Risk Choreography

Operationalize thresholds as traffic rules: confidence above 85 percent proceeds; 50–85 percent routes to a supervised verification flow; below 50 percent moves to fraud operations. That choreography converts ambiguous signals into clear routing actions, reducing false accepts and stopping attackers before they reach an agent.

Focus on Employee Training

How do you teach agents to apply these layers without slowing callers? 

• Train in short, scenario-driven modules that map precisely to the automation you run.
• Run four 30-minute labs in the first month:
  • Passive-signal interpretation
  • Scripted responses for low-confidence calls
  • Escalation protocol
  • Simulated social-engineer interactions
• Use role-play with recorded synthetic-voice examples so agents learn the audible differences that matter to the ML model, and score their sessions against objective KPIs, such as:
  • Correct escalation rate 
  • Time-to-escalation 
• Tie each training module to a single metric, then coach to the metric. That reduces inconsistent rule application and the emotional strain agents experience when they must decide under pressure.

Leverage Data Encryption

What checks prevent a verified call from being hijacked later? Require SRTP for media and TLS for SIP signaling end-to-end, and verify cipher suites during vendor onboarding. Mandate server-side key management in an HSM with quarterly key rotation and enforce AES-256 storage for recordings and transcripts. 

Immutable Verification Artifacts

Validate encryption with automated penetration testing and a quarterly compliance audit confirming that no cleartext credentials are logged. These controls close the gap that allows attackers to intercept calls after verification, turning the verification proof into a defensible, encrypted artifact rather than a fragile spoken promise.

Robust Password Management

How do you stop weak customer passwords from undoing verification? 

• Enforce password rules at account creation: a minimum of 12 characters, checks against a banned password list, and client-side entropy meters to guide customers. 
• Add frictionless recovery options, such as one-time email links or in-app biometrics, and sunset static passwords by offering passwordless enrollment within 90 days. 
• When possible, replace password reliance with token-based session verification tied to device posture so agents are not asking customers to produce secrets that can be stolen over the phone. That directly mitigates credential stuffing and account takeover.

Monitor, Analyze, and Improve

Which metrics tell you the system is healthy, and how do you act on them? 

• Track these daily: average authentication time, percent of calls auto-verified, manual escalation rate, fraud detection accuracy, and customer effort score.
• Run red-team simulations monthly, then compare model confidence drift and false-positive trends against a baseline; if false positives increase by more than 10 percent in two weeks, roll back the last model change and run targeted retraining. 
• Feed resolved fraud cases back into your model pipeline, so signatures for new attack patterns enter production quickly. This continuous loop prevents the slow degradation that turns a secure system into a compliance headache.

Choose the Right Software

What should your procurement checklist look like? 

• Prioritize API-first platforms that natively support ANI validation, voice biometrics, MFA, CRM connectors, and low-latency call routing. 
• Require SOC 2 Type 2, PCI DSS alignment if payment work occurs on calls, and a transparent encryption and key-management policy. 
• During evaluation, run a 30-day pilot on a 10 percent traffic slice and measure both blocked fraud attempts and customer friction metrics; then require a plan for model updates and rollback. Choosing flexible, auditable software avoids the need to rebuild core flows later and keeps integration costs predictable.

What Features Should the Best Call Center Authentication Solutions of 2026 Include?

Look for multi-factor options that combine passive and active checks, passwordless token flows, VoIP caller ID management with STIR/SHAKEN signal processing, branded calling for outbound trust, and AI features that surface conversational intent and anomaly signals in real time. 

Unified Governance Infrastructure

The platform should offer role-based administration, immutable audit logs, and prebuilt connectors for your CRM and workforce tools so verification decisions and evidence travel with the record. When testing a demo, verify that each feature generates an auditable event that can be exported for compliance review. Most teams handle verification with manual checks because it feels immediate and low-cost. That familiar approach scales poorly: as volume grows, rules fragment across agents, spreadsheets, and one-off exceptions, which multiplies investigation work and creates audit gaps. 

Frictionless Identity Intelligence

Teams find that platforms such as Bland Live centralize identity signals and continuous voice AI, compress verification from minutes to seconds, and produce consistent, auditable evidence without adding agent burden.

Practical Quick Wins You Can Deploy This Month

What can you do in four weeks that matters?

• Run a pre-dial carrier-reputation sweep on your top 50,000 numbers and quarantine anything transient.
• Add a passive-voice biometrics enrollment prompt to the IVR for returning callers, and use it to seed your model.
• Implement a simple escalation rule that routes low-confidence calls to a specialist queue rather than escalating to additional KBAs.
• Update the password policy and send a one-time enrollment email to enable passwordless login. Each action maps directly to a risk: number spoofing, synthetic voice fraud, human-escalation error, and credential reuse.

Quantifiable Security ROI

Early evidence from programs like this is promising: a report on call center authentication methods finds that 80% of call centers report a reduction in fraud attempts after implementing multi-factor authentication. The same report also shows that call centers using voice biometrics often experience a 30% decrease in average call handling time. This is significant because faster, more confident authentication frees up agent time for valid exceptions.

A practical analogy helps keep priorities straight. Treat verification like airport security: most travelers pass through a fast, automated lane; suspicious signals trigger a secondary screening line with more detailed checks; only a small fraction move to full manual inspection. Build the fast lane first, tune the middle lane to catch sophisticated attempts, and reserve people for the rare, high-risk events. The frustrating part? Getting these mechanics right requires process, training, encryption, and vendor selection to work together, not independently.  

Book a Demo to Learn About Our AI Call Receptionists

Most teams keep adding manual checks to aging IVR and call workflows because they feel familiar. As call volumes rise, that frays consistency, slows authentication, and ties up staff. Book a Bland AI demo to watch self-hosted, real-time conversational voice agents handle live calls, surface clear confidence scores and audit-ready evidence, and show how faster, reliable verifications preserve revenue and free agents for higher-value work. Experience the future of voice automation today, book a demo, and see how Bland would handle your calls!

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