Sandeep Shivam is an Associate Director at Tavant, building AI-powered lending products that improve efficiency and customer experience.getty​When I speak with leaders in banks and financial institutions, I often hear a version of the same question: How do we use large language models (LLMs) to move faster without creating new risks?While many financial services leaders agree that LLMs can clearly create value, the concern is whether institutions can trust them in environments where decisions, recommendations and actions must stand up to scrutiny long after the model has produced an answer.In my experience, the answer to this question starts by understanding that you cannot treat LLMs as ordinary software to be plugged into workflows and managed like previous generations of tools. In regulated environments, the moment an AI system starts influencing a credit decision, a compliance review, a payment workflow, a customer communication or a trading process, it becomes part of the institution’s decision chain. And decision chains in financial services must be accountable, auditable and traceable.Black-box thinking does not work in financial services.Black-box AI, where the model's decision-making processes are not revealed to users, may be tolerable in a low-risk consumer application, but it is a non-starter in banking, insurance, capital markets or lending. Regulators do not just want to know that a system worked most of the time. They want to know what it did, why it did it, what authority it operated under, what data it used and what safeguards were in place when it acted. Under the EU AI Act, for instance, credit decisioning is high risk, which means automatic logging will become a build requirement once the regulation comes in December 2027.That is a very different standard from the typical enterprise software mindset. In a regulated institution, an LLM cannot be treated like a mysterious engine that produces useful output. Instead, it has to be treated like an accountable operating component.Identity and accountability must come first.The first shift institutions should make is around identity. If an AI agent or LLM-powered workflow can reason, recommend or trigger an action, the institution should know exactly which agent did it. Not through a shared service account or a generic automation user, but through a distinct and attributable machine identity tied to clear human ownership and approval.If an examiner asks who approved a wire transfer adjustment or who generated a particular recommendation, “the system did it” is not a defensible answer. Financial institutions need a model where every meaningful AI actor has clear identity, bound authority and a visible approval chain behind it.Logging activity is not the same as explaining decisions.The second shift is around traceability. Traditional logs can tell you that something happened, but they are much weaker at showing why it happened.That difference becomes critical with LLMs. In financial services, an audit trail must be able to reconstruct the decision path: what information the model used, what prompt or instructions shaped the response, what version of the model was active, what alternatives were considered and what output was ultimately selected.This is why institutions should ensure they have decision trails that are tamper-evident, reconstructable and tied to causal provenance. Traditional logs explain activity, but causal audit trails explain reasoning. In a serious examination, that distinction matters a great deal.​Explainability has to be built in from the beginning.Another common mistake is assuming explainability can be handled later through reporting or documentation. Auditability and explainability are related, but they are not the same. Auditability captures what happened in a way that can be verified. Explainability captures why a decision was made in a way humans can understand. Business leaders, model risk teams, auditors and regulators all need different levels of explanation, from a plain-language summary to a detailed technical trace.If that structure is missing, institutions are left trying to reverse-engineer reasoning after the fact. That is expensive, unreliable and difficult to defend. In practice, explainability needs to be designed into the system from day one.Validation and control matter more than speed.Many financial institutions already have model risk frameworks, but most of those frameworks were built for more traditional models.LLM-based systems introduce challenges like prompt sensitivity, hallucinations, boundary failures and behavioral inconsistency across changing inputs.That means institutions need stronger pre-deployment validation, ongoing monitoring and explicit sign-off before these systems are trusted in production. This becomes even more important when the model is not just answering questions but influencing material business decisions.The desire to move quickly is understandable, but speed without traceability often brings more exposure than innovation in financial services. ​A control architecture is the real competitive advantage.I think this is where the market is headed: AI-powered financial services will stop focusing on deploying the most agents the fastest and instead prioritize building the strongest control architecture around them.​That means stronger identity controls, clearer read and write boundaries, immutable audit trails, structured explainability, circuit breakers and a staged path to autonomy. In other words, the real advantage will come from making LLMs operationally visible, accountable and defensible.That is the mindset shift I would encourage financial leaders to make. Do not ask only whether the model is powerful, but also whether its actions can be traced, explained, reconstructed and defended.In a regulated industry, that is what turns AI from an interesting experiment into something the business can rely on.​Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?