Credit decisioning: AI’s biggest prize in asset finance

Of all the use cases being explored across Asset, Auto and Equipment Finance, credit decisioning attracts among the least current AI activity and carries the most long-term significance. That combination tells you something important about where the market is headed and what it will take to get there.

The AI Adoption, Workforce Redesign and Operational Readiness report from Resilient Management Solutions and Finance Connect puts credit decisioning and underwriting at just 11% when respondents are asked where AI has the strongest near-term business impact. Document processing leads at 46%, customer service at 18%. Credit sits well back from both.

That ranking reflects where the friction is, not where the value is.

Why credit decisioning is different

Most AI deployment in this market is following a pragmatic logic. Start where the operational pain is visible, where the data is accessible, and where a mistake is recoverable. Document processing fits that profile well. A misclassified document can be caught and corrected. A flawed credit decision carries consequences that are harder to reverse and attract a level of regulatory scrutiny that most other use cases do not.

That is the core reason credit decisioning is progressing more slowly. It is not that the technology is unproven. It is that the conditions required to deploy it responsibly in a regulated lending environment are significantly more demanding.

Explainability is the most immediate constraint. When AI contributes to a lending decline, Consumer Duty obligations require that the outcome can be explained in terms that are meaningful to the customer. Black-box models that cannot articulate the factors behind a decision are not compatible with that requirement, regardless of their predictive accuracy. The model architecture, the feature selection and the reason-code framework all need to be designed for explainability from the outset.

Accountability is the second constraint. Boards remain responsible for credit outcomes regardless of the degree to which AI supports or automates the decision. That accountability cannot be delegated to a model, which means governance structures need to be explicit about where human judgement sits in the process, what triggers escalation to manual review, and how model performance is monitored over time.

The long-term case is strong

Despite those constraints, the long-term case for AI in credit decisioning across Asset Finance is compelling. The volume of small-ticket decisions in Auto and Equipment Finance creates exactly the conditions where well-governed, explainable AI can deliver consistent, faster outcomes without requiring underwriter time on every file.

Predictive analytics and credit scoring models are already being explored by 49% of respondents, suggesting the appetite is building even where operational deployment remains limited. Machine learning for risk and pricing sits at 29%. The infrastructure of interest is there. What is still developing is the governance infrastructure required to deploy it at scale in a regulated context.

Murad Baig, Director, AI & Product Strategy at FIS frames the broader shift well in the report: “The question is no longer: ‘Do we use AI?’ It is: ‘Have we designed our leadership and systems so decisions can scale without us in the world of Agentic AI?'”

That question is particularly pointed for credit. Scaling decisions without adding equivalent underwriter capacity is precisely the productivity gain that boards across Asset Finance are seeking. But scaling without the right governance design will create regulatory and reputational exposure that outweighs the efficiency gain.

What getting it right requires

The firms that will unlock genuine AI value in credit decisioning are those that treat the governance design as the primary engineering challenge, not a secondary consideration once the model is built.

That means choosing model architectures that support explainability, not just accuracy. It means defining in advance which decisions can be handled by straight-through processing, which require augmented underwriter review, and which require full manual assessment. It means building reason-code frameworks that satisfy both internal audit and Consumer Duty obligations. And it means establishing ongoing monitoring for model drift, bias and performance degradation before deployment, not after the first issue surfaces.

None of that is straightforward. But it is solvable, and the organisations that solve it earliest will carry a durable advantage. In a market where time-to-decision is a competitive differentiator, and where underwriter capacity is finite, the ability to handle a growing volume of decisions consistently and compliantly is not a marginal gain.

It is a structural one.

Download the AI Adoption, Workforce Redesign and Operational Readiness report here.