Open Finance and AI in Treasury: Moving from Manual Reconciliation to a Multi-Bank View

Corporate treasury has always faced a practical problem: the money sits in banks, but the cash view usually lives in spreadsheets, bank return files, internet banking portals, manually downloaded statements, and integrations that don't talk to each other.

Open Finance changes part of that equation by enabling standardized, consent-based sharing of financial data. With it, companies can access balances, statements, and banking information from multiple institutions through APIs, reducing their reliance on manual downloads and proprietary integrations.

AI comes in as a complementary layer. It helps classify transactions, suggest accounting matches, identify anomalies, update forecasts, and turn banking data into operational decisions.

The expected outcome isn't just a better-looking dashboard. It's a treasury function that stops operating by looking in the rearview mirror and starts tracking cash, receipts, payments, and exceptions continuously.

What Open Finance changes for companies

For individuals, Open Finance is usually associated with sharing data between banks. For companies, the real potential lies in operations.

A company with accounts at several banks needs to answer simple questions every day. What is the consolidated balance right now? Which accounts have cash available? Which payments go out today? Which receipts have already come in? Which statements haven't been reconciled yet? And, above all, which discrepancies actually require human action?

Without integration, these questions depend on bank logins, tokens, files, emails, spreadsheets, and manual checks. With Open Finance, part of this data can flow in a standardized way into a management layer.

The multi-bank treasury problem

Many companies accumulate banks as they grow.

One bank handles payroll. Another concentrates receivables. Another offers a better credit line. Another has a relationship with a particular business unit. Another is required by a specific customer, supplier, or operation.

This scenario is common, but it creates complexity.

Treasury ends up dealing with multiple balances, statement formats, settlement dates, identifiers, fees, and bank descriptions. When file delays and discrepancies between the bank and the ERP are added to the mix, consolidating cash becomes a daily verification chore rather than a reliable view for decision-making.

The problem isn't having multiple banks. The problem is not having a reliable layer to see all of them.

From batch reconciliation to continuous reconciliation

Traditional bank reconciliation is usually done in batches: the team downloads files, imports them into the ERP, cross-checks data, handles discrepancies, and closes out open items.

This model works when volume is low and complexity is limited. In larger operations, it creates delays. The company only discovers problems after they have already piled up.

With banking data arriving more frequently, reconciliation can move closer to a continuous process.

Instead of waiting for the close, the system can capture transactions throughout the day, classify inflows and outflows, compare them with expected invoices, suggest matches, separate exceptions, alert the people responsible, and update the forecast while operations are still underway.

AI helps mainly in cases where fixed rules fall short. For example: inconsistent bank descriptions, bundled payments, embedded fees, partial amounts, or customers with multiple open invoices.

The role of AI in classification and matching

AI doesn't replace governance. It reduces operational effort when applied with proper controls.

In treasury, it can classify transactions by type, identify payers and payees, suggest cost centers, detect out-of-pattern entries, and find likely matches between the bank and the ERP. It can also prioritize high-impact exceptions, explain discrepancies, and feed cash forecasts with signals that were previously scattered across spreadsheets and statements.

The critical point is maintaining an audit trail. If the AI suggested a match, the system needs to show why: amount, date, CNPJ, history, description, related invoice, or recurring pattern.

Without a minimum level of explainability, automation becomes a risk.

Cash forecasting with fresher data

Cash forecasting depends on the quality and timeliness of the data.

If the team relies on old statements, outdated spreadsheets, and manual updates, the forecast always arrives late. It may be well structured, but it loses value when it doesn't reflect actual cash.

Open Finance can reduce the latency of banking data. AI and automation can turn that input into more useful projections.

A good forecast combines current balance by bank, accounts payable, accounts receivable, recurring billing, scheduled payments, expected receivables, seasonality, payment delay history, non-recurring events, credit limits, and investments. The difference lies less in the list of inputs and more in the update cycle.

The faster treasury sees what has changed, the faster it can decide.

Open Finance doesn't eliminate the ERP

A common mistake is treating Open Finance as a replacement for the ERP. It isn't.

The ERP remains the system of record for invoices, accounts, documents, suppliers, customers, cost centers, and accounting entries. Open Finance brings standardized banking data. Automation connects these worlds.

The healthiest architecture separates roles. The bank reports transactions, balances, and financial events. The ERP maintains invoices, accrual periods, and the accounting structure. The automation layer connects data, rules, and exceptions. Dashboards show position, risk, and open items. People continue to approve exceptions and critical decisions.

When these roles get mixed up, problems arise: settlements without supporting evidence, reconciliations without explanation, forecasts disconnected from operations, or banking data without accounting context.

Governance of consent, access, and approval levels

Open Finance depends on consent. For companies, this brings additional challenges.

Unlike an individual, a company may have legal representatives, multiple accounts, approval levels, internal policies, and access controls.

Before scaling usage, finance needs to define who can authorize data sharing, which accounts are in scope, how long consent remains valid, who renews or revokes access, what data can be used by automation, and which decisions require human approval. Auditors also need to be able to review this usage clearly.

AI and automation must respect these rules. Technology should carry the company's controls, not bypass them.

Payments and payment initiation

Beyond data, Open Finance also connects to payment initiation, especially with Pix.

For treasury, this opens the door to centralizing payments in a single environment. But execution needs to account for limits, approvals, segregation of duties, and audit trails.

A secure flow might look like this:

1. the ERP creates a payable obligation;
2. automation validates banking details and rules;
3. the batch is prepared;
4. approvers sign off according to their approval level;
5. the payment is initiated;
6. the response confirms settlement;
7. the ERP records the settlement;
8. exceptions go into a queue.

The point isn't to remove controls. It's to remove rework.

Metrics to track

To find out whether Open Finance and AI are improving treasury, track operational metrics.

Some examples:

• percentage of bank accounts integrated;
• time to consolidate the daily cash position;
• percentage of transactions reconciled automatically;
• volume of exceptions by reason;
• average time to resolve discrepancies;
• forecast accuracy by horizon;
• number of manual logins to internet banking;
• payments processed with a complete audit trail;
• differences between the ERP and the bank;
• open items by bank, entity, or account.

These metrics show whether the company is gaining control or simply adding yet another technology to the process.

Where to start

The best starting point isn't connecting every bank at once.

Start with a critical flow: daily bank reconciliation, a 13-week forecast, balance consolidation, accounts receivable settlement, supplier payments, or multi-bank visibility.

Choose a scope with high volume, clear rules, and evident impact. Then expand.

A well-run pilot should have:

• defined banks and accounts;
• success criteria;
• owners;
• access control;
• an exception plan;
• a before-and-after comparison;
• flow documentation;
• an audit trail.

The role of Abstra

Abstra serves as an automation and orchestration layer for financial processes that live between systems.

In an Open Finance and AI scenario, this means connecting banking data, the ERP, spreadsheets, internal rules, approvals, and exceptions in auditable flows.

In practice, this can mean consolidating balances from multiple accounts, creating reconciliation queues, classifying transactions with AI, validating payments before execution, generating discrepancy alerts, updating forecasts automatically, recording logs and evidence, and integrating human approvals at critical steps.

Finance doesn't have to choose between control and speed. The right design delivers both.

Treasury checklist

• Which banks concentrate the highest financial volume?
• Which accounts need to be included in the consolidated view?
• Does the ERP have enough identifiers for reconciliation?
• Who authorizes and renews consents?
• Which payments require multiple approval levels?
• How are exceptions recorded today?
• Does the forecast use up-to-date banking data?
• Which rules can be automated without risk?
• Where can AI suggest, but not decide on its own?
• Can auditors reconstruct every action?

Conclusion

Open Finance and AI can transform treasury, but the value doesn't come just from connecting APIs. It comes from redesigning the flow: fresher banking data, the ERP as the source of context, automation as the execution layer, and people acting at decision points.

For companies with multiple banks, high volume, and a lot of manual reconciliation, this combination can reduce delays, improve forecasting, and provide greater control over liquidity.

The path starts small: one process, one set of accounts, one exception queue, and one clear metric. From there, treasury stops putting out fires and starts operating with continuous visibility.