Beyond the Checkbox: The 2026 Treasury Technology RFP for an Agentic Era

Key TakeawaysTreasury is shifting from a system of record to a system of action, where the platform does not just store truth, it triggers workflows and decisions from truth.The overnight file era is no longer enough for modern volatility, faster payment rails, and multi-entity complexity. Latency is now a treasury risk.In 2026, your RFP must separate RPA, which runs static rules, from agentic intelligence, which reasons through ambiguity with controlled autonomy and explainability.The RFP questions that matter most test for API connectivity, metadata enrichment, agentic exception handling, fraud detection, and risk controls like data isolation and HITL thresholds.

Executive Summary

At 8:12am, the question arrives: “Do we have enough liquidity to run payroll, prepay a vendor, and still stay safe?” If your answer depends on logging into portals, exporting files, and stitching together yesterday’s balances in a spreadsheet, you do not have cash visibility. You have a ritual. Rituals break the moment reality moves. A sweep posts early, a wire lands late, an entity opens accounts in a new region, or a payment rail accelerates settlement. In 2026, modern treasury teams are replacing tool stacks that only report history with technology that can drive action. That is the shift from a system of record to a system of action. The best platforms in this category can pull current bank truth quickly, preserve context through metadata enrichment, reason through exceptions, update forecasts as actuals arrive, and route decisions into controlled approvals. This guide gives you a treasury tech RFP template designed for an agentic era. It is built to cut through AI-washing and force vendors to show their work. Use it to evaluate architecture, connectivity and latency, agentic intelligence, operational agility, and risk posture in a way that maps to how treasury actually operates. For broader treasury process requirements, see The Ultimate Checklist for Cash Management and Forecasting. For background on the treasury function, see Treasury Management.

Modern Treasury Architecture

Sub-second visibility and why the overnight file era is over

A modern treasury stack is not defined by module lists. It is defined by how quickly it can move truth from bank systems into a workflow, and how safely it can automate what follows. The overnight file model was designed for a slower world. It assumes that end-of-day snapshots are close enough for tomorrow’s decisions. That assumption breaks when cash moves intraday, when entities multiply, and when payment rails compress reaction windows. In 2026, your RFP needs to test for architecture that is built for faster truth and faster action.

Connectivity and Latency

Real-time API connectivity vs daily SFTP batches

RFP Question 1: Connectivity Model

Does the platform rely primarily on daily SFTP batches, host-to-host files, or end-of-day statements, or does it provide real-time, or near real-time, API connectivity to your banking partners? Why it matters: If you are looking at yesterday’s data, you are making yesterday’s decisions. API connectivity and lower latency improve the chances that treasury is acting on current balances and current flows. Evidence to requestA list of supported banks and connection types by region, API, host-to-host, SWIFT, file-basedRefresh frequency by connection type, intraday vs dailyAn uptime and monitoring approach, plus any SLA language availableA step-by-step implementation plan for your top three banks, including prerequisites and timelines Red flags”Real-time capable” without a defined refresh window and clear coverage limits”API connectivity” that still relies on daily file updates for core workflowsNo clarity on how connectivity differs across banks, regions, or account types

RFP Question 2: Latency and Time-to-Truth

What is the measured end-to-end time from a bank posting event to availability in the platform for reporting, forecasting, and workflow automation? Why it matters: Treasury does not just need data access. Treasury needs usable truth fast enough to act, especially across multi-bank environments where one stale account can distort decisions. Evidence to requestMeasured examples showing posting time and platform visibility timeHow intraday updates are handled versus end-of-day statementsHow reconciliation and forecasts update when new actuals arrive Red flagsData that updates only once per day, regardless of bank activityForecast updates that require manual refresh or scheduled batch jobs only

