Bank Reconciliation and Financial Control in Production with O137
How a multi-entity group automated bank reconciliation across multiple accounts, banks, and currencies while maintaining full control, traceability, and audit readiness.
Client Context
A multi-entity group (retail, services, marketplace, or industrial) managing:
High volume of financial flows:
- •Customer payments
- •Supplier transfers
- •Direct debits
- •Refunds
- •Commissions
Reconciliation was partially automated, but:
- • Rules were scattered
- • Exceptions were exploding
- • Teams spent time verifying, correcting, and justifying
The Problem
1. Fragile and Uncontrolled Reconciliations
Current systems rely on:
- •Rigid rules
- •Local scripts
- •Sometimes isolated AI
Result:
- • Incomplete matching rates
- • Inconsistent decisions
- • Dependency on accounting teams
2. Lack of Traceability and Control
For each reconciliation, it's difficult to answer:
- •Why was this payment reconciled this way?
- •Which rule was used?
- •Which model was involved?
- •Why did this case remain an exception?
Unacceptable in an audit / internal control / compliance context.
3. Explosion of Exceptions
- •Split payments
- •Slightly different amounts
- •Variable banking delays
- •Fees, commissions, exchange rates
- •Human errors
Teams spend more time on exceptions than on value-added work.
The O137 Solution
O137 is deployed as the central reconciliation control system.
1. Connection to Financial Sources
O137 connects to:
- •Bank statements (APIs / files)
- •ERP / accounting tools
- •PSPs (Stripe, Adyen, PayPal, etc.)
- •Internal billing systems
👉 Data remains in existing systems.
2. Orchestration of Matching Methods
For each flow, O137 orchestrates:
- •Strict rules (exact match)
- •Tolerant rules (variances, delays)
- •AI models for complex cases
- •Confidence scoring
Methods are combined, not opposed.
3. Contextual Decision
O137 decides:
- •If a reconciliation can be automated
- •If it should be proposed for human validation
- •If it should remain an exception
The decision takes into account:
- • Amount
- • Risk
- • History
- • Internal rules
- • Entity context
4. Multi-Model and Fallback
Depending on the case:
- •Fast model for standard flows
- •More precise model for exceptions
- •Automatic fallback in case of unavailability
No model lock-in.
5. Governance and Audit
Each reconciliation is:
- •Traceable
- •Explainable
- •Justified (rules + AI)
- •Historized
👉 Audit-ready by design
Results Observed
After implementation:
What O137 Enables
In Summary
Not a simple matching tool
Not a "magical" AI out of control
A controlled, explainable, and auditable financial decision system
"Automate reconciliation, without losing control."