Article | September 24, 2026
AI in banking has entered a more consequential phase.
Banks have used models to spot fraud, forecast risk and support service teams. Now, agentic AI can gather evidence, recommend actions, trigger workflows and coordinate tasks across systems.
Sure, AI can make a decision, but can the bank prove why that decision deserves trust?
For boards and executive teams, “trustworthy AI” is an abstract agenda item. However, in practice, it’s far less theoretical.
A decision must be based on the right data. The data must be current, permitted for that use and traceable. The model or agent must operate within an agreed policy. Its output must be reviewed at the appropriate level of seniority. And the bank must be able to reconstruct what happened after the event.
In credit, it might mean knowing which customer data and exception rules influenced a recommendation. In payments, it might mean showing why a transaction was blocked, delayed or escalated.
Executives don’t need to inspect the algorithm, but they need to be confident that the institution can explain, govern and defend the outcome, a core requirement of explainable AI banking.
Many banks still run critical processes across aging core platforms, acquired systems, local workflows and manual reconciliation. People compensate for those fractures every day. They know which system is more reliable, which screen to check and which spreadsheet contains the practical truth.
AI doesn’t have that institutional instinct. If systems disagree, an agent may stall, escalate too often or produce a confident answer from weak foundations. That’s why agentic AI will force banks to address data quality, system integration, consistent process ownership and clear accountability.
Banking leaders should resist the false choice between full automation and manual control. The better model is tiered responsibility.
AI can assemble the evidence, check completeness, apply policy, highlight anomalies, and propose next steps. Meanwhile, people should focus on ambiguity, materiality, customer impact and accountability.
Straightforward cases may become increasingly automated over time. Complex cases should reach a human faster, with better information and less administrative drag.
The practical test is simple: does AI help the right person make a better decision sooner?
A bank’s AI environment won’t stand still. Models will improve, vendors will change, regulations will evolve and new threats will emerge. A decision made today may need to be re-examined years from now.
The bank needs a record of the:
That discipline separates executive-grade AI from experimentation and creates the foundation for trusted explainable AI banking environments. Regulators, customers and internal audit functions will expect evidence that the bank understood the risk, applied appropriate controls and acted consistently.
The most sensible use cases are high-value, evidence-heavy and bounded. Loan file preparation, fraud case triage, know-your-customer reviews, customer complaint routing, treasury forecasting and regulatory reporting all fit that pattern.
These are areas where AI can improve speed and uniformity, but without immediately granting irreversible authority, making them ideal use cases for explainable AI in banking. They also create quantifiable effects: shorter cycle times, fewer manual checks, improved customer experience, better audit trails and less risky operations. There are already instructive examples.
One large banking group digitized check-clearing processes, reduced check-clearing costs by 50%, cut clearance cycle time by 66%, processed about 200,000 checks in 2 hours at 99.9% accuracy and used automated fraud detection to identify an average of 350 fraudulent checks a month, avoiding more than £50 million in potential fraud losses each year.
A major Spanish bank is using AI-driven accessibility testing and modernization to reduce user drop-off, improve satisfaction among 12 million customers and deliver 350,000 hours of advanced accessibility testing each year.
It’s not about automating every banking process. It’s just that better data, more-refined orchestration and increasingly credible assurance can produce outcomes that customers, employees and regulators can recognize.
Leaders should treat agentic AI as a business operating model rather than a technology feature, while ensuring agent explainability in banking operations remains embedded in governance and decision-making processes:
This space is where DXC Technology and banks work most effectively together: modernizing core and customer platforms, improving data foundations, embedding automation into business processes and creating audit trails for regulated AI.
DXC provided MLOps services for a European financial services customer. A key challenge was to demonstrate a clear audit trail from model development through deployment and operation. It also developed AI solutions for email processing and GenAI chatbots for complex financial product documentation.
DXC OASIS extends this thinking into mission-critical IT operations. OASIS is designed as a governed, secure orchestration layer across existing technology estates, combining human expertise with agentic AI, so that agents handle volume while people apply judgment. Its operating model stresses unified visibility, traceable action and role-aware access controls, along with progressive autonomy that can be expanded or restricted as confidence grows.
That’s the secure path forward.
So, before banks let AI move more money, they must make every AI-assisted decision easier to understand, govern and defend. Autonomy can come later.
Explainability has to come first.
