AI will not replace deterministic workflows in financial services. It complements them with reasoning and context capabilities where rules alone are not enough.
Core financial processes cannot handle the probabilistic reality of LLMs. By structural and regulatory necessity, they must be deterministic. But AI still has a place in financial services.
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While data must move from place to place without variation, AI can examine that data to help firms solve problems faster. The deterministic workflow produces structured output. The AI produces meaning.
We call this approach “deterministic plus.” It leaves deterministic workflows deterministic, but adds an agentic layer that interprets what those workflows surface.
In financial services, a deterministic approach is often a legal requirement, and the regimes that mandate explainability aren't going away.
So the question isn't whether deterministic workflows will go away. They won’t. The question is where AI can add value that deterministic workflows alone structurally cannot: automating work that's unstructured, context-dynamic, and requires reasoning.
The deterministic-plus pattern works like this:
Our customers often find “deterministic-plus” opportunities when they look at the manual work that surrounds integrations and automations. Where is data handed to a person who then does analysis? What could AI do before the data is handed off to make that analysis more effective?
While real-time settlement systems now handle most institutional transactions, core banking overnight batch processes still handle a small portion of the load. These processes require deterministic operations. Accounting reconciliation, transaction aggregation, and end-of-day settlement need to behave identically every time, with complete traceability.
When a batch run detects an accounting discrepancy, the deterministic workflow moves the imbalance to a suspense account. It also aggregates all relevant transaction logs, API payloads, and system-of-record states across whatever legacy systems are involved.
Then it waits for senior operations staff to log in. Diagnosing the root cause of a batch failure can consume 40-60% of total mean time to resolution , and banking batch failures carry real liquidity risk for every hour they remain unresolved.
Deterministic plus solution: AI can’t help resolve discrepancies, but it can accelerate the diagnosis. It can reason across the aggregated forensic data to suggest a probable root cause and a recommended remediation. By 6 a.m., when senior operations staff log in, the exception ticket isn't blank. It contains a probable diagnosis (a 1-hour cut-off window mismatch with an external vendor network, for example), the relevant transaction IDs, and a suggested action (a ledger reversal, a force-posting, an escalation path).
Value: The AI does the overnight reading. It proposes a likely root cause and potential solution. The team decides what to do with it. The AI won’t be right every time (that’s why humans stay involved), but it will be right most of the time. When it is, it will cut remediation time by half.
Financial services firms increasingly use data meshes to facilitate analytics. This approach distributes data ownership across domain teams rather than concentrating it in the IT department. Each team publishes data for the rest of the organization to consume and analyze.
Data catalog projects often fail due to lack of adoption and engagement. Many failures trace to a lack of up-to-date documentation that non-technical users can understand. Without proper guides, analysts can’t find what they’re looking for and revert to siloed spreadsheets.
Deterministic plus solution: An agentic workflow takes a sample of data from the deterministic data pipelines, along with any existing documentation, to produce an up-to-date guide, including any major changes.
Value: The catalog stays current without a documentation backlog. Non-technical teams can find, understand, and trust the data products they need. The adoption gap narrows, and the firm’s investment in the mesh yields a return.
Every night, Digibee banking customers sync branch data back to the home office—including a rolling record of customer complaints. Simple aggregation of this information can show complaint volume and category breakdowns, but can’t get deeper than that.
Deterministic plus solution:
Layer in an agentic workflow that:
The agent runs downstream against the complaint data on a rolling basis—summarizing substance across batches, identifying geographic or product-line concentration, and layering current volumes against historical baselines.
Value: The core banking process stays auditable, but the signal that once took weeks of manual analysis can appear in hours.
Each of these use cases centers on a core process that leaves no room for probabilistic output. None of the proposed solutions touch those core processes. Instead, they ask AI to synthesize inputs that don't reduce to a formula. That's where LLMs perform reliably. It's also work that has historically either gone undone or consumed senior human capacity that belonged elsewhere.
natural candidates for a complementary AI workflow, talk to a member of the Digibee team.
Matt Casey, Senior Product Marketing Manager at Digibee, creates blog posts, videos, e-books, and other content about enterprise technology. Before moving into product marketing, Matt built data products as a data scientist and produced stories for magazines, newspapers, and radio stations. In his spare time, he plays board games, parents two sons, and builds the occasional AI side project.
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