An Israeli fintech team buying AI development should commission an evaluation and release process alongside the feature. Define which answers the system may produce, what evidence supports them, which actions require a person and which failures stop deployment. A fluent demonstration cannot answer those questions.
The useful buying question is specific: can a proposed engineering partner make one financial workflow testable enough for your product, operations and risk owners to approve? TuniCyberLabs supports buyers remotely. The approach below is a proposed engineering method, illustrated with a fictional analyst assistant, rather than a claim about a delivered banking project.
The local signal is governance moving into delivery
In its 17 May 2026 discussion of AI in banking, the Bank of Israel describes early integration during 2024–2025 and a survey of AI governance maturity. It reports initial organisational policies and controls alongside gaps to address as use expands. This is a useful signal for fintech suppliers selling into institutions: buyers will need evidence about operating the feature, as well as its demonstration.
The December 2025 inter-ministerial report announcement recommends a risk-based approach and discusses governance, disclosure and responsibility of financial bodies operating AI. These are report recommendations; this article does not treat them as a new universal legal requirement. Your reviewers should determine which obligations apply to your actual product.
Our procurement conclusion is narrower: build an acceptance package that lets those reviewers ask concrete questions. Make evidence a deliverable with an owner, rather than an appendix promised after launch.
Start with an assistant that has a clear boundary
Consider an illustrative assistant that summarises payment exceptions for an operations analyst. It may retrieve authorised transaction notes, identify missing information and draft a summary. It may not release money, change an account, invent a reason for rejection or decide a complaint. The analyst remains responsible for the next action.
Draw the workflow from incoming exception to final human decision. Mark the systems accessed, the facts needed and where the user sees uncertainty. A successful summary should link to the underlying records, distinguish events from assumptions and show when a source was last updated.
This boundary determines the work estimate. A read-only summary feature and an agent permitted to initiate payments have different failure consequences. Do not let an attractive interface conceal an expanded authority model.
Buy an evaluation set that resembles the work
Ask the partner to create a versioned set of cases with your domain specialists. Use approved synthetic or appropriately de-identified material. Keep a held-out set for release review so repeated prompt changes do not simply memorise the development examples.
For the example assistant, include:
- ▸A straightforward exception with complete, consistent records.
- ▸Hebrew and English notes describing the same business event.
- ▸Mixed-language text containing names, dates and currency amounts.
- ▸An outdated note contradicted by a later authoritative event.
- ▸A missing transaction record that should produce an explicit limitation.
- ▸A record belonging to another customer that the user cannot access.
- ▸A malicious instruction embedded in retrieved text that must remain untrusted data.
Have specialists write expected facts and forbidden conclusions, rather than prescribing one exact sentence. Evaluate factual support, permission handling and appropriate abstention separately from tone. A readable answer can still fail the test.
Specify the release evidence before choosing a model
Give every evaluation run an identifiable configuration: model version, prompt, retrieval settings, application version and dataset revision. Record outputs and reviewer decisions under an access policy appropriate for their contents. This makes a later regression investigation possible without retaining unnecessary customer data.
Choose explicit blocking conditions. For this illustrative read-only assistant, the team might block release after any observed cross-customer disclosure, invented payment status or unauthorised action attempt. These are proposed acceptance criteria, not a promise that a finite test proves zero risk. Other quality thresholds should reflect the intended use and review capacity.
Amounts and dates deserve deliberate handling. Where the workflow requires a calculation, use a verified application function and test it independently. Have the model explain or summarise the result without making its prose the accounting source of truth.
Rehearse the change that will happen after launch
Evaluate what happens when the model provider, retrieval index or product policy changes. A small prompt adjustment can alter a previously approved answer. Require the relevant evaluation suite to run before release and preserve the last accepted configuration for an agreed recovery procedure.
Walk through one incident: an analyst reports a plausible summary that cites the wrong record. Who disables the affected feature, preserves the necessary evidence, reviews exposure and communicates to users? Decide how the ordinary manual workflow continues. The fallback should be usable by operations staff, not only by the original developer.
Put the engineering partner's scope in writing
Request deliverables that include the workflow boundary, evaluation cases, access-control tests, release report, monitoring plan and handover session. Identify which domain judgements your team must supply. A supplier cannot validate a banking interpretation merely by connecting a model API.
Separate a bounded investigation from production implementation. The first engagement should establish whether the task is sufficiently supported by available data and measurable acceptance criteria. It may reveal that a simpler search or rules-based workflow better serves one part of the problem.
For collaboration responsibilities, use our guide to release ownership with an engineering partner for Israeli startups. To compare proposals consistently, use the European software vendor scorecard.
Bring one proposed AI workflow and a small set of approved example cases to a discussion of our software engineering services. Request an AI evaluation scope review to define the evidence your team needs before a production decision.