Selene is an advanced Conversational AI platform that combines natural language understanding with banking-grade security. Integrated with Zeta's next-gen processing platform Tachyon, Selene helps issuers rapidly launch intelligent card assistants with out-of-the-box capabilities for personalized support, insights and actions across the card lifecycle.
This paper examines Selene's capabilities, architecture, security controls, and implementation framework - offering issuers a comprehensive guide to delivering intelligent, personalized card support at scale.
Introduction
Card Support Needs an AI Revolution
Card issuers face a mounting customer support challenge: call volumes are projected to grow 20% in three years1, but traditional agent-based models can’t scale to meet this demand.
The growing complexity of digital payments is placing higher demands on existing support infrastructure. This is compounded by inflexible capacities, talent shortages, and rising operational costs.
20%
projected call volume growth in the next three years. Traditional agent-based support models cannot scale to meet this demand.
Issuers are addressing these challenges by investing in self-service capabilities and digital capabilities across chat, IVR and live agent channels. However, the response to these digital solutions has been mixed. Only 10% of newly built digital platforms are fully scaled or adopted by customers2 due to cognitive load, lack of personalization, and broken context across interactions (Image 1).
Some issuers are also experimenting with AI to enhance their support infrastructure.Their approach includes deploying predictive analytics for proactive customer engagement, AI-assisted agent tools for complex queries, and automated chatbots as first points of interaction in digital channels.
75%
expected reduction in live agent dependence through direct integration of AI solutions with next-gen card processing platforms
The more forward-looking issuers are integrating AI directly with their processing platforms to enable real-time data access, automated workflows, and intelligent service orchestration across channels. Through these implementations, issuers expect to automate 50-80% of routine queries while reducing live agent dependence by up to 75%3.
Despite the investments in AI however, issuers have yet to unlock its true potential in customer support. Chatbots - the most common AI implementation - demonstrate this gap clearly. They have increased the complexity of user interactions instead of simplifying them. Advanced Conversational AI can solve this, enabling natural interactions that transform how cardholders access support.
The Conversational AI Implementation Gap
While chatbots brought about a breakthrough in customer care through automation of responses they have largely failed to solve persistent challenges like personalization, adapting to human language, and gauging the context of a customer’s query. Data on customer dissatisfaction with chatbots keeps surfacing, and a recent survey reported 68% consumers4 having a bad experience when the chatbot was unable to understand their need or answer their question.
68%
dissatisfied customers reporting a bad experience with a chatbot that was not able to answer their question or understand their need
Combining the capabilities of Conversational and Generative AI underpinned by a next-generation processing platform can help issuers overcome these challenges. With these capabilities, an advanced Conversational AI card support solution can deliver:
A natural language interface enabling intelligent, unscripted conversations
Intelligent automation of routine query resolution at scale
Awareness of cardholder intents and context continuity through the conversation
Personalization of conversation through cardholders’ historical and real-time data
Intelligent insights and action to meet customer needs
Contextual escalation to human agents
These capabilities form the foundation of Selene, Zeta’s advanced Conversational AI that reimagines what card support can be.
Introducing Selene: Advanced Conversational AI for Card
Zeta is pleased to introduce Selene, an advanced Conversational AI for cardholders that brings intelligent and personalized card support to life. Selene breaks through the limitations of conventional support solutions to deliver what cardholders truly need - instant and contextual engagement, relevant insights and automated actions - that scale seamlessly for issuers of all sizes and without the limitations of today’s solutions.
This solution paper presents Selene capabilities and impact as follows:
Part 1: Selene: AI-Powered Card Support at Scale
A view of the 4-layer architecture and system orchestration that enable dynamic cardholder experiences and rapid deployment of new workflows on Selene
Part 2: System Design: Selene's Architecture and Orchestration
A view of the 4-layer architecture and system orchestration that enable dynamic cardholder experiences and rapid deployment of new workflows on Selene
Part 3: Building Trust: Selene’s Banking-Grade Guardrails and Security
Measures to ensure accuracy and reliability of Selene’s responses, prevent hallucinations and deliver quality and regulatory compliance
Part 4: Processing Foundation: The Role of Next-Gen Processing Systems in Conversational AI
An introduction to Selene’s core capabilities, its differentiators with respect to current chatbot models, and its enterprise-grade deployment capabilities
Part 5: Path to Value: Framework for Conversational AI Adoption
A 4-part adoption framework covering business, operational, compliance and security, and technology considerations to help issuers maximize value realization from Conversational AI
Watch Selene Transform Cardholder Support Experiences
Experience card support that understands context, responds intelligently, and takes action instantly. See how Selene combines human-like understanding with banking-grade security to deliver support across the card lifecycle.
