Banking executives have rushed from asking whether artificial intelligence works to demanding that it work at enterprise scale. Early trials quickly proved the technology’s viability, but the journey from sandbox to production has stalled. According to the 2025 Evident AI Index, half of the world’s 50 largest banks report active AI use, yet only seven can forecast returns and merely four have documented actual ROI. The chasm, says Monu Kurien Mathew, senior vice-president of Business Solutions at HCLTech, is not a shortage of models but the weight of legacy data, outdated architecture, strict governance, and the need for a new operating model.
HCLTech, a $14.7 billion technology firm, is positioning itself as the partner that can bridge this divide. Its vision of the next wave—autonomous banking—places AI at the core of customer interaction, back-office processes, engineering, and compliance. Rather than tacking AI on top of existing systems, the company embeds domain-specific models, private AI capacity, and built-in human accountability into the bank’s architecture from day one.
Why generic AI cannot power regulated finance
In regulated sectors, getting the answer “mostly right” is insufficient. A single hallucinated figure in a credit memo or a mis-classified transaction can trigger a compliance incident, not just a quality flaw. Generic foundation models are trained on broad internet data and lack the contextual awareness required for decisions that involve policy, regulation, and decades of institutional judgment. Even with rigorous prompt-engineering, the risk of erroneous output remains.
Financial institutions therefore need AI that can interpret decisions, not merely converse. The trick lies in fusing a model with the bank’s proprietary data, policy libraries, and workflow logic. At production scale, a right-sized domain model often beats a massive, generic model in terms of cost, latency, and accuracy, because it is tailored to a specific task such as credit underwriting or sanctions screening.
Domain-specific AI where it matters most
The sweet spot for industry-tailored intelligence is where expertise, regulation, and operational complexity intersect. Lending and credit operations—underwriting, covenant monitoring, collections—are prime candidates. Here, AI can move from assistance to execution, drafting credit memos, flagging risky covenants, and even initiating collection actions.
Financial crime detection presents another high-impact arena. Domain-tuned models sharpen KYC and AML triage, dramatically reducing false-positive alerts that drown compliance teams. The same principle applies to trade finance, insurance claim handling, and wealth-management advice, where a more accurate decision translates directly into revenue or risk mitigation.
Equally important, yet often overlooked, is the middle- and back-office workload: reconciliations, settlement, and servicing. Automating these repetitive processes can swing the profit-and-loss line, and HCLTech predicts the next AI surge will target operations and risk management, turning routine tasks into self-resolving workflows.
Private AI infrastructure as a banking imperative
Bank data is a high-value asset bound by strict compliance, security, and residency mandates. When AI becomes part of critical infrastructure—sitting alongside core banking, payments, and risk platforms—the location and control of computation become strategic issues. Private AI capacity ensures that sensitive datasets never leave the jurisdictional boundaries set by regulators.
Beyond compliance, sovereignty extends to the intellectual property embedded in finetuned models, policy encodings, and customer-specific reasoning. Controlling this knowledge safeguards competitive advantage and reduces reliance on third-party providers. Economic considerations also surface at scale: token-based pricing of public models can explode as transaction volumes grow, whereas private clusters deliver predictable cost structures for high-volume, repeatable workloads.
Choosing between public, private, or hybrid deployments is not a binary decision. HCLTech advises banks to map each workload to its risk tier, data sensitivity, and latency requirements, allocating the most appropriate environment for each use case.
HCLTech’s industry-AI portfolio fuels cross-sector adoption
On October 8, HCLTech unveiled a suite of 20 Industry AI Solutions spanning banking, healthcare, life sciences, aerospace, retail, telecom, mobility, energy, and manufacturing. The portfolio comprises tools already running at scale for global clients, alongside early-stage pilots. In banking, the firm offers autonomous accounts-payable automation, AI-driven underwriting assistants, and a regulatory-compliance engine that translates policy updates into actionable model adjustments.
Outside finance, the VisionX platform employs computer-vision to cut warehouse response times by more than 90 % and slash operating costs by 70 %. The SmartTwin system enables manufacturers to simulate products and plants before physical rollout, accelerating go-to-market timelines by roughly 44 %. In life sciences, the Intelligent Safety Platform shortens pharmacovigilance case processing by 40-50 %, illustrating how domain-specific AI can tighten regulatory cycles.
These solutions illustrate HCLTech’s broader thesis: deep domain expertise, combined with scalable AI, bridges the gap between experimental pilots and measurable business outcomes. As the company prepares to release its Q2FY27 financial results on October 12, the emphasis remains on turning AI promise into profit, particularly for banks eager to evolve into truly autonomous institutions.
