Industry
Capital Markets
Unlocking Exponential Future Growth in Capital Market Firms
Banking
Banking Services Transformation: Unlocking Exponential Value
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TCS BaNCS™
TCS BaNCS™: Enabling Transformation in Banks and Financial Institutions
Highlights
- The past decade has witnessed a steady evolution in payment processing infrastructure, but nothing has shown more transformative potential than recent advances in Generative AI (GenAI) and Agentic AI technologies.
- Early adopters aren’t just reducing operating costs marginally—they are attempting to completely redefine the art-of-the possible. They are processing payments in seconds that previously took days, dramatically reducing fraud losses.
- This article shares insights from the impacts that AI-led evolution can have on the payment processing value chain.
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Overview
Enterprise payment hubs (EPH) stand as the unsung heroes of the financial ecosystem—silently orchestrating millions of transactions daily, from your morning coffee purchase to billion-dollar corporate transfers. These sophisticated networks have evolved to handle everything from instant domestic payments to complex cross-border transactions navigating multiple regulatory jurisdictions. Despite their mission-critical-like importance, many institutions continue struggling with outdated systems plagued by manual interventions and operational bottlenecks.
The past decade has witnessed a steady evolution in payment processing infrastructure, but nothing has shown more transformative potential than recent advances in GenAI and Agentic AI technologies. Through extensive research and direct conversations with industry leaders and operations teams, it is becoming clearer that these innovations aren’t merely improving payment systems incrementally—they have the momentum to fundamentally reimagine how an EPH functions.
For financial institutions, the implications are profound. Early adopters aren’t just reducing operating costs marginally—they are attempting to completely redefine the art-of-the possible. They are processing payments in seconds that previously took days, dramatically reducing fraud losses, and creating payment experiences that generate genuine customer engagement.
This also ripples into practical implementation strategies and a realistic assessment of the challenges institutions would face on the frontlines of payment transformation.
Evolving Landscape
The Evolving Landscape of Enterprise Payment Hubs
Current state and persistent challenges.
Despite billions invested in digital transformation, many payment hub landscapes remain patchworks of legacy systems connected by custom integrations and manual processes. These environments must contend with escalating complexity of:
- Real-time payments (RTPs) requiring 24/7/365 processing with zero tolerance for delay or error.
- Cross-border payments navigating correspondent banking networks, currency conversions, and conflicting regulations.
- Domestic payment networks (ACH, wire transfers) each with unique formats, settlement windows, and limitations.
Traditional payment hubs operate through interconnected modules handling multiple acquisition channels, validation checks, compliance screening, format translation, exception handling, settlement processing, account posting, and reconciliation. Despite technological advancements, persistent challenges include:
- Manual interventions: Exceptions and complex cases requiring human review.
- Compliance complexity: Evolving regulations demanding constant updates.
- Format fragmentation: Multiple messaging standards and protocols.
- Operational inefficiencies: Redundant checks and delayed processing.
- Customer experience gaps: Limited personalization and transparency.
AI promise
The transformative promise of GenAI and Agentic AI.
GenAI and Agentic AI represent paradigm shifts in how payment systems can operate:
GenAI employs large language models (LLMs) and other generative approaches to process unstructured data and natural language, generate content, code, and structured outputs, learn patterns from vast datasets and adapt to new scenarios without explicit programming.
Generative AI and Agentic systems aren’t just another incremental improvement in payment technology —they represent a fundamental shift in how payment hubs function.
Agentic AI builds on these capabilities by taking autonomous actions based on objectives, coordinating between multiple systems and processes, making decisions with limited human intervention, planning and executing multi-step workflows.
Together, these technologies can transform payment hubs from static, rules-based systems into dynamic, intelligent networks capable of learning, adapting, and operating with unprecedented autonomy.
Implementation
Implementation of ideas to revolutionize payment hubs.
Traditional challenge: Most institutions rely on static routing rules defined months or years ago. A payment to Germany always follows the same path, regardless of network congestion, temporary outages, or fluctuating costs—resulting in unnecessary delays, higher fees, and frustrated customers.
AI-powered solution: GenAI and Agentic systems enable intelligent routing that works more like an experienced navigator, tracking real-time conditions across SWIFT, SEPA, Ripple, and domestic networks, calculating optimal paths considering speed, cost, reliability, and customer priorities and anticipating problems before they happen, such as rerouting payments that would otherwise get stuck in end-of-day processing windows.