Part 1: Selene
AI-Powered Card Support at Scale
Selene offers cardholders an intelligent, personalized and human-like experience at scale
Selene is an advanced Conversational AI platform for card support. With Selene, issuers can rapidly launch banking-grade intelligent card assistants with out-of-the-box capabilities for personalized support, insights and actions across the card lifecycle. Given the rapidly evolving nature of AI capabilities, Selene addresses high frequency card support requests5 out-of-the-box and also enables issuers to easily build custom intent flows and continuously evolve their AI-powered support capabilities over time. This allows them to stay ahead of the curve and differentiate their customer support.
Transformed Cardholder Experience
Selene offers cardholders an intelligent support experience through natural, human-like conversation across voice and chat. While conventional apps and automated voice responses force users through static menus, forms, and workflows, Selene leverages Generative AI models that enable intelligent, natural conversations, recognition of user intent, and context continuity in conversations without the constraints of pre-defined scripts.
Cardholders can request real-time insights on their transactions and ask for automated actions through these natural interactions. When situations require human expertise, Selene ensures seamless transitions by intelligently escalating to live agents with full context preserved.
Selene’s ability to understand intent and resolve cardholder queries can be classified as follows:
1. Knowledge Driven Queries
Queries that can be resolved entirely from issuer knowledge base and product documentation.
Examples:
‘How do I apply for a credit card’
‘How do I increase my credit limit’
2. Data Driven Queries
Queries that require customer, account, card or transaction data along with the issuer knowledge base to resolve.
Examples:
‘Why was my transaction declined’
‘Why have I not received my card yet’
3. Insight Driven Queries
Queries that require customer, account, card or transaction data, analysis on this data as well as the issuer knowledge base to resolve.
Examples:
‘Show my expenses by merchant category’
‘Have I spent more money on groceries or gas last month’
4. Action Driven Queries
Queries that require a multi-step workflow leveraging customer, account, card or transaction data, its analysis and the issuer knowledge base to complete the desired cardholder action.
Examples:
‘I’d like to file a dispute against the last transaction on my card’
‘I need to make a payment’
Use Case: Cardholder Making a Payment Using Selene
The following example showcases Selene in action helping a cardholder make a payment against their outstanding balance.
Intelligence Beyond Scripted Chatbots
Most current implementations of chatbots fail to deliver intelligent, personalized customer experiences due to limited context awareness and poor integration with underlying core systems.
Selene’s intelligence, allowing it to overcome these limitations, stems from two powerful integrations:
Direct access to issuer knowledge bases ensuring responses align with institution-specific policies and operating procedures
Native integration with Zeta’s next-generation card processing platform, Tachyon to create, read, and update customer, account, card or transaction data in real-time
With these integrations firmly in place, Selene can deliver human-like customer service through capabilities across three important dimensions:
1. Seamless context navigation
Selene maintains continuous conversation context, just like a human agent. Users can modify requests mid-conversation - for example, a user can initiate a request to pay $100 against their credit card bill and change the amount to $150 in the same conversation without restarting the process. When escalation is needed, the entire conversation context is transferred to human agents.
2. Banking-grade hallucination prevention
Selene manages the risk of incorrect or arbitrary responses through robust guardrails. Every response is grounded in issuer-specific knowledge bases and follows pre-approved workflows, ensuring accuracy and compliance.
3. Human-like conversations
Interacting with Selene reduces cognitive load. Cardholders can express what they want to know or do in everyday language, without worrying about the right terminology. It enables this through four capabilities:
Advanced Speech Recognition (ASR) for accurate voice command interpretation
Natural Text-to-Speech (TTS) for clear vocal responses
Precise Intent Detection to understand user needs
Contextual Dialogue Generation for natural conversation flow
Enterprise-Grade Deployment Capabilities
Selene’s integration with a next-generation card processing core and the issuer knowledge base also enables enterprise-grade deployment capabilities like:
1. Omnichannel servicing capability
Selene provides consistent support across voice and chat platforms
2. Embeddability
Selene can be natively embedded into any modern mobile/web app or industry-leading contact center platform, for example, AWS Connect, Genesys, NICE or others
3. Monitoring and analytics
Issuers can access detailed performance data of all Selene interactions for in-depth analysis
4. Compliance and security
Selene has built-in guardrails to ensure it addresses only the intended issues while adhering to industry compliance and security standards
Part 2: System Design
Selene's Architecture and Orchestration
Selene’s architecture enables dynamic cardholder experiences, continuous learning and agile deployment of new workflows
The digital interfaces enabling card support today have access to customer data but can’t act on it intelligently. This is especially true in an omnichannel context across chat, IVR, and live agents. And every new feature demands custom development, hampering agility and increasing costs, often across each channel. Experienced IT teams understand this complexity all too well.
