For finance teams managing high-volume transactions, bank reconciliation is manual, time-consuming, and error-prone.
It also never really stops.
Payments flow in daily from multiple banks, platforms, and processors. Matching those transactions against ERP records, identifying variances, and resolving exceptions still takes hours of spreadsheet work, inbox digging, and cross-team follow-ups.
Most finance teams do a great job maintaining accuracy but struggle with capacity and speed.
This guide outlines how to solve the problem with AI, utilizing Ledge. We discuss an approach that combines daily ingestion, intelligent matching, and embedded exception handling to reduce manual work and make reconciliation a continuous process.
Why bank reconciliation with AI?
In a recent survey that Ledge fielded to finance leaders at companies with high-transaction volumes, respondents shared that account reconciliation is the most time consuming task each month (approx. 20-50 hours per month using 3-5 systems).
At its core, bank reconciliation is a matching problem. Processes break down when data is inconsistent, incomplete, or misaligned across systems.
AI, using large language models (LLMs), solves this problem.
Instead of relying solely on exact field-level logic, LLMs recognize patterns across unstructured data, infer relationships between entities, and apply contextual understanding to match records that traditional rules miss.
Here's how it works in practice, with 7 detailed steps to automating bank reconciliation with AI.
1. Ingest transactions daily
Bank reconciliation only works if you’re working with the full picture. But with disconnected bank feeds, delayed file uploads, or batch-based systems, teams are often working around unreliable or incomplete bank feeds.
AI resolves this problem by shifting operations towards continuous accounting—reconciling throughout the month rather than backloading everything to period-end.
Using LLMs, solutions like Ledge ingest transactions continuously across banks, ERPs, PSPs, and billing systems. Your team can work off live data, not exported files. That means you can spot missing deposits, catch misapplied payments, and track inflows and outflows in real-time.
2. Standardize messy payment data
One of the biggest blockers to automation isn’t the volume. It’s the formatting. Bank memos are inconsistent. Payouts are bundled. ERP references don’t always match. And different systems call the same thing by different names.
Ledge handles this complexity at the point of ingestion. AI is used to enrich and standardize payment data—mapping formats, aligning metadata, and unpacking bundled payouts, netted fees, or FX-driven discrepancies.
3. Match transactions across systems with context-aware AI
Most ERPs match on exact values: amount, date, reference number. But that logic breaks when the amount is off due to fees, or the memo references a parent company, or the payment date is slightly misaligned.
Ledge uses AI to bridge that gap, matching transactions based on historical behavior, entity mapping, payment patterns, and unstructured context like remittance fields or attachments.
What would take a human minutes per line item, Ledge can resolve in seconds.
4. Surface exceptions with everything you need to resolve them
Flagging a mismatch is easy. Resolving it isn’t. Most platforms leave finance teams chasing invoice numbers, digging through remittance emails, or calling other departments for missing context.
Ledge doesn’t just flag issues. It surfaces the supporting data needed to explain and fix them. When something doesn’t match, Ledge shows the related remittance information, attached invoice, and past transactions so your team can take action immediately.
5. Use AI to suggest and post adjusting entries
Not every discrepancy is a matching error.
Some require reclassification, accruals, or adjusting journal entries, and these adjustments are often delayed or missed when teams rely on manual processes.
Ledge applies AI to detect common adjustment patterns based on historical data and accounting logic. It recommends journal entries for your team to review, approve, and post in-platform without needing to export to Excel, rewrite descriptions, or chase approvals.
6. Reconcile at scale across entities and currencies
Multi-entity businesses face a harder challenge: same counterparties showing up under different names, payments landing in the wrong entity, or FX differences that complicate matching.
Ledge uses AI to understand organizational structure and currency logic. The platform automatically groups transactions by entity, flags and tags intercompany flows, and accounts for FX-driven variances in order to produce accounting-ready outputs that eliminate hours of manual review.
7. Keep a complete audit trail, by default
Audit prep shouldn’t require recreating decisions. But in many finance teams, that’s exactly what happens—weeks or months after the fact.
Ledge tracks every match, exception, and adjustment with full context, user attribution, and time stamps. Everything is explainable, traceable, and reviewable, even months later.
The result: real-time reconciliation that scales with your business
With Ledge, bank reconciliation becomes part of your daily operations. It is no longer a manual, backloaded task that clogs up the close.
You get:
- Daily visibility into matched and unmatched transactions
- Fewer delays due to data gaps or recon backlogs
- Embedded control and audit readiness without the lift
It’s not just faster. It’s cleaner, more resilient, and ready for scale.
Book a demo to see how.
