Voice AI for Banking

Voice AI agents for retail banks, neobanks, and credit unions that field high call volumes on routine enquiries, balance checks, fraud confirmations, loan status, and payment instructions, without adding headcount.

Built on a real-time conversation pipeline with Deepgram for speech recognition, GPT-4o for intent routing, and live API calls to your core banking system. PCI-DSS aware architecture, encrypted call recordings, and audit-ready transcripts included.

  • Balance and transaction query voice agents with live core banking API integration

  • Fraud alert confirmation bots with verbal dispute capture and case system update

  • Loan status voice agents that pull live origination data and escalate when needed

  • Account opening and payment instruction flows with step-up authentication

RaftLabs builds voice AI agents for banks, neobanks, and credit unions. A voice AI deployment handles high-volume inbound call types, balance and transaction queries, fraud alert confirmations, loan status enquiries, and payment instructions, authenticating callers, pulling live account data via API, and responding in under 500ms. Projects ship in 10 to 14 weeks at a fixed cost. The architecture is PCI-DSS aware, with call recording, encrypted storage, and audit-ready transcripts. RaftLabs builds on Deepgram for speech recognition and GPT-4o for intent routing.

Recognition

Sound familiar?

  • Call centre drowning in balance and transaction queries that an agent could resolve in under 60 seconds without a human?

  • Fraud alert callbacks queued behind routine calls, so your team confirms disputes hours after the suspicious transaction?

  • Loan status enquiries and product questions consuming agent time during peak periods while complex cases wait?

Companies we've built for

Vodafone
Nike
Microsoft
Cisco
T-Mobile
Aldi
Heineken
GE
Cost delivery
Fixed
Week delivery cycles
10-14
Products shipped
100+
Aware architecture from sprint one
PCI-DSS

Call centre cost is a routing problem, not a headcount problem

Most bank contact centres handle a predictable mix of inbound calls. A large share are routine: a customer wants to know their balance, confirm a recent transaction, check where their loan application stands, or verify whether a payment went through. These calls follow a narrow script, require data that already exists in the core banking system, and end quickly once the customer has the answer.

The cost is not in the complexity of these calls. It is in the volume. When every balance query, every fraud callback, and every loan status check routes to a human agent, the queue fills, wait times grow, and the agents who should be handling disputes or onboarding conversations spend their day reading account balances.

A voice AI agent built for banking deflects the high-volume routine interactions without degrading the experience. The caller authenticates by voice, the agent pulls live data from the core, and the call resolves in under 90 seconds. For fraud callbacks, the agent calls the customer proactively, confirms the flagged transaction, captures a verbal response, and updates the case system, without a human in the loop. The human agents who remain on the queue handle the calls that actually need them.

Problems we solve for banking operators

  1. 01
    Problem

    Call centre volume dominated by balance and transaction queries

    Solution

    Balance enquiries and recent transaction lookups are the single highest-volume inbound call type at most retail banks and credit unions. Each call is short, but the aggregate volume is large enough to keep a meaningful share of agent capacity occupied with work that follows the same three steps every time: authenticate, retrieve, respond. When agent capacity is constrained, these routine calls sit in the same queue as disputes, complaints, and onboarding conversations, adding wait time across every call type.A voice AI agent handles this interaction end-to-end. The caller authenticates using a spoken PIN matched to the CRM record, or via voice biometric for higher-assurance flows. The agent calls the core banking API in real time, retrieves the balance or transaction list, and reads it back in natural language with sub-500ms latency. The call closes without a transfer. Agents handling the remaining queue deal with interactions that require judgment, not lookups.

  2. 02
    Problem

    Fraud alert callbacks waiting in the agent queue

    Solution

    When a fraud rule fires on a transaction, the clock starts. The longer it takes to confirm whether the transaction is legitimate, the longer the card stays active or blocked in the wrong state, and the longer the customer waits for resolution. Most fraud alert workflows rely on SMS or email, which require the customer to initiate a response, and response rates on passive channels are lower than voice. When callbacks are handled by agents, they join the same queue as inbound calls, which means the fraud case may sit unconfirmed for hours during peak periods.A voice AI fraud callback agent calls the customer automatically when the alert fires, identifies itself clearly, confirms the transaction details in plain language, and captures a verbal confirmation or dispute. The response is logged, the case system is updated, and, if the customer reports the transaction as unauthorised, the case is escalated to a fraud specialist immediately. The loop closes in minutes rather than hours, and it closes without occupying agent time on a structured call that follows the same script every time.