Selene is responsive, extensible and scalable by design. While its modern, 4-layer architecture enables rapid addition of new intent flows, its integration with Zeta’s processing platform Tachyon allows swift deployment of new workflows. And its AI models support the ability to generate insights and take action without the need for specific code for each and can evolve independently to incorporate emerging capabilities across every relevant support channel.
Multi-Layer Intelligence Framework
Architecturally, Selene is composed of 4 distinct layers (Image 3):
A central conversation orchestration engine manages all conversations, whether voice or chat
Advanced AI models that understand and execute requests through
Intent Detection
Retrieval Augmented Generation
Code Generation
Other models including image recognition
Advanced toolkit to
Run code
Run Text-to-Speech, Automatic Speech Recognition
Execute function calling and others
Integration layer that connects to
Zeta’s card processing core for API calls and operations
The issuer document repository comprising its standard operating procedures, policies and knowledgebase
This architecture enables Selene to dynamically answer questions, generate insights, and execute actions without requiring specific code to be written for each.
Orchestrating Intelligent Insights and Actions
Let’s see how it works with an example (Image 4). The conversation orchestration engine receives the cardholder request to view their recent purchases by category, visualized in a pie chart. This triggers the foundational models for Intent Detection, Retrieval Augmented Generation, Code Generation, etc., allowing Selene to understand and execute the request being made.
Part 3: Building Trust
Banking-Grade Guardrails & Security
With advanced guardrails to prevent arbitrary responses or hallucinations, Selene uses issuer-specific knowledge and customized workflows to ensure accurate handling of queries.
The accuracy and integrity of information provided to cardholders is non-negotiable in card services: a single error can damage customer trust and issuer reputation. The risks are amplified with modern AI technologies; while Large Language Models (LLMs) offer powerful conversational capabilities, they can also generate convincing but incorrect information (aka hallucinations), make unauthorized decisions, or breach compliance guidelines. Selene’s built-in safeguards ensure that every interaction meets strict standards for safety, accuracy, and compliance.
While Large Language Models (LLMs) offer powerful conversational capabilities, they can also generate convincing but incorrect information (aka hallucinations), make unauthorized decisions, or breach compliance guidelines
Intelligence Trained on Issuer Knowledge
Selene builds its knowledge foundation exclusively from issuer-approved sources:
Detailed knowledge repositories
Standardized operating procedures
Approved workflow documents
Compliance guidelines
This focused training ensures Selene operates strictly within defined boundaries, eliminating the risk of unauthorized actions or incorrect information.
Multi-Layer Hallucination Prevention
Selene employs four critical components to prevent AI hallucinations:
1. Intent-focused action generator
Selene’s Action Generator validates user intent before directing conversations through pre-approved flows, keeping the risk of hallucinations near zero.
2. Enterprise-grade language model
Selene’s response generation system uses an enterprise-grade large language model independently verified for its accuracy and minimal hallucination risk.
3. Robust prompt engineering and guardrails
Selene’s comprehensive guardrails combine precise prompt engineering with strict boundary enforcement. Every response is validated in real-time against approved topics and functions, ensuring conversations remain focused and accurate.
4. Rigorous input management
Each interaction undergoes systematic validation through sophisticated anti-jailbreak protections to maintain robust security against potential system manipulations.
Quality Assurance
Selene maintains peak performance through continuous response monitoring and systematic scenario testing. Edge cases undergo careful evaluation, with immediate corrective action capabilities ensuring consistent accuracy. This proactive approach to quality control ensures Selene consistently meets the exacting standards of card issuers while delivering the fluid, natural interactions cardholders expect.
Regulatory Compliance
Selene is engineered to support card issuers’ compliance requirements. Every implementation adheres to critical banking and payments industry standards like:
1. Payment Card Security
Built to PCI-DSS specifications for card data handling
End-to-end encryption of sensitive information
Secure data transmission protocols
Controlled access to cardholder data
Compliant with operational security protocols
2. SOC 2 Type II certified infrastructure and processes
Regular security audits and assessments
Robust data privacy protocols
Secure cloud infrastructure
Its integration with the issuer processing platform ensures that it inherits the platform’s banking compliance framework and meets the data retention and privacy requirements.
