Banks are deploying AI across critical operations faster than ever, embedding it into strategic decision-making processes. Some of the largest consumer banks in the United States JPMorgan Chase, Bank of America, and Wells Fargo have spent years and millions of dollars building custom-designed AI systems, backed by dedicated teams of engineers, data scientists, and compliance professionals often working in tandem with outside providers to ensure control and oversight.
The work is proceeding at such a pace and scale that there is a real risk people inside the bank do not fully understand how these systems work. This creates immediate concerns: can banks explain how AI decisions are made? Will regulators accept outcomes from outside systems they did not build or fully audit? And when something breaks or when the decision-making process cannot be explained, will there be clear accountability?
What explainability means and why it matters
At the center of all this is the question of explainability: the ability to show how and why a model produced a given output, in a way that stakeholders can understand, regulators can trust, and executives can act on.
Without it, AI becomes a “black box”: data is processed, and results are produced, but the underlying logic remains inaccessible. Opaque models create operational risk, introducing a critical liability in regulated environments. Risk officers, compliance teams, and even relationship managers need to understand how decisions are made, not just trust that the system gets it right.
Explainability cannot be an afterthought bolted onto a model after the fact. It must be embedded from the start to ensure compliance but also scalability and AI is used across the organization. This aligns with regulatory expectations such as the EU AI Act while building confidence among employees and enhancing customer outcomes by ensuring fairness and consistency.
Explainability and human oversight are both required
A common refrain is that AI systems need to keep “a human in the loop.” The phrase is reassuring and gets repeated often, but it often blurs two distinct needs. Explainability is a technical requirement: systems must generate outputs that reveal why a decision was made. Human oversight is a governance requirement: people must supervise, interrogate, and intervene at key points in the AI lifecycle. The two work together — explainability provides the insights and humans consume them to ensure accountability. Effective workflows and architecture must combine transparency, human oversight, and explainability in a way that scales without slowing the value of AI.
Agentic tools and layers of explainability
Newer agentic tools advance this approach by generating natural language “chain of thought” that reveals step-by-step reasoning, making it easier to understand why decisions are being made. This approach eliminates reliance on cryptic scores or static dashboards; users can now interrogate models using natural language, drilling into the logic, testing assumptions, and understanding outcomes in context.
Banks also have access to multiple layers of explainability. For example, large language models can understand the metadata structure of a bank’s technology stack and then provide analysis and recommendations based on what they find – revealing opportunities in areas such as traceability, audit trails and versioning. Natural language interfaces mean they are easier to probe and analyze outputs. Large language models will soon support compliance, serving as first-line compliance reviewers that help organizations keep pace with changing regulations, ensuring consistency across documents and generating document drafts that are nearly ready for external use.
Predictive vs. deterministic
One key distinction in designing these systems is whether a model behaves predictively or deterministically.
Predictive systems rely on statistical inference, they guess based on what’s likely, not what’s certain. That makes them very valuable, but it also makes them harder to audit. Even sophisticated models can stumble on something basic, like counting the number of R’s in “strawberry.” They estimate based on probability rather than performing an exact count.
Deterministic systems, by contrast, follow defined logic. They’re easier to trace, and they produce consistent, auditable outcomes.
For banks, deterministic systems must be the bedrock. Predictive systems should only be used when deterministic systems cannot meet the requirement. When used, banks must provide clear justification for why they are needed and establish deterministic guardrails around them, clear policies, fixed review points, and explainable thresholds to maintain accountability.
Human oversight meets scalable AI
Zafin’s AI + Human Workflow is designed to ensure explainability, scalability, and control. Banks can monitor, approve, and audit AI outputs in line with internal governance frameworks, while retaining full access to data and metadata through our open, modular architecture. This enables transparent orchestration between human and AI decision-making, a critical requirement for secure and scalable adoption.
Beyond critical workflows, Zafin addresses explainability on three levels:
- Embedding transparency into every AI model
- Designing effective and efficient collaboration between humans and AI, so explainability is consumed and governed without slowing innovation
- Applying explainable AI across the platform; from dynamic pricing to agentic configuration (e.g., PPI GPT), ensuring visibility and accountability everywhere
Traditional technology approaches often force a trade-off between innovation and oversight. Zafin eliminates that trade-off, enabling banks to build systems that are both governed and explainable, providing the information leaders need today, while adapting to what’s ahead. Banks using Zafin can meet compliance requirements faster, reduce operational risk, and launch products with confidence while maintaining the pace of innovation. Over the long term, the systems that endure will be those that deliver accountable, explainable results at scale, earning trust from the people who rely on them daily.