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Agnostic Finance
Use Case

Bank Statement to General Ledger Reconciliation

An AI agent that retrieves bank statements, matches transactions with entries in the general ledger, identifies discrepancies, suggests matching rules, and prepares a reconciliation report.

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Process Details

Inputs

Bank statements, General ledger transaction data

Outputs

Bank reconciliation report, List of outstanding items and discrepancies, Suggested journal entries for bank fees/interest

Systems

ERP System (SAP, Oracle NetSuite, Microsoft Dynamics 365), Online Banking Platforms (APIs)

The Challenge

Manual processes
create real problems.

  1. 1

    High potential for costly errors from manual data handling.

  2. 2

    Significant time and resources are spent on repetitive, low-value work.

  3. 3

    The manual process is difficult to scale without increasing headcount.

  4. 4

    Process bottlenecks lead to delays and missed deadlines.

The Solution

Describe it in English.
It runs deterministically.

  1. 1

    Data Acquisition

    It retrieves bank statements (Excel) and transaction data from the ERP System's General Ledger

  2. 2

    Transaction Matching

    Applies pre-defined rules to match bank transactions with GL entries based on amount, date, reference numbers, and descriptions

  3. 3

    Exception Identification & Categorization

    Identifies unmatched items, outstanding checks, deposits in transit, bank fees, and interest. Categorizes discrepancies (e.g., timing differences, errors, unrecorded transactions).

  4. 4

    Automated Journal Entry Suggestion

    For identified bank fees or interest, the AI agent can suggest journal entries to be posted in the ERP System

  5. 5

    Reporting & Review

    Generates a reconciliation report highlighting matched items, outstanding items, and exceptions. Routes the report and exceptions to an accountant via email for review and approval

Primary Benefits

What you gain with
Kognitos automation.

Increase Efficiency

Dramatically reduce the time and manual effort required to complete the process.

Enhance Accuracy

Eliminate human error to ensure data integrity and reduce financial risk.

Empower Employees

Free your team from monotonous tasks, allowing them to focus on strategic work that requires their expertise.

Improve Scalability

Handle growing volumes of work without a proportional increase in operational costs.

Ensure Transparency

Maintain a complete, auditable trail of every action the AI agent takes, described in plain English.

FAQ

Common questions
answered.

The agent is designed for flexibility. It can natively integrate with major ERP systems (e.g., SAP, Oracle NetSuite, Microsoft Dynamics 365) via APIs. For data acquisition, it can process files like Excel, CSV, and PDFs from any banking institution, and it can be configured to retrieve them from secure FTP sites, email inboxes, or network drives.
The matching logic is highly customizable. While it comes with pre-built rules for matching by amount, date, and reference numbers, you can easily configure more sophisticated, multi-tiered logic.
The agent enhances your audit and compliance posture. Every action taken by the agent—from data retrieval to a matched transaction to a generated report—is logged in an immutable audit trail. The final reconciliation reports provide clear documentation of matched items, outstanding items, and all exceptions, giving auditors a complete and easily verifiable record of the process.
No, it does not. To ensure strict financial governance and maintain a human-in-the-loop control, the agent suggests journal entries for items like bank fees and interest. These suggestions are then sent to a designated accountant for review and approval before anything is posted to the General Ledger. This maintains the principle of segregation of duties.
A standard implementation/configuration can be completed in as little as 2-4 weeks. The process involves an initial discovery session to understand your specific ERP, chart of accounts, and bank statement formats. The primary requirement from your team will be access to a subject matter expert from your finance department and light support from IT for system access and credentials.
The agent is built on a cloud-native architecture, making it highly scalable. It can process tens of thousands without a degradation in performance. You can run reconciliations for multiple legal entities or bank accounts in parallel, ensuring the solution grows seamlessly with your business needs.
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Challenges

Solution

This use case solution follows these general steps at a high level:

  1. Data Acquisition It retrieves bank statements (Excel) and transaction data from the ERP System's General Ledger
  2. Transaction Matching Applies pre-defined rules to match bank transactions with GL entries based on amount, date, reference numbers, and descriptions
  3. Exception Identification & CategorizationIdentifies unmatched items, outstanding checks, deposits in transit, bank fees, and interest. Categorizes discrepancies (e.g., timing differences, errors, unrecorded transactions).
  4. Automated Journal Entry Suggestion For identified bank fees or interest, the AI agent can suggest journal entries to be posted in the ERP System
  5. Reporting & Review Generates a reconciliation report highlighting matched items, outstanding items, and exceptions. Routes the report and exceptions to an accountant via email for review and approval

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