Obol partners with Mastercard to bring AI-driven cash flow management to Australian businesses
A new partnership between Obol and Mastercard signals a push to make cash flow management faster, clearer, and more predictive for Australian businesses. For managers, this is about better visibility and tighter control over working capital without adding headcount or overhead.
Details are light, but the direction is clear: AI that forecasts cash positions, spots risk early, and suggests the best timing for payables and receivables. If your team is still juggling spreadsheets and manual reconciliations, this is a moment to reassess your finance stack.
Why this matters for management
- Stronger cash position: AI can flag shortfalls sooner and recommend steps to protect runway.
- Cleaner operations: Expect fewer manual touchpoints across invoicing, reconciliation, and payment runs.
- Decision support: Scenario planning that shows the impact of changing payment terms, discounts, or credit risk.
- Faster close: Automated matching and exception handling can shorten monthly close cycles.
What to expect from AI-driven cash flow tools
- Forecasts that update as new invoices, payments, and sales data land.
- Payment timing suggestions that weigh discounts, fees, and supplier relationships.
- Anomaly and risk alerts for late payers, unusual activity, or data gaps.
- Dashboards that surface the few numbers that actually matter day to day.
How to prepare your finance stack
- Data hygiene: Standardise chart of accounts, supplier records, and invoice fields. Inconsistent data kills model quality.
- Integrations: Confirm APIs/connectors for your ERP, accounting platform, bank feeds, and payment gateways.
- Access control: Map roles and permissions before rollout to avoid shadow processes.
- Policies: Define rules for approvals, payment windows, and early-payment discounts the system can follow.
- KPIs: Set baseline metrics (DSO, DPO, forecast accuracy, close time) to measure gains.
Questions to ask your provider (and your team)
- What data sources does the model use, and how often does it refresh?
- How is forecast accuracy measured and reported over time?
- What controls exist for approvals, overrides, and audit trails?
- How are Australian privacy requirements handled for customer and supplier data?
- What does rollout look like for a 90-day pilot with clear success criteria?
Practical 30-60-90 day plan
- Days 1-30: Clean data, connect systems, define policies and KPIs. Pilot with one entity or business unit.
- Days 31-60: Expand to core vendors/customers. Enable alerts and approval flows. Track forecast accuracy weekly.
- Days 61-90: Automate payment runs under thresholds. Review impact on DSO/DPO. Lock in governance and audit trails.
Risk and governance checklist
- Model drift: Schedule quarterly reviews of forecast accuracy and retraining plans.
- Bias and gaps: Validate that recommendations don't unfairly penalise smaller suppliers.
- Privacy: Align with the Australian Privacy Principles. Minimise personal data exposure.
- Vendor lock-in: Ensure clean data export and clear off-ramp terms.
Australia-specific context
Cash flow discipline is under the microscope, especially for mid-market firms working with larger payers. Tighter payment practices and transparency are increasingly expected.
If supplier terms are a pressure point, review your compliance posture against the national reporting framework for payment times. It's a useful benchmark for internal SLAs and supplier communication.
What to do next
- Set up a pilot: Pick one entity, five top suppliers, and a clear KPI (e.g., +10% forecast accuracy, -5 days close).
- Upskill your team: Give finance and ops leads a working playbook for AI in cash flow and automation. Explore curated options here: AI tools for finance and courses by job.
- Run a risk review: Privacy, auditability, and approval workflows first-then automate.
Partnerships like this point to a simple truth: the companies that treat cash visibility as a daily habit, not a month-end scramble, win. Put the plumbing in now, and let AI do the heavy lifting where it actually counts.
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