Metadata Enrichment

Keeping context intact from bank to ERP or TMS

RFP Question 3: Metadata Preservation and Enrichment

How does the platform ingest, preserve, enrich, and pass through transaction metadata, including remittance information, from the bank to downstream systems like ERP or TMS? Why it matters: Automated reconciliation fails the moment you lose context. Metadata is the why behind the payment. Without metadata enrichment, matching quality falls, exception volume grows, and audit work increases. Evidence to requestA sample transaction record showing raw bank fields and stored fields in the platformField-level mapping documentation for ERP and TMS exportsHandling of ISO 20022 fields where applicableRules or enrichment logic, normalization, tagging, and match scoring Red flagsMetadata truncated, overwritten, or collapsed into generic fields”We store statements” without field-level retention proofRemittance handling that is vague or treated as out of scope

Unstructured Ingestion

PDFs and emails are not edge cases

RFP Question 4: Unstructured Ingestion and Linkage

How does the platform ingest unstructured inputs like PDF invoices, remittance advice, and email payment notifications, and link them to transactions for reconciliation and audit? Why it matters: Treasury operations are messy by default. A modern system of action should absorb common unstructured inputs without forcing teams into manual exception loops. Evidence to requestA demonstration showing PDF or email ingestion, extracted fields, and transaction linkageAccuracy targets and human review controlsAn audit trail that includes the original artifact plus extracted outputs Red flagsUnstructured data requires external tools and manual handoffsNo human review and approval path for extracted contentNo traceability between source documents and system actions

The Anatomy of Agentic Intelligence

Differentiating static RPA from reasoning-based AI

In 2026, “AI” is not a feature. It is an operating model. Your RFP should separate two very different systems. RPA is rules and scripts. It is useful for repeatable tasks, but it breaks when inputs shift. Agentic intelligence is reasoning plus tools plus guardrails. It can propose resolutions, learn patterns, and operate autonomously within defined boundaries, with explainability for every action.

RFP Question 5: Decision Reasoning in Exceptions

When a reconciliation exception does not have a 1:1 match, what does the system do? Does it simply list failures, or can it propose resolutions based on patterns and context? Why it matters: If exceptions only surface as failures, the human work stays the same. A system of action should reduce exception labor by proposing likely matches and routing the decision into controlled approvals. Evidence to requestReal examples of exceptions with suggested resolutionsConfidence signals, match rationale, and user feedback loopsControls for suggestion-only vs auto-action behavior Red flags”AI flags exceptions” without proposing resolutionsNo explanation of what signals drive match suggestionsNo measurement of suggestion quality over time

RFP Question 6: Explainable AI and Auditability

Does every automated action, tagging, matching, forecasting adjustments, anomaly flags, come with a clear audit trail explaining the rationale? Why it matters: Auditors and controllers will not accept “the model decided” as justification. Explainability is the bridge between automation and governance. Evidence to requestSample audit logs showing inputs, decision, rationale, and user confirmationsExportable logs and controls for retentionClear visibility into model changes and rule changes that affect outcomes Red flagsRationale is limited to a confidence score with no explanationForecast shifts with no traceable driversInability to reproduce why a decision was made

RFP Question 7: Self-Learning Loops and Rule Drift

When business rules change, new entities, new banks, new payment behaviors, how does the system adapt? Does it require manual reconfiguration, or learn new patterns with guardrails? Why it matters: Treasury structures change constantly. Systems that require frequent rebuilds create reliance on a small group of power users and long implementation cycles. Evidence to requestHow learning happens, what is automated, what is configurableMonitoring for drift, rollback controls, and approval of learned behaviorsDemonstrations of adapting to a new entity or a new bank connection Red flags”It learns” without explaining guardrails and monitoringNo rollback, no drift detection, no governance around change

RFP Question 8: Fraud Detection and Pre-Execution Controls

How are models applied to detect anomalies in beneficiaries, payment amounts, timing, and payment instructions, and can the system flag risk before execution? Why it matters: Faster payment rails reduce reaction windows. The most useful fraud detection happens before funds move, and it must integrate into approvals and workflows. Evidence to requestSupported anomaly categories and tuning controlsHITL thresholds by risk level, amount, and entityEvidence of false positive management Red flagsFraud detection limited to generic, rules-only alertsAlerts that do not integrate into approvals or blocksNo tuning, no measurement, no governance

