How AI and machine learning are reshaping core banking: Lessons from TCS and Khan Bank

In 2025, core banking transformation is increasingly defined not by system upgrades alone, but by the integration of real-time intelligence, predictive automation, and personalized services. At the heart of this shift is the adoption of artificial intelligence (AI) and machine learning (ML) within the foundational layers of banking infrastructure. Tata Consultancy Services (TCS), through its flagship platform TCS BaNCS, is demonstrating what this evolution looks like in practice. Its ongoing collaboration with Mongolia‘s Khan Bank is now one of the most visible examples of AI-enabled core banking in an emerging market environment.

The decision by Khan Bank to deepen its engagement with TCS and migrate to a modernized, AI-integrated version of the BaNCS platform illustrates how financial institutions are positioning for long-term digital resilience. With over 2.9 million customers and 548 branches, Khan Bank’s challenge was not just modernization—it was how to do so in a way that embraced intelligent automation, reduced operational friction, and aligned with global compliance frameworks. The answer, increasingly, lies in embedding AI directly into the core banking stack.

How Is AI Transforming Core Banking in 2025?

Across global markets, AI in banking is shifting from peripheral experimentation to enterprise integration. Once limited to customer-facing interfaces such as chatbots or robo-advisors, AI is now being deployed within core systems—those that manage accounts, risk, transactions, and compliance. This transformation is being driven by rising expectations for real-time banking services, increased regulatory complexity, and intensifying competition from fintech disruptors.

TCS BaNCS enables this transition by offering embedded AI and ML tools across its modules. This includes AI-powered fraud detection systems that learn over time, ML-based credit decisioning frameworks that consider dynamic borrower profiles, and real-time product recommendation engines that drive hyper-personalized financial engagement. These tools are not external overlays—they are integrated into the platform’s core data fabric, which significantly shortens response cycles and enables banks to deploy intelligent services natively.

What AI Capabilities Does TCS BaNCS Deliver?

The TCS BaNCS Global Banking Platform provides AI capabilities across a range of business functions. In risk and compliance, for example, BaNCS supports adaptive learning algorithms that detect anomalies in transaction behavior, alerting banks to suspicious activity with greater accuracy than traditional rule-based systems. This enables fraud prevention in real time while reducing false positives—a long-standing challenge in legacy environments.

In credit operations, machine learning models in BaNCS allow banks to go beyond static credit scoring. Instead, they can incorporate variables such as digital footprint, repayment behavior, and macroeconomic data to arrive at more nuanced lending decisions. This improves both approval rates and portfolio performance, especially in markets like Mongolia, where formal credit histories may be limited.

Customer experience is another focal area. AI tools within BaNCS can segment users based on real-time engagement, past transaction patterns, and financial goals. Banks can then deliver personalized offers, contextual nudges, and pre-approved products—all orchestrated from within the core platform. At Khan Bank, this capability aligns closely with its digital banking strategy aimed at reaching underserved and digitally native populations.

Why Is AI Integration Crucial for Emerging Markets?

The strategic relevance of AI within core banking is especially acute in emerging markets. Banks often operate in hybrid environments that mix high-tech digital channels with legacy back ends and physical branches. They also face the challenge of extending services across geographies with poor infrastructure or fragmented data availability.

For Khan Bank, the adoption of an AI-ready BaNCS deployment supports its goal of delivering fully digital services across Mongolia’s rural and urban population. AI allows for contextual onboarding, biometric verification, and multilingual engagement—features critical for customer inclusion. Moreover, AI-powered core systems reduce the need for manual intervention in routine processes, lowering costs and freeing up resources for higher-value functions.

This approach also helps meet the regulatory expectations of international partners and investors. As global scrutiny of AML (Anti-Money Laundering) and KYC (Know Your Customer) frameworks grows, the ability to demonstrate explainable, auditable, AI-driven decisions becomes essential. BaNCS addresses this need by embedding governance features and model transparency within its AI modules.

What Are the Broader Market Trends Supporting AI in Core Banking?