Part 4: Processing Foundation
Role of Next-Gen Systems in Conversational AI
Delivering personalized and intelligent cardholder support demands a next-generation processing platform built for real-time data access and action across systems.
Since its first introduction in banking around a decade ago, AI-powered customer support has struggled with managing high expectations in terms of query resolution rates, response times, and productivity. The emergence of Generative AI has further fueled expectations, with success stories of early adopters now routinely making headlines6. However, for most issuers, the technical complexities of deploying and scaling advanced AI solutions with technical and operating debt due to their legacy systems remains a key challenge.
The ability of an AI-powered card solution like Selene to evolve and scale effectively depends on how the underlying card processing platform enables real-time data access and rapid feature updates.
The 4 Barriers to Conversational AI Adoption on Legacy Systems
Advanced AI requires instant access to card, account and transaction data through direct card processor integration. Legacy card processors, with their batch processing and rigid workflows, can’t support the real-time operations that a Conversational AI demands.
While legacy card systems have multiple constraints, the following 4 limitations critically impact AI-powered support performance.
1. Fragmented data and systems integration
Legacy systems store data in silos across multiple subsystems. This fragmentation makes it difficult for Conversational AI to aggregate information and generate coherent, context-aware responses. While middleware can consolidate data between systems, it adds significant costs and delays, creating bottlenecks that slow down AI implementation.
2. Outdated messaging protocols Leading to Reduced Interoperability
Legacy systems store data in silos across multiple subsystems. This fragmentation makes it difficult for Conversational AI to aggregate information and generate coherent, context-aware responses. While middleware can consolidate data between systems, it adds significant costs and delays, creating bottlenecks that slow down AI implementation.
3. Lack of Real-Time Event Handling
Legacy systems have limited real-time processing capabilities. This limits the AI’s ability to respond dynamically to state changes and user interactions, often resulting in delayed responses and outdated information.
4. Limited Availability of Purpose-Built APIs
Without comprehensive APIs, Conversational AI cannot deliver personalized support or automated workflows. It needs real-time access to cardholder data to provide tailored responses and alerts. Lacking these APIs, it can only offer generic answers, forcing users to rely on traditional support channels for anything beyond basic queries.
The 4 Next-Generation Processing Enablers of Conversational AI
Delivering AI-powered personalized, and conversational cardholder support demands a next-generation processing platform built for real-time data access and action across systems – such as Zeta’s Tachyon processing platform.
Tachyon delivers four key enablers for advanced Conversational AI solutions like Selene to deliver on their promise:
1. A comprehensive API catalog that supports every create, read, update operation in scope for the AI solution.
For example, Selene’s access to Tachyon’s extensive API library enables it to perform actions typically handled by agents, including payment processing, account freezing/unfreezing, transaction searches, and adding payment methods.
2. Real-time events stream that allow the AI to learn and adapt its actions and decisions.
Selene adapts continuously through real-time events and conversation context, enhancing decision-making at scale. For example, during payment conversations, Selene can display balances due after accounting for partial payments in real-time.
3. Open data stack for continuous supervised learning of the AI models on comprehensive user data available in structured data lakes.
Selene analyzes varied phrases used in real user conversations to request payments, such as ‘clear my balance’ or ‘settle my dues’ to boost its recognition of payment intents in natural language.
4. Hyper-personalization and configurability of the AI’s conversational flows, enabling unique and context-aware responses to cardholder needs.
For example, a cardholder can ask Selene to freeze a lost card in natural conversational style, eliminating the need to locate specific app functions.
Part 5: Path to Value
Framework for Conversational AI Adoption
Leveraging Conversational AI for card support represents a mature, adoption ready use case for issuers.
For issuers exploring use cases where AI can be introduced for tangible benefit, cardholder support is a clear winner. In its recent report on AI-enabled customer service, McKinsey highlights two key areas of operational impact7:
Enhanced self-service automation with contextual in-app nudges and controls (~50-80% contacts automated), and
Reduction in call volumes escalated to human agents by installing Conversational AI at each entry point (>80% of all actions automated)
Zeta adopts a similar framework to drive improved operational outcomes for issuers.