Operational Agility

Managing multi-entity complexity and 13-week forecast autonomy

RFP Question 9: Multi-Entity Complexity

How does the solution manage cash consolidation across dozens of legal entities, jurisdictions, currencies, and bank relationships, including intercompany visibility and policy control? Why it matters: Multi-entity complexity is where spreadsheets and partial systems collapse. Treasury needs consolidated visibility without losing entity-level control, access boundaries, and auditability. Evidence to requestEntity hierarchy support and permissions modelIntercompany tagging, mapping, and reporting approachControls for jurisdiction-specific policies and access Red flags”Supports multi-entity” without showing controls and segmentationHeavy manual mapping required to keep the model accurate

RFP Question 10: Forecast Autonomy for a 13-Week Model

Does the system provide a static forecast output, or does it autonomously update a 13-week forecast based on new actuals, and explain changes clearly? Why it matters: Forecasting is only valuable if it stays current and interpretable. Autonomy without explainability increases risk. Evidence to requestDemonstration showing actual inflow or outflow updating the forecastDrivers used, bank data, ERP signals, historical patternsOverride controls, scenarios, and governance Red flagsForecast updates that are manual or scheduled weekly onlyForecast changes that cannot be explained or audited

RFP Question 11: Implementation Timeline and Time-to-First-Automation

Provide a detailed 90-day implementation plan. What is the expected time-to-first-automation for a standard reconciliation workflow? Why it matters Treasury platforms should demonstrate measurable value quickly, especially when they layer on top of existing systems rather than replacing them. Evidence to requestWeek-by-week milestones tied to outcomesPrerequisites and dependencies, banks, ERP, permissionsClear definition of “first automation” and how it is validated Red flagsTimelines measured in quarters with no early winMilestones described as tasks, not outcomes

Risk and Governance

Essential questions on data isolation, security posture, and HITL thresholds

RFP Question 12: Data Isolation and Model Training Boundaries

Is customer data isolated, and is it used to train shared models that impact other customers? Can the vendor contractually prohibit training and reuse of customer data? Why it matters: Treasury data is sensitive. Model training boundaries must be explicit, enforceable, and aligned with enterprise security expectations. Evidence to requestData handling and isolation model, tenant isolation, encryption, retentionContract language options for no-training clausesData residency controls and access controls Red flags”We may use data to improve models” without a defined scope and opt-outNo contractual mechanism to restrict training or reuse

RFP Question 13: Human-in-the-Loop Thresholds

Describe the human approval interface. Can you define granular thresholds where the system operates autonomously vs where it pauses for approval? Why it matters: Agentic intelligence is only acceptable in treasury when autonomy is controlled. The best platforms allow policy-driven approvals by amount, entity, vendor type, risk score, and anomaly category. Evidence to requestApproval workflows and threshold policy examplesAudit logs for approvals and overridesEscalation paths for exceptions Red flagsAll-or-nothing automationHITL that is a single approval step instead of a policy engine

Choosing the Right Treasury Tech Vendor in 2026

Use one simple filter when reading responses. Ask whether the vendor is answering a system of record question or a system of action question. A system of record stores and reports. A system of action does four things reliably.Pulls truth fast enough to matterPreserves context through metadata enrichmentReasons through ambiguity with explainabilityAutomates with controlled governance That is what this treasury tech RFP template is designed to surface. If you want to see how a system of action looks in practice for treasury workflows like cash positioning, reconciliation, and forecasting, you can start with Nilus here.

FAQs

Why is real-time API connectivity critical for 2026 treasury?

Because latency is operational risk. Faster payment rails and higher volatility reduce the time between “data changed” and “decision needed.” Real-time, or near real-time, API connectivity reduces reliance on stale snapshots and supports faster liquidity decisions, especially when paired with clear governance and auditability.