  3. 03
    Problem

    Loan status and product enquiries compressing agent capacity during peaks

    Solution

    Applicants call to ask where their loan stands. Customers call to ask about rate changes, product features, or eligibility criteria. These calls land during the same peak windows as every other inbound enquiry, morning and early evening, when agent capacity is already under pressure. A loan status call requires the agent to authenticate the customer, pull the application record, and read back a status that is already in the origination system. A product enquiry requires the agent to read from a script the customer could have found on the website.A voice AI agent handles both. For loan status, the agent authenticates the caller, queries the origination system via API, and reads back the current status, the next required action, and the expected timeline. For product enquiries, the agent delivers a structured response from a curated knowledge base and offers to connect the caller to a specialist if the conversation needs to go further. Neither call type requires an agent. Both types contribute to queue congestion when they do.

What we build

  1. Balance and transaction query voice agents

    Voice agent integrated with the core banking API to handle inbound balance and recent transaction enquiries end-to-end. Authentication uses a spoken four-digit PIN matched against the CRM record, with a fallback to a security question for unrecognised numbers. For institutions with voice biometric infrastructure, the authentication layer connects to the existing biometric service rather than building a separate credential store.

    Account data is pulled in real time via the core banking REST or SOAP API. The agent reads the current balance, available balance, and pending transactions in natural language. If the caller asks about a specific transaction, the agent filters by date, merchant, or amount and reads back the matching result. Response latency runs at 380 to 450ms on a properly provisioned infrastructure, which keeps the interaction conversational. The agent handles the full call lifecycle, greeting, authentication, data retrieval, response, and close, and logs the interaction to the audit trail with a timestamp, authentication outcome, and data accessed. Calls that request account actions the agent is not authorised to complete are transferred to a human agent with the authentication context passed through so the customer does not re-authenticate. PCI-DSS aware data handling governs how account data is used within the call session and what is retained in call logs.

  2. Fraud alert confirmation bots

    Outbound voice agent triggered automatically when a fraud rule fires in the transaction monitoring system or card processor. The agent calls the cardholder's registered number, identifies the bank by name, and describes the flagged transaction, merchant name, amount, and timestamp, in plain language without reading a card number or full account number over the call.

    The caller confirms or disputes the transaction verbally. The agent captures the response, logs the outcome with a timestamp and the caller's authentication status, and updates the fraud case system via webhook. If the customer confirms the transaction as legitimate, the agent closes the call and the case is marked confirmed. If the customer reports the transaction as unauthorised, the agent escalates immediately to the fraud team via case system alert and, where configured, initiates an automated card block before the escalation completes. The agent does not request card numbers, PINs, or full account numbers during the confirmation call. All call recordings are encrypted and retained to the institution's configured retention policy for AML and FCA, PSD2, or FinCEN audit requirements. DTMF fallback allows customers who prefer keypad input to confirm or dispute without speaking, covering accessibility requirements and environments where voice input is impractical.

  3. Loan status voice agents

    Inbound voice agent integrated with the loan origination system to handle application status enquiries. The caller authenticates, the agent queries the origination system by application reference or customer identifier, and reads back the current stage, the last action completed, the next required action from the applicant, and the expected timeline to the next stage.

    Where the origination system returns a status that requires explanation, for example, a request for additional income documentation, the agent reads the specific document required and the submission instructions, and offers to send an SMS summary to the customer's registered number so they have the detail in writing. For applications that have reached a decision, the agent reads the outcome and transfers to a human specialist if the customer has questions about the decision that fall outside the agent's authorised scope. The agent does not communicate a credit decision reason or offer to override an underwriting outcome, those interactions route to a trained human. All status calls are logged to the origination system audit trail. AML and KYC status flags on the application record are checked before the agent reads back status information, if the application has an open compliance hold, the call routes to a compliance officer rather than an automated response.

  4. Account opening voice flows

    Voice-guided account opening flow for retail current accounts and savings products, designed for customers who prefer voice to a web form. The agent collects the applicant's name, date of birth, address, and employment status in a structured conversational flow, confirms each field before proceeding, and submits the completed record to the account opening system via API.

    Identity verification connects to the institution's existing KYC provider, Onfido, Jumio, or Alloy, via a handoff that sends the applicant an SMS link to complete document capture and liveness check after the voice intake is complete. This keeps the structured data collection in voice while delegating the document verification step to a purpose-built mobile flow, rather than trying to handle document capture over a phone call. The agent captures explicit verbal consent to the account terms and records the consent event with a timestamp and call reference. AML screening runs automatically when the application record is submitted, the agent does not communicate the AML screening outcome but routes flagged applications to the compliance queue for manual review. The account opening flow is scoped to personal accounts; business account opening with company verification and beneficial owner checks is handled as a separate flow with additional data collection steps.

  5. Dispute initiation voice automation

    Inbound voice flow for customers initiating a transaction dispute. The agent authenticates the caller, asks them to describe the transaction they want to dispute, and uses the description to retrieve the matching transaction from the account record via the core banking API. The caller confirms the transaction, states the reason for the dispute, not recognised, wrong amount, duplicate charge, or goods not received, and the agent creates a dispute case in the case management system with the transaction reference, dispute reason, and a transcript of the caller's statement.

    Where the institution uses Mastercard Dispute Resolution or Visa Dispute Resolution as the card scheme dispute system, the dispute case is formatted to the scheme's required fields and submitted automatically, reducing the manual step of a human agent translating the customer's verbal description into a structured scheme submission. The caller receives a reference number and an estimated resolution timeframe before the call closes. The dispute transcript is retained as part of the case record, satisfying the evidence requirement for scheme-level disputes. Dispute cases involving potential fraud, where the customer states the card was not in their possession or the PIN was not used by them, are flagged as potential fraud and escalated to the fraud team rather than processed as a standard chargeback dispute.

  6. Payment instruction voice agents

    Voice agent for handling inbound payment instructions, bill payments, peer transfers, and standing order setup, using a structured confirmation flow that reduces the risk of misdirected payments. The agent authenticates the caller, collects the payee name, account details, and amount, reads back the full instruction for verbal confirmation, and submits the payment to the payment rail via the core banking payment API.

    Step-up authentication triggers for payments above a configurable threshold, typically the same threshold the digital banking app applies for strong customer authentication (SCA) under PSD2 or Regulation E. For payments above the threshold, the agent sends an OTP to the registered mobile number and requires the caller to read it back before the payment is submitted. Payments are not submitted without explicit verbal confirmation of the full instruction details. The agent does not accept payments to payees not already on the customer's registered payee list without step-up authentication, reducing the risk of authorised push payment (APP) fraud, a specific concern for FCA-regulated institutions under the APP fraud reimbursement rules. Payment instruction calls are logged with the full instruction details, the authentication events, and the payment reference returned by the core, satisfying FCA and PSD2 audit trail requirements.

Frequently asked questions

Voice AI for banking is an automated phone agent that handles inbound and outbound calls on behalf of a bank, credit union, or neobank, authenticating callers, pulling live data from core banking systems and origination platforms, and completing structured interactions without a human agent in the loop. Common interactions handled by banking voice AI include balance and transaction enquiries, fraud alert confirmations, loan status updates, dispute initiation, and payment instructions.

A banking voice AI agent differs from a generic interactive voice response (IVR) system in that it understands natural language rather than requiring the caller to select from a numbered menu. The caller can say "what did I spend at Tesco last Tuesday" or "I want to dispute a charge" and the agent understands the intent, retrieves the relevant data, and responds in plain speech. The agent operates within defined authorisation boundaries, it can read account data and confirm transactions, but it cannot override credit decisions or release holds without human authorisation. All interactions are logged to an audit trail with timestamps and authentication events for FCA, PSD2, FCA, FinCEN, and AUSTRAC compliance purposes.

