Infusing AI into Journal Entry Process - Beyond Automation

[SrinathGanesan]

Background:

During critical periods such as month-end, quarter-end, or year-end close, organizations often face a high volume of manual journal entries being posted into their S/4  systems. These entries can sometimes contain errors, especially when they are entered by various users who might not have a consistent understanding of the required formats, values, or related documentation.

Currently, these manual entries are posted directly into the system without prior checks, leading to potential discrepancies, accounting errors, or compliance violations. These errors could include incorrect amounts, incorrect GL codes, misaligned descriptions, missing or inaccurate supporting documentation, and others. To mitigate this risk, a more streamlined and controlled process is needed where the journal entries are parked first, reviewed for validation, and then posted after being confirmed as correct.

Key Issues:

1. Manual Journal Entry Errors: These include issues such as:
• Incorrect amounts posted for a particular entry type (e.g., a cost that is typically $5,000 being entered as $500).
• Incorrect General Ledger (GL) codes or descriptions (e.g., the GL code does not match the standard or policy documentation).
• Missing or incorrect attachment references (e.g., an invoice or supporting document is either not attached or is attached to the wrong entry).
• Missing or incomplete information (e.g., missing approval, insufficient details for audit).
2. Lack of Validation Prior to Posting: Since the journal entries are directly posted into the system, there is no opportunity to detect these errors before they are committed to the books, leading to potential audit issues, compliance risks, and manual corrections that are costly and time-consuming.

Solution Objective: To streamline this process, the goal is to implement a system where all manual journal entries are first "parked" (held temporarily) in the system, rather than posted immediately. During this parked state, the entries can undergo a series of automated checks or validations, leveraging historical data, company policy documents, and attachments to ensure accuracy and compliance. The system can then generate alerts or notifications to flag any anomalies, providing users with the opportunity to correct them before final posting.

Key Components: A Symphony of Technology

SAP BTP AI Services

Leverage pre-built AI models for document analysis, text extraction, and machine learning. These services enable intelligent data processing and automation.

S/4HANA

The core ERP system provides access to real-time financial data, enabling accurate and efficient journal entry creation and posting.

Data Pipelines

Securely transport data between BTP AI and S/4HANA, ensuring smooth and reliable integration of processed data for journal entries.

AI-Enabled Validation System:

An AI-enabled solution would help in detecting these anomalies by analyzing historical data, policy documents, and attachment details. The AI system would focus on the following validation areas:

1. Amount Validation:
• Historical Amount Comparison: AI can learn typical amounts for certain types of journal entries based on historical data. For example, if a specific type of entry (e.g., payroll) typically has an amount in the range of $4,500 to $5,500, the system can flag entries that are too high or too low.
• Amount Consistency: Cross-checking the amounts with similar entries from previous periods or other business units to ensure consistency.
2. GL Code Validation:
• GL Code Matching: Ensuring that the correct GL code is used based on the entry type. If an entry for "supplies" is posted under a GL code used for "payroll", it can be flagged.
• GL Code Description Match: The AI can validate that the description associated with the GL code aligns with the standard naming convention or description used in the policy documents.
• Cross-checking with Financial Policies: Validation against internal financial policies or specific rules regarding what types of entries can use which GL codes.
3. Description Validation:
• Attachment Match: The AI can compare the GL description with the content of the attached document to ensure they are consistent. For example, if the entry refers to "Consulting Fees" but the attachment shows "Travel Expenses", the system can flag this discrepancy.
• Standard Description Check: Ensuring that entries match the standard descriptions specified in internal accounting guidelines or corporate documentation.
4. Attachment Validation:
• Document Existence Check: Ensuring that required documents (e.g., invoices, contracts, receipts) are attached. If they are missing, an alert is generated.
• Attachment Type Matching: The system can check whether the correct type of attachment is associated with the entry. For example, if the entry refers to a "Vendor Payment," the attached document can be an invoice or a vendor agreement.
• Document Consistency: Comparing the details in the attachment (e.g., amounts, dates) with the amounts or other details in the journal entry to verify consistency.
5. Approval Workflow Validation:
• Approval Matching: Ensuring that all journal entries have been approved according to the organization’s internal controls. The AI can validate whether the appropriate manager or department head has provided the necessary approval for the entry type.
• Approval Policy Check: Cross-checking entries against approval thresholds defined by the organization (e.g., entries above $10,000 must have senior management approval).
6. Data Completeness:
• Missing Information: Identifying any missing or incomplete fields in the journal entry. For example, ensuring that the "reason for entry" field or "cost center" field is populated.
• Consistency with Historical Data: Cross-checking the current entry against similar entries in historical data to ensure that the same fields are populated appropriately.
7. Audit & Compliance Validation:
• Regulatory Compliance: Validating that the journal entry complies with relevant accounting regulations (e.g., GAAP, IFRS). The AI system can cross-check entries for common non-compliance patterns.
• Audit Trail: Ensuring that there is a clear and complete audit trail for all parked journal entries, including details on who made the entry, when it was parked, and who approved it.

Solution Approach

1. AI and Machine Learning Models:
• Use machine learning algorithms that can be trained on historical data, previous journal entries, and standard accounting practices to recognize patterns of typical errors and anomalies.
• The system can apply supervised learning techniques, using labeled data (e.g., previous correct and incorrect journal entries), to improve accuracy over time.
2. Natural Language Processing (NLP):
• To validate descriptions, attachment content, and other free-form text fields, