How Financial Services Teams Can Evaluate Automation That Delivers

Key Takeaways

McKinsey estimates that machines could perform 10% to 25% of work across bank functions, but buyers should evaluate individual workflows rather than automate entire departments.
A practical architecture links RPA platforms like UiPath or Blue Prism to integration points for ERP, payroll, IT service management, ISO 20022, and SWIFT.
Post-launch measurement should track exception age, straight-through processing, reconciliation completion, manual touches, and failed automation jobs.

Define the Operational Problem Before Comparing Vendors

A payment exception arrives at 4:45 p.m. The finance team copies transaction details from a banking portal into an ERP system, checks the beneficiary against internal records, emails an approver, and updates a spreadsheet used for reconciliation. Each tool works, but the process depends on people transferring data between them.

That fragmented work is a sensible starting point for financial automation. According to McKinsey, machines could perform 10% to 25% of work across bank functions in the next few years. McKinsey Global Institute separately estimates that about 42% of finance and accounting activities are already technically automatable with current technology.

Those figures describe technical potential, not a target for headcount reduction. Buyers should begin by documenting specific workflows, including transaction volume, source application, destination system, approval rule, exception path, and audit requirement.

Good candidates tend to involve structured inputs and repeatable decisions. These examples encompass importing ISO 20022 payment messages, matching purchase orders with invoices in SAP ECC, validating CSV payroll files, resetting user accounts via ServiceNow, or reconciling bank transactions against an Oracle database.

The less predictable the input, the more careful the design needs to be. A software robot can copy a validated invoice number reliably. Interpreting a poorly scanned handwritten invoice may require optical character recognition, confidence thresholds, and human review.

Build an Evaluation Around Processes, Not Product Demos

Vendor demonstrations often show a clean path through a workflow. Buyers need to examine what happens when an API times out, a supplier changes its invoice layout, or an employee record lacks a payroll code.

The evaluation scorecard should therefore include exception handling, role-based access control, credential storage, deployment model, version management, audit logging, and recovery after a failed job. For example, an RPA platform should record the robot identity, source record, action, timestamp, and outcome in an append-only log that can be exported to a security information and event management platform.

Organizations reviewing providers such as ECIT should also distinguish process advice from platform capability. A provider may support accounting, payroll, and IT services, while the underlying workflow could still depend on UiPath, Blue Prism, Automation Anywhere, Microsoft Power Automate, REST APIs, or ERP-native automation.

Integration depth matters more than the number of advertised connectors. Buyers should ask whether a connector uses a supported REST or SOAP API, reads directly from a SQL database, exchanges SFTP files, or relies on screen-level automation. API-based integrations generally tolerate interface changes better, while screen automation can remain useful for legacy systems that expose no service layer.

That said, replacing every legacy interaction is rarely practical. A controlled robot operating a Citrix-hosted finance application may be more realistic than commissioning a new API for software scheduled for retirement.

Plan Implementation Around Controlled Phases

A typical rollout starts with process discovery and technical validation, moves into a limited production deployment, and expands only after exception patterns are understood. The timeline should reflect process count, application access, testing obligations, and the availability of representative financial data rather than an arbitrary deadline.

The working group commonly includes a finance process owner, payroll or accounting specialist, automation developer, IT integration engineer, security reviewer, and internal audit representative. In a smaller organization, one person may cover several roles, but approval authority and development access should remain separated.

During technical validation, the team maps every field between systems. For instance, an ISO 20022 payment instruction could require populating beneficiary details in an ERP payment batch while simultaneously retaining its original message identifier for reconciliation. Payroll automation may involve encrypted CSV files transferred over SFTP, validation against employee master data, and rejection of records with missing tax codes.

ECIT can be assessed on how its accounting, payroll, and IT service capabilities support these controls, including API ownership, data residency, escalation paths, and audit evidence. Buyers should also establish who updates a robot when an ERP screen changes and whether that work is covered by a managed-service agreement.

Before broader deployment, test duplicate records, malformed files, unavailable applications, revoked credentials, and partial batch completion. A robot that processes 900 records and silently skips 100 creates a harder accounting problem than a job that stops with a clear error.

Select Outcomes That Finance and IT Can Observe

The business case should use operational measures available from workflow logs, ERP timestamps, and ticket records. Useful metrics include the percentage of transactions completed without human intervention, average exception age, number of manual touches per invoice, reconciliation completion time, payroll corrections, and failed jobs by cause.

Generative AI and intelligent automation could raise banking productivity by 2.8% to 4.7% of industry revenues, equivalent to $200 billion to $340 billion in annual value, according to McKinsey Global Institute. Individual buyers should not treat those industry estimates as promised savings. Their baseline may reveal that approval delays, poor master data, or application downtime account for more lost time than manual entry.

Quality deserves equal weight. For an accounts-payable workflow, buyers can compare invoice totals, duplicate-payment flags, and unresolved three-way-match exceptions before and after launch. In IT services, they can monitor reopened tickets, automation failure codes, and the percentage of password resets completed after identity verification.

While technology vendors often keep customer-specific performance metrics confidential, buyers should request reference architectures and anonymized operating data, then validate projected outcomes against their own transaction sample.

Buyer Takeaways From the Evaluation Process

Starting with one bounded workflow makes technical weaknesses visible. A payment-reconciliation process exposes message formats, ERP integration, approval controls, and exception handling without requiring the team to redesign the whole finance function.

Audit design should begin during process mapping. Adding log retention after deployment can leave gaps in robot identities, approval records, and source-document lineage.

Human review also needs an explicit trigger. An OCR confidence score below an agreed threshold, a beneficiary change, or a payroll variance outside policy should route the record into a work queue rather than allow the robot to guess.

Finally, ownership cannot stop at launch. Dashboards should expose queue depth, failed jobs, credential expiry, API latency, and software version so finance and IT can decide whether a problem belongs to the process, integration, or platform.

Broader Applicability

Banks, insurers, manufacturers, and professional-services firms can apply the same evaluation model to accounting, payroll, and IT workflows. The specific protocol may change from SWIFT messaging to an ERP API, but the need for traceable decisions and managed exceptions remains.

How long does financial automation implementation take?

Duration depends on application access, process variation, security review, and test-data quality. A single API-based reconciliation is usually easier to deploy than a workflow spanning a Citrix application, SFTP payroll files, and manual approvals, so buyers should request phase-based estimates tied to named integrations.

What is the difference between RPA and intelligent automation?

RPA follows defined rules to move data or operate application interfaces. Intelligent automation adds technologies such as OCR, machine learning, or generative AI, but it still needs confidence thresholds, human-review queues, and audit logs when processing financial records.

Is financial automation suitable for a small finance team?

It can be, particularly when the team repeatedly handles structured invoices, payroll validations, reconciliations, or IT requests. A smaller buyer should favor a limited workflow with measurable volume and supported REST, SFTP, or database integration rather than building a large automation estate immediately.

Editorial oversight by Rich Tehrani — CEO of TMC and creator of ITEXPO.

Disclosure: Rich Tehrani is also CEO of RT Advisors and a Registered Representative offering securities through Four Points Capital Partners LLC (Member FINRA/SIPC). Nothing in this article is an endorsement or recommendation to buy or sell any security or sector mentioned. RT Advisors may have or have had business relationships with companies mentioned in TMC Insight articles. Such relationships do not affect editorial judgment or coverage.