Banking voice AI agents use layered authentication matched to the risk level of the interaction. For low-risk interactions such as balance enquiries, a spoken four-digit PIN matched against the CRM record is typically sufficient. For higher-risk interactions, payment instructions above a threshold, dispute initiation, or account changes, the agent applies step-up authentication: an OTP sent to the registered mobile number that the caller reads back before the interaction proceeds.

Voice biometric authentication is available as an additional layer for institutions that want passive voice matching without requiring the caller to remember a PIN. The biometric enrolment is separate from the voice AI deployment and connects to the agent via API. Knowledge-based authentication, last transaction date, registered address, or similar, is available as a fallback for callers who cannot provide their PIN.

All authentication events are logged to the audit trail with the authentication method used, the outcome, and the timestamp. PCI-DSS requirements for cardholder data handling apply to any interaction where card or account data is read or transmitted during the call, and the agent architecture is designed to meet those requirements from the start.

The voice AI architecture is designed to account for the primary regulatory frameworks applicable to the institution's jurisdiction. For UK-regulated institutions, that includes FCA consumer duty requirements for clear and fair communication, PSD2 strong customer authentication requirements for payment instructions, and FCA APP fraud reimbursement rules for payment instruction flows. For US institutions, PCI-DSS requirements for cardholder data, Regulation E requirements for electronic fund transfer disclosures, and FinCEN record-keeping requirements for customer interactions. For Australian institutions, AUSTRAC AML/CTF Act obligations for customer identification and transaction monitoring.

Cross-jurisdiction concerns, for institutions regulated in multiple markets, are scoped during discovery to confirm which regulatory standards apply to each interaction type and how the audit trail and data handling requirements differ by jurisdiction. KYC and AML checks that apply at onboarding are enforced at the voice agent layer for account opening flows, with the agent routing flagged applications to a human compliance officer rather than processing them automatically. Call recordings are encrypted and retained to the institution's configured retention period, with access controls limiting retrieval to authorised staff.

A focused single-workflow deployment, for example, a balance and transaction query agent with core banking API integration and PIN authentication, typically ships in 10 to 14 weeks from kickoff to production. That timeline includes integration, testing against the core banking API, and a parallel-run period where the agent runs alongside the existing IVR before taking live traffic independently.

A multi-workflow deployment covering several interaction types, balance queries, fraud callbacks, loan status, and payment instructions, with full core banking, origination system, and case management integration typically runs 14 to 20 weeks. The primary variable is the institution's vendor security assessment and third-party approval process, which for regulated financial institutions can add 6 to 10 weeks to the overall timeline regardless of development complexity. RaftLabs scopes every project before pricing, you receive a fixed cost and delivery schedule before development begins.

A focused banking voice AI deployment, one interaction type, one core system integration, PIN authentication, typically runs between $40,000 and $80,000 including integration, testing, and the parallel-run period. A production system covering multiple interaction types with core banking, origination, and case management integration, step-up authentication, and a full compliance review typically runs $90,000 to $180,000.

Ongoing costs include LLM API usage (billed per token by the model provider), telephony infrastructure (SIP trunking or a telephony platform such as Twilio or Vonage), and maintenance as the core banking API or regulatory requirements change. RaftLabs gives you a fixed project cost before development starts. Ongoing infrastructure costs are estimated during scoping so you have the full picture before committing.

What clients say

What our clients say

Three-year average engagement. Founders and operators describing the work in their own words. No marketing varnish.

Charles E.
Charles E.
USA flagUSA
Entrepreneur at Aggie Technologies

All of the sprints were completed on schedule and on budget. We highly recommend RaftLabs!

Related services

Talk to us about your banking voice AI project.

Tell us which interaction types generate the most call volume, which core banking or origination system we need to integrate with, and what your regulatory jurisdiction is. We will scope the right agent and give you a fixed cost.

  • Scope and cost agreed before work starts. No surprises. No obligation.
  • Working prototype within 3 weeks of kickoff.
  • Pay by milestone. You see progress before each invoice.
  • 60-day post-launch warranty. Bug fixes, UI tweaks, and deployment support. No retainer.
  • All conversations are NDA-protected.