1. Improved effectiveness of in-app self-service
Next-gen capabilities like granular card and transaction controls and advanced security features can reduce dependence on live agent card support by up to 75%8
2. Automation of routine queries
Selene’s natural language interfaces across chat and voice can automate 20% of the remaining 25% calls, ensuring that only the most complex and critical calls are escalated to live human agents
3. Enhanced operational efficiencies
Selene leverages cloud-native infrastructure to enable flexible processing capacity management, 24/7 availability and 99.9% uptime with faster response times and higher accuracy
However, realizing value with a Conversational AI assistant for card support requires a structured framework (Image 5) that evaluates key considerations along 4 critical dimensions:
Business Considerations
Operational Readiness
Compliance
Technological
The following section elaborates the essential considerations under each dimension.
Essential considerations under each dimension
1. Business Considerations
Objective Definition: Prioritize business objectives, e.g. reducing customer service costs, enhancing user satisfaction, or improving resolution times
Customer Expectations: Identify customer needs and preferences for personalization, quick resolutions, and accessible support across multiple channels
Competitive Analysis: Benchmark capabilities of Conversational AI solutions offered by leading issuers to ensure competitiveness of your solution
Expected Outcomes: Quantify expected outcomes. e.g. reduced agent-handled queries, faster response times, and higher customer satisfaction ratings
2. Operational Readiness
Scope: Ring fence the scope of service that your Conversational AI can provide. e.g.
Information like balances, statements, or billing cycle
Financial transactions like repayments
Advanced insights like spend categorization
Omnichannel Service Delivery Support: Evaluate what is needed for the assistant to be seamlessly accessible via the mobile app, website, and IVR, catering to different customer preferences.
AI Agent - Human Agent Handoff: Implement a smooth handoff process to human agents when queries go beyond the scope of the AI agent, with the context preserved in transitions.
Continuous Feedback Loop: Integrate performance analytics and customer feedback to iteratively improve the assistant’s responses and expand its capabilities over time.
3. Compliance and Security
Data Privacy Compliance: Adhere to CARD Act, CCPA, and other regulations for customer data, ensuring consent and robust data encryption.
Banking-Grade Guardrails: Prevent unauthorized actions or responses by limiting the AI’s access strictly to predefined, bank-approved workflows and knowledge sources.
Security and Fraud Prevention: Employ strict anti-fraud measures, including transaction verification, and ensure the Conversational AI detects any signs of suspicious activity.
Hallucination Prevention: Integrate multiple layers of control to ensure accurate responses, such as intent-focused action generators, input validation, and anti-jailbreak protections to maintain reliable, secure interactions.
4. Technology & Infrastructure
Next-Gen Card Processing Platform: Leverage a cloud native, real-time card processing platform to handle API requests, track transactions, execute payments, and fetch details to ensure quick and accurate responses.
Comprehensive API Catalog: Ensure the availability of an extensive API library to support all types of customer interactions, from simple queries to complex requests like payment processing and account management.
Scalability and Resilience: Design the architecture to scale on demand, with cloud-native infrastructure capable of handling high volumes and concurrency without degradation in response time.
The successful implementation of an advanced Conversational AI like Selene for card support represents a significant leap forward in modern banking services. By following this comprehensive framework - encompassing business considerations, operational readiness, compliance protocols, and technological infrastructure - financial institutions can deliver a solution that not only meets current customer needs but also scales for future demands.
Next Steps
Launch Intelligent Card Assistants with Selene
Accelerate AI adoption by launching banking-grade intelligent assistants for your card programs.
Conversation Model: Component that produces natural language responses
based on processed data and context. This is an enterprise-grade large
language model verified for accuracy and performance.
Intent Detection Model: Component that processes user inputs to
determine the requested action or query type
Attribute Detection Model: Component that identifies specific entities
within user inputs to enable accurate request processing
Function Calling Model: Component that selects appropriate API calls
and system functions from a predefined API catalog based on user
request
Code Generation Model: Component that generates code for data
processing and analysis tasks
Conversation Orchestration Engine: System that coordinates the
sequence of interactions across AI models andcomponents
Retrieval Augmented Generation (RAG): Framework that enhances AI
responses by incorporating relevant information from issuer knowledge
bases
Technical Capabilities
Advanced Speech Recognition (ASR): System for accurate interpretation of voice commands and spoken queries
Text-to-Speech (TTS): Technology that converts text responses into natural-sounding voice output
Anti-jailbreak Protections: Security measures preventing manipulation of AI responses or unauthorized access
Content Verification Systems: Controls ensuring responses align with approved topics and issuer guidelines