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Explainable AI in finance means every figure an AI system shows can be traced to the records and the rule that produced it. In Kognitos Apps, numbers are computed by deterministic steps written in plain English, never generated by a language model, so you can open any number and follow it back to source records.

In this stack it is enumerable. The catalog is built from 54 primitives: 29 deterministic, 4 connectors, 3 platform operations, 3 classical ML models, 1 OCR, 1 human step, 6 language-model operations, and the rest hybrids. Every step of every procedure names the one it uses: 1,225 citations across 236 finance templates. What is neurosymbolic AI?.

The symbolic layer is not a wrapper around a model. It is the majority of the system, and the model is a component inside it.

Structurally, the rendering path has no model in it: the widgets read run records and compute counts, sums, averages, rates, minimums, maximums and groupings. There is no model dependency in that code and there cannot be one.

Empirically, open any tile's inspect view. It shows the population and every record's contribution. If the number had not come from those records, the arithmetic would not reconcile.

What a model can produce is a field inside a record: an extracted amount, a classification, a drafted paragraph. Those are labelled, scored, and visible as such.

We will not use that phrase, because it is not true and you would find out. A model can misread an invoice, misclassify a transaction, or draft something unwise.

What is true is narrower and more useful: a hallucinated number cannot reach a tile, because tiles do not render model output. Model errors are possible, localized, labelled and correctable, not impossible. The numbers are computed, not generated.

Yes, and that is the point. Evaluating a finance application against an empty shell tells you nothing. Every catalog application ships with a sample database holding enough realistic history to work a full close, a full exception queue or a full onboarding backlog.

None of it is presented as your data. Each dependency carries an explicit binding, sample or live, and the inspect view always states which one answered.

They are modeled on the BIAN v13 service landscape: 300 templates, each carrying the BIAN source identifier, the published source URL for the service domain, and the step count of the source definition. You can open the BIAN specification alongside the application and check the mapping yourself.

An application is the surface a team works in. The AI employees are the roles that work the queues inside it, and the context graph is the memory they read the business from. The procedures described on this page are what all three execute.

No, and we would not trust a vendor who said otherwise: frontier language models are not bitwise deterministic even at temperature zero.

The honest claim is that the parts which must be exact are exact, because they are not models. Arithmetic, filters, tolerances, routing and the audit record are all symbolic. The model-touching steps are variable, individually identified, and their outputs land in fields you can see and correct.