Implementation idea: Imagine this: you’ve got a “smart router” working behind the scenes, always keeping an eye on the web of correspondent banks and processing options. One day, a big payment from Singapore to Mexico is about to hit a snag—delay at the main route. No problem! The system instantly finds a quicker alternative, reroutes the payment, and saves nearly eight hours. The customer? They’re pleasantly surprised to see the money arrive sooner than expected, blissfully unaware there was ever a hiccup in the process.
Traditional challenge: Rule-based compliance systems generate high false-positive rates, requiring extensive manual review and creating processing delays.
AI-powered solution would perform contextual sanctions screening that understands transaction intent and context, with entity resolution that distinguishes between similar names and identifiers. The solution performs anomaly detection identifying unusual patterns without explicit rules and goes onto identify regulatory change monitoring which automatically updates compliance parameters
Implementation idea: A GenAI system analyzes transaction narratives, beneficiary information, and historical patterns to determine the true purpose and risk profile of payments. The system can distinguish between a legitimate payment to “John Smith” and a sanctioned individual with the same name by analyzing contextual information—reducing false positives while maintaining compliance effectiveness.
Traditional challenge: Exception handling remains largely manual, with specialized staff reviewing and resolving issues like formatting errors, missing information, or compliance flags.
AI-powered solution can perform an Intelligent classification of exceptions by type, severity, and resolution approach, provide automated correction of common formatting and data issues, predict resolution suggesting likely successful resolution paths and self-healing workflows learning from past resolutions.
Picture this: the agentic system spots a payment missing beneficiary account details. Instead of kicking it over to a human for manual review, it instantly scans past transactions for similar patterns, pulls the most likely account info from trusted sources, double checks it, and resubmits the payment on its own. If it comes across a trickier exception, it doesn’t just throw up its hands. Instead, it puts together a set of recommended solutions, complete with supporting evidence, so that when a person steps in, they know exactly what to do. Smooth, seamless, and efficient.
Traditional challenge: Payment systems must translate between numerous messaging formats (ISO 20022, SWIFT MT, proprietary formats), often requiring custom mapping rules and data enrichments across the formats.
AI-powered solution does adaptive format translation learning mapping patterns without explicit rules, carries out semantic understanding of payment data across different schemas. This also performs automatic adaptation to format changes and new standards while carrying out Field enrichment completing missing information based on context. When a new field pops up or a format changes, it intuitively understands what needs to be done—figuring out the right mapping based on the meaning behind the data. No more scrambling to update manual mappings or worrying about translation errors. The result? You slash those format mishaps by 90%— and your team can finally focus on what really matters.
Traditional challenge: Traditional fraud detection systems rely on static rules and threshold-based triggers that struggle to identify novel fraud patterns.
AI-powered solution does behavior-based anomaly detection understanding normal patterns for each customer. Identifies cross-channel correlation, social network analysis connecting activities across multiple touchpoints and builds upon adaptive risk scoring evolving with changing fraud tactics.
Imagine this: Your fraud detection doesn’t just sit in the background crunching numbers—it’s actively learning and adapting to your customers’ transaction habits across every channel. Instead of relying on outdated, rigid rules, this intelligent system spots even the most subtle anomalies, flagging only what truly matters. The moment it detects something suspicious, it can automatically send an alert for review, temporarily adjust transaction limits, or prompt for extra authentication—all while providing clear explanations to your compliance team. The result? Proactive protection, less noise, and more confidence that you’re staying one step ahead.
Traditional challenge: Most institutions treat payment data as merely transactional information to be processed and forgotten. Valuable intelligence remains buried in unstructured fields, remittance information goes unutilized, and payment patterns that could predict customer needs go unnoticed.
AI-powered solution:
- Contextual understanding of unstructured data: GenAI systems parsing free-text payment descriptions to extract invoice numbers, contract references, and project codes with remarkable accuracy.
- Entity resolution and enhancement: AI identifying that “Acme GmbH,” “Acme Germany,” and “ACME-DE” represents the same entity, then automatically standardizing and enriching these records.
- Predictive working capital insights: Analyzing payment timing patterns, seasonal variations, and counterparty behaviors to predict cash flow with unprecedented precision.
- Supply chain intelligence: Mapping entire supply networks from payment flows, identifying concentration risks and suggesting alternative suppliers before disruptions occur.
Imagine putting GenAI to work as your behind-the-scenes payments expert— quietly studying your clients’ historical payment data and learning the quirks of every vendor’s reference formats. With this “translation layer” in place, reconciling incoming payments to the right invoices suddenly becomes automatic, even when suppliers change their formats or new vendors are added to the mix. The result? Fewer reconciliation headaches, faster cash application, and a much smoother process overall.
Challenges
Challenges for financial institutions.
AI transformation isn’t primarily a technology problem—it’s a people and process challenge with a technology component.
Organizations that fail to recognize this typically achieve minimal benefits despite substantial investments.
AI readiness assessment: Be brutally honest.
Before initiating implementation, institutions must honestly evaluate their readiness:
- Data readiness: Banks have to know where their data resides, its currency, its quality and in what shape or formats.
- Technical infrastructure: Multiple considerations here for e.g. the AI models in use, the API ecosystem maturity, the infrastructure to host these models, the related costs and the speed of the AI engine.
Governance and risk management.
AI systems in payment processing require robust governance frameworks:
- AI risk assessment: For model governance, banks must invest in model risk evaluations, bias detection and mitigation, and security measures.
- Operational controls: Design and frame the human oversight mechanism, fallback procedures in case of AI system failures, audit trails for the AI-driven decisions.
- Regulatory compliance: Explainability, especially in case of automated decisions, is a key requirement. Many countries are evolving on the regulatory reporting for the AI systems in use.
Change management and skill development.
Successful implementation requires organizational transformation:
Workforce evolution: Creation of a human-machine collaborative environments, redefining the operational roles as well as upskilling the payment operations team. This will involve redesigning workflows and new performance metrices
Ethical considerations
As AI systems become more autonomous in payment processing, institutions must address the following challenges around Ethical practices:
- Algorithmic fairness to ensure equitable access to payment services across demographics, while preventing inadvertent discrimination in risk scoring or maintaining transparency in payment routing decisions
- Human oversight and accountability through responsibility frameworks for AI decisions, determining appropriate levels of human intervention and creating explainable AI systems for regulatory review
- Privacy and sovereignty: As this intelligence is based on past data, balancing personalization with privacy concerns, addressing cross border data sovereignty issues and implementing privacy-preserving AI techniques would help gain customer and regulatory trust.
AI Payments Future
The future of intrinsic AI payment hubs.
The intelligence economy: Data enrichment as the new currency.
We’re entering what could be called the ‘intelligence economy’—an era where insights derived from payment lows often deliver more value than the payment processing itself. This shift has profound implications:
Sovereign payments360: New opportunities exist for ‘payments intelligence vaults'—secure environments where payment data can be enriched, analyzed, and converted to actionable insights while maintaining strict compliance with privacy regulations.
Intelligent ecosystem networks: The next frontier involves collaborative intelligence networks enriching payment data across institutional boundaries while preserving privacy.
Early implementations using federated learning and zero-knowledge proofs allow banks to collectively detect fraud patterns without sharing raw transaction data.
From reactive to just-in-time treasury services: Treasury services are transitioning from solely processing payments to managing financial lows using predictive intelligence. This shift includes supporting clients within their supply chains and may serve as a distinguishing feature in the industry. EPH should predict supplier payment needs before invoices are issued, optimize payment timing to maximize float or capture early payment discounts, and dynamically allocate working capital based on continuously enriched payment intelligence.
The orchestration challenge: Data enrichment in a payments lifecycle is not as an add-on feature. This is the orchestrating force for their entire payment ecosystem. Next-gen architectures place intelligence at the center, with transaction processing as just one of many services feeding into and consuming from this central intelligence layer, representing perhaps the most significant architectural shift in payments since the introduction of the hub model itself.
Path Forward
The window for gaining competitive advantage from AI in payments is closing faster than most executives realize.
Leaders that began their AI implementations 18-24 months ago are already seeing dramatic results. The middle of the pack is scrambling to catch up. The laggards are still debating whether this is hype while their customers gradually migrate to more innovative providers.
GenAI and Agentic systems aren’t just another incremental improvement in payment technology —they represent a fundamental shift in how payment hubs function.
The benefits are too substantial to ignore. The most successful institutions will approach this transformation as a strategic journey rather than a tactical technology implementation.
As payment ecosystems continue to evolve, GenAI and Agentic AI will transition from competitive advantages to table stakes. Financial institutions beginning this journey today will be best positioned to lead the payments industry of tomorrow.