According to Accenture, over 70% of global banking executives expect AI to be a key differentiator in core transformation projects over the next five years. IDC forecasts that by 2026, 80% of banks will have launched AI-infused modernization initiatives to streamline operations and lower risk.

TCS BaNCS is strategically positioned to capitalize on this trend. Unlike many competitors that rely on third-party AI integrations, BaNCS embeds AI services across its retail, corporate, treasury, and payment modules. Its containerized deployment model supports flexible AI workload orchestration, allowing banks to scale usage based on operational needs and regional constraints.

This AI-centric approach is gaining traction not only in Asia but also in the Middle East, Africa, and parts of Eastern Europe, where many institutions are leapfrogging legacy systems entirely. In these regions, BaNCS is often selected for its localized compliance support, multilingual capabilities, and end-to-end transformation roadmap.

How Does TCS BaNCS Compare with Competing Platforms?

The core banking software market includes high-profile competitors such as Temenos Transact, Oracle FLEXCUBE, Infosys Finacle, and Thought Machine Vault. Each platform offers varying levels of modularity and AI capabilities. Temenos has invested in explainable AI models for lending. Infosys Finacle provides AI-based customer engagement tools and has introduced sandbox testing environments for banks experimenting with intelligent workflows. Thought Machine, a newer entrant, markets itself as cloud-native and offers smart contract-based banking logic.

However, TCS BaNCS differentiates itself on three key fronts: deep integration of AI across modules, multi-market scalability backed by Tata Consultancy Services’ global delivery model, and robust experience in regulatory-heavy environments. With over 30% of the world’s banking transactions running on BaNCS infrastructure, the platform has matured through real-world performance across a spectrum of operational scales.

Additionally, BaNCS’ ability to deliver both greenfield digital bank launches and brownfield legacy migrations provides banks with a low-risk pathway to AI readiness. TCS’ wider consulting and managed services offerings ensure end-to-end project alignment—from API orchestration and cloud integration to security and compliance.

What Is the Institutional Sentiment Around TCS and BaNCS?

From an investor standpoint, TCS’ platform-based offerings like BaNCS are viewed as strategic long-term growth levers. While TCS’ core IT services remain the primary revenue drivers, the company has consistently signaled its intent to scale up platform revenues. In recent quarterly earnings calls, TCS executives highlighted growing interest in BaNCS from mid-sized banks in Africa and Asia as well as government financial entities modernizing payment infrastructure.

The institutional sentiment is broadly positive, particularly given BaNCS’ annuity-based pricing model, which generates stable revenue streams. Analysts tracking TCS stock (BSE: 532540, NSE: TCS) have pointed to the platform’s relevance in IP-led growth narratives and margin protection strategies amid global IT service commoditization. BaNCS deployments also act as anchors for long-term service contracts, increasing client stickiness and wallet share.

What Is the Outlook for AI-Driven Core Banking in the Next Five Years?

AI is expected to become an inseparable component of core banking platforms by 2030. The next wave of evolution will involve generative AI, agentic automation, and embedded compliance monitoring. Platforms like TCS BaNCS are already laying the groundwork by enabling streaming analytics, composable architecture, and federated learning environments.

For banks, the benefits will be transformative. They will be able to offer real-time financial wellness tools, deploy credit decisioning on mobile-first platforms, and automate regulatory reporting—all within the core system. Banks that adopt platforms like BaNCS now are likely to outperform peers in product velocity, operational efficiency, and customer retention.

Banking Intelligence Starts at the Core

The integration of AI and machine learning within TCS BaNCS is redefining what core banking means in a digital-first world. It is no longer just a ledger system—it is an intelligent engine for innovation, compliance, and personalized finance. The case of Khan Bank underscores how even in frontier markets, strategic investment in AI-ready infrastructure can unlock scale, trust, and growth.

As global banks look to decouple innovation from risk, and regulators demand greater transparency in AI usage, platforms like TCS BaNCS will continue to set benchmarks. They represent not just technological maturity, but strategic foresight in a financial landscape being shaped by data, speed, and intelligence.