Biometric authentication is the strongest factor for verifying identity in conversational AI banking channels: voice, face and fingerprint confirm who the customer is without relying on passwords. Combined with knowledge and possession factors in adaptive MFA, it adjusts the verification level in real time based on risk, profile and channel.
At Delto we know that digital banking is advancing by leaps and bounds. In recent years, conversational channels powered by Artificial Intelligence (AI) have established themselves as an essential and strategic point of contact between financial institutions and their customers. We are talking about chatbots in apps, virtual assistants on websites, or seamless interactions through platforms like WhatsApp, delivering immediate and personalized 24/7 support. But behind this immediacy and closeness lies a critical challenge: how do we guarantee with certainty that whoever is chatting is really who they claim to be? In a landscape where digital fraud is constantly rising and security regulations are becoming ever stricter (from Strong Customer Authentication - SCA oversight in Europe to operational risk management standards in Latin America), customer authentication has become a non-negotiable strategic priority . The real challenge in digital banking is striking the ideal balance between robust security and an excellent user experience . Customers expect fast solutions, while we, as technology partners, must ensure that institutions comply with cybersecurity policies and local regulations without creating friction. In conversational channels this balance is even more delicate. The interaction flows naturally, in human language , making traditional methods (username and password) insufficient and insecure. That is why the evolution toward smarter, more secure and contextual authentication models is a smart and necessary step to maintain and strengthen digital trust . Main types of customer authentication in AI At Delto we understand the need for diverse approaches to verify identity. In this blog post we present the fundamental principles that guide authentication in AI channels: 1. Knowledge-based: something the customer knows This is the most traditional method: passwords, PINs or security questions. Implementation in AI: text or voice responses that validate previously registered information. Advantages: easy to implement and understand; compatible with most systems. Limitations: vulnerable to phishing or credential theft. It does not validate the person, only the knowledge of a piece of data. Use case: initial validation for general or low-risk queries. 2. Possession-based: something the customer has It relies on a physical element belonging to the user, such as a token, a mobile app, a one-time passcode (OTP) sent by SMS, or a push notification. Implementation in AI: sending and validating the OTP code within the conversational flow, or confirmation through the banking app. Advantages: it strengthens security without requiring the user to remember additional information. It integrates with existing infrastructure. Limitations: risk if the device is compromised. It requires reliable connectivity and synchronization. Use case: authentication for medium-risk operations such as transfers, payments or access to sensitive data. 3. Inherence-based or biometrics: something the customer is It leverages unique and non-transferable traits of the user: fingerprint, face or voice. Implementation in AI: voice analysis for vocal authentication (voice biometrics) in assistants, or facial/fingerprint recognition through the integrated app . Advantages: maximum accuracy and low friction; it is extremely difficult to forge and enables continuous authentication that is invisible to the user. Limitations: it requires explicit consent and strict regulatory compliance on biometric data; it needs infrastructure and the training of robust AI models. Use case: digital onboarding , highly personalized support or access recovery. Multifactor Authentication (MFA): the definitive strategy We recommend combining these principles to achieve the greatest robustness and security . Multifactor Authentication (MFA) integrates two or more factors (for example, Biometrics + OTP code). MFA in conversational channels: it is applied through adaptive flows managed by AI . The system defines the level of authentication required in real time , based on the type of operation, the risk level and the customer's history. Multifactor Authentication (MFA) Advantages Greater resistance to attacks, regulatory compliance, optimal security/experience balance. Limitations It requires a well-thought-out technology architecture that is compatible across systems . How to choose the right authentication model? Focused on resolution, the key lies in a strategic combination of factors. Financial institutions must analyze three main dimensions: Operation risk: authentication must be proportional to the risk (checking a balance is not the same as making a high-value transfer). Customer profile: habits, devices and technological familiarity. Channel and context of use: conversation by voice, text or embedded app. :root{ --wrap-radius:18px; --row-radius:14px; --header-bg:#CFC8F5; --panel-bg:#F9F8FF; --text:#0F2A2E; --muted:#5A5A6A; } .risk-block{ width:704px; /* ← EXACTO */ margin:0; /* ← sin margen */ padding:0; /* ← sin padding */ font-family:"TT Hoves", system-ui, -apple-system, "Segoe UI", Roboto, Arial, sans-serif; color:var(--text); } /* HEADER */ .risk-head{ --cols: 1.05fr 1.35fr 0.8fr 1.35fr 1.1fr; display:grid; grid-template-columns:var(--cols); gap:24px; background:var(--header-bg); border-radius:var(--wrap-radius); padding:14px 20px; /* ← padding interno correcto */ font-size:14px; font-weight:400; margin:0 0 16px 0; /* ← solo separación vertical */ } /* BLOQUES */ .risk-section{ background:var(--panel-bg); border-radius:var(--row-radius); padding:16px 20px; /* ← padding interno */ margin:0 0 16px 0; /* ← sin margen lateral */ } .risk-grid{ --cols: 1.05fr 1.35fr 0.8fr 1.35fr 1.1fr; display:grid; grid-template-columns:var(--cols); gap:24px; align-items:start; } .cell{ font-size:14px; line-height:1.5; font-weight:400; padding:6px 0; border:none; } .risk-row{ display:contents; } .dim{ font-weight:600; } .span-3{ grid-row:span 3; padding-top:6px; } .nowrap{ white-space:nowrap; } /* ——— RESPONSIVE ——— */ @media (max-width:900px){ .risk-block{ width:100%; } .risk-head{ display:none; } .risk-section{ padding:16px 16px; } .risk-grid{ grid-template-columns:1fr; gap:0; } .dim{ display:none; } .risk-section::before{ content:attr(data-dim); display:inline-block; font-size:12px; font-weight:600; color:var(--text); background:rgba(15,42,46,.06); border-radius:999px; padding:4px 10px; margin-bottom:12px; } .risk-row{ display:block; padding:12px 0; } .cell{ padding:8px 0; display:grid; grid-template-columns:140px 1fr; gap:8px; } .cell::before{ content:attr(data-label); color:var(--muted); font-weight:600; font-size:12px; } } Dimension Practical example Risk level Recommended authentication Main objective Operational risk Balance or transaction inquiry Low Knowledge-based (simple PIN) Minimize friction, ensure a security baseline. Transfer request or limit increase Medium / High Multifactor (OTP + biometrics or token) Ensure double validation for critical operations. Adding a new payee or changing personal data High MFA combined with usage patterns Verify identity and prevent impersonation. Customer profile Young user with the banking app installed Medium Possession-based (push notification) Speed and technological familiarity. Older adult supported via WhatsApp Medium / Low Knowledge-based or voice biometrics Avoid complexity while keeping conversational fluidity. Corporate customer with multiple accesses High MFA combined with usage patterns Granular control and traceability of accesses. Channel and context of use Assistant on web or app Variable Possession-based or facial biometrics Speed and technological familiarity. Hybrid assistant with handoff to a human agent Variable Personal data validation Avoid complexity while keeping conversational fluidity. Integration in WhatsApp Business Variable OTP combined with usage patterns depending on the identified risk Granular control and traceability of accesses. Best practices for secure conversational channels Our commitment is to help our customers go one step further and stand out. The goal is to find the sweet spot between agile security and a high-quality user experience . The most innovative banks and financial institutions we work with are applying these approaches: Progressive integration: start with simple validations and escalate security only if the risk demands it . Minimize friction: keep the conversation natural without awkward redirects or pauses. Comply with regulations: guarantee explicit consent and data protection, especially in biometrics. Centralize identity management: ensure that all channels share the same authentication and audit model . Monitor and learn: fraud patterns change, so we must always be ready and willing to update the risk signals. Trust is the new digital currency Authentication is no longer a simple compliance requirement; it is an essential component of the customer experience and of the banking innovation strategy. AI conversational channels offer a unique opportunity to combine advanced technology with our promised human closeness. But to take full advantage of it, identity verification must be as intelligent, agile and expert as the conversation itself. In a reality where trust defines the relationship with the user, in LATAM, the banks that adopt adaptive, secure and frictionless authentication schemes will be better positioned to lead the next wave of digital financial services. At Delto we support financial institutions in integrating secure, scalable and experience-centered authentication within their AI-powered conversational channels, thereby strengthening digital trust and responsible innovation in banking. Related reading: Delto security and compliance · conversational AI in bank contact centers · conversational banking platform .
What are the authentication types in AI conversational channels? There are three core types: knowledge-based (something the customer knows, such as a PIN or password), possession-based (something the customer has, such as an OTP or push notification) and biometric (something the customer is, such as voice or face). MFA combines two or more of these factors.
How do you choose the right authentication model for a bank? You analyze three dimensions: the risk of the operation (proportional to its impact), the customer profile (habits, devices, tech familiarity) and the channel and context of use (voice, text or embedded app). Authentication should escalate only when the risk demands it.
What is adaptive MFA in conversational channels? It is multifactor authentication applied through AI-managed flows that define the required level of verification in real time, based on the type of operation, the risk level and the customer's history, without adding unnecessary friction.
What is biometric authentication in banking and which types exist? Biometric authentication verifies a customer identity through unique physical or behavioral traits. In conversational banking the most common types are voice biometrics, facial recognition and fingerprint. It is the inherence factor: something the customer is, impossible to forget and very hard to steal or replicate at scale.
How does biometric authentication relate to automated KYC? Automated KYC uses identity verification with biometrics to onboard and validate customers without manual processes. The same liveness check and face or voice comparison used during onboarding can be reused in every conversation, keeping regulatory compliance intact without adding friction to the customer experience.