What is the difference between RPA and Agentic AI in an RFP?

RPA automates repeatable tasks using fixed rules and scripts. Agentic intelligence can reason through ambiguity, propose next steps, and learn patterns, while operating within defined approval and audit boundaries. In an RFP, require evidence of explainability, controlled autonomy through HITL thresholds, and demonstrated exception resolution, not just “AI-powered” labels.

How do I evaluate an AI vendor’s data security?

Start with attestations like SOC 2 Type II and ISO 27001 where applicable, then pressure-test specifics:Data residency and processing locations: Confirm exactly where your treasury data is stored and processed.Model training boundaries: Ask whether your transaction data or prompts are used to train or fine-tune the vendor’s AI models.Access controls: Evaluate role-based permissions, MFA, and audit logging.‍****Incident response: Request the vendor’s documented breach notification timeline and their track record of past incidents.

Comparing RFP Templates with Agentic AIs for Corporate Treasuries

• { box-sizing: border-box; margin: 0; padding: 0; } body { font-family: Arial, sans-serif; font-size: 14px; color: #222; } table { width: 100%; border-collapse: collapse; table-layout: fixed; } col.label { width: 18%; } col.legacy { width: 41%; } col.agentic { width: 41%; } th, td { border: 1px solid #ccc; padding: 12px 14px; vertical-align: top; line-height: 1.5; } /* Header row / thead th { background: #fff; font-weight: bold; text-align: center; font-size: 15px; } thead th:first-child { border-top: none; border-left: none; border-bottom: none; background: transparent; } / Section header rows / .section-header td { background: #fff; font-weight: bold; text-align: center; font-size: 15px; border-left: 1px solid #ccc; border-right: 1px solid #ccc; } / Label cells / td.label { font-weight: normal; color: #222; } / Data cells */ td.legacy, td.agentic { font-weight: normal; }

  Legacy RFP Agentic AI (Nilus)

  Connectivity

  Data ingestion Overnight SFTP batch files and end-of-day snapshots only Native real-time or near real-time API connections to banking partners with faster visibility across accounts, FX, and liquidity

  Data latency Yesterday’s data, decisions often made on stale positions More current bank data enabling liquidity decisions with lower latency as conditions change

  Data handling

  Metadata Context can be stripped in transit, remittance info may be lost before reaching ERP or TMS Full transaction descriptions and remittance data preserved end-to-end through to audit

  Unstructured formats Struggles with PDFs, email notifications, and non-standard formats Ingests common unstructured inputs and prioritizes extracting usable data content with human review controls

  Automation and intelligence

  Automation model RPA with static rules that break when inputs change Reasoning-based agentic intelligence that can propose actions and adapt with defined guardrails

  Reconciliation exceptions Generates a list of failures requiring manual investigation Suggests resolutions based on patterns and context, requests human confirmation when needed

  Self-learning Manual reconfiguration required when entities, banks, or rules change Learns new patterns with monitoring and change controls, adapts as operations evolve

  Forecast model Static output requiring manual refresh Updates 13-week forecasts as new actuals arrive and provides explainability for changes

  Risk and data compliance

  Audit trail Limited rationale behind automated decisions Explainable AI logs for tagging, matching, and forecasting actions with rationale

  Fraud detection Rule-based flags and limited anomaly detection Anomaly detection that can flag beneficiary or payment pattern shifts before execution, with HITL thresholds

  Human oversight (HITL) Limited approval workflows, often all-or-nothing automation Granular thresholds so the system can act below limits and pause above them

  Scale and security

  Multi-entity Complex to manage across many entities and jurisdictions Supports multi-entity consolidation with role-based access and policy controls

  Data security Standard SOC 2 Type II with limited controls on model training use Data isolation controls and contractual options to restrict model training use, plus RBAC, MFA, and audit logging