How AI Cuts Operational Costs in Financial Services
Financial services is moving past simple cloud lifts and shifting to targeted automation that removes manual work at the source. AI agents now flag exceptions before they hit the P&L, auto-classify invoices, and keep reconciliation tight without overtime. The business case is clear: Mastercard reports 73% of organisations using embedded finance have reduced manual effort, with the same percentage seeing gains in accuracy.
This isn't about shiny tools. It's about cleaner processes, fewer keystrokes, and faster closes.
From manual workflows to exception-led operations
Oracle NetSuite's Financial Exception Management agent identifies anomalies early, so your team focuses on resolution, not data hunting. Combined with RPA, banks are extracting data from PDFs, emails, and portals, then triggering real-time actions across ERP, inventory, and sales ops.
NetSuite's Bill Capture uses document object detection and OCR to eliminate data entry, speeding approvals and shrinking cycle time. As Evan Goldberg of Oracle NetSuite put it: "Modern businesses with diverse revenue streams need to connect data and automate processes across their financials, inventory, sales operations and more."
Productivity through data partnerships
Google Cloud is collaborating with institutions like Starling Bank and Ecobank to apply AI where it moves the needle: customer interaction, accessibility, and live data analysis. The focus is practical-solve business problems with data, rather than shifting legacy pain into a new environment.
The result: fewer handoffs, faster responses, and clearer lines of sight for operations teams managing risk, service levels, and cost per transaction.
Embedded finance cuts reconciliation drag
Reconciliation and reporting accuracy are persistent pain points for teams still tied to manual workflows. According to Mastercard, organisations adopting embedded finance report meaningful reductions in manual effort, plus improvements in accuracy and reliability-especially across procurement and payment flows. Integrating payments with procurement platforms also helps with cash flow control and supplier relationships.
See Mastercard's perspective on embedded finance.
Where finance and operations should start
- Target the worst friction first: AP invoice capture, supplier onboarding, reconciliations, KYC/AML checks, and exception handling.
- Instrument the data: standardise formats, map fields in ERP/procurement, and set reference data (vendors, terms, GL codes) to reduce false positives.
- Automate decisions with guardrails: define approval thresholds, escalation paths, and audit trails to keep compliance clean.
- Integrate end-to-end: link procurement, ERP, banking, and document systems so data flows without copy-paste.
- Pilot narrowly, then scale: choose one process, set clear baselines, and track results weekly.
Metrics that prove the value
- Straight-through processing rate (invoices, payments, reconciliations)
- Exceptions per 1,000 transactions and average time to resolution
- Time-to-close (days) and rework rate on journal entries
- Cost per invoice and touchpoints per invoice
- PO match rates (2-way/3-way) and on-time payment percentage
If you can't measure it weekly, you can't improve it. Put these KPIs on a single dashboard and review them with finance ops and IT together.
Practical playbook: AI in finance ops
- Exception-first design: route clean transactions straight through, isolate anomalies for human review.
- Document intelligence: use OCR and object detection to capture fields, line items, and terms from invoices and statements.
- Continuous reconciliation: match payments, purchase orders, and receipts in near real time to avoid month-end pileups.
- Policy as code: embed approval rules, segregation of duties, and sampling into the workflow-not in a PDF on a shared drive.
- Feedback loops: use corrections from analysts to retrain models and cut future exceptions.
Why this matters right now
Manual work is a tax on margins. AI-driven workflows cut processing time, reduce errors, and free up analysts for higher-value work like supplier strategy and working capital optimisation. You get faster closes, tighter controls, and fewer late fees.
And you don't need a full platform overhaul to get wins. Start with invoices, reconciliation, and exception handling; expand as the metrics improve.
What's next
OpenText and Cognizant are bringing senior executives together for an exclusive breakfast roundtable on how Generative AI is improving enterprise content, strengthening customer communications, and lifting operational efficiency across financial services. The session will be held at Tiffany's Blue Box Café on 5th Avenue, New York, on 29 January 2026.
If your team is building skills for finance-focused automation, explore curated resources here: AI tools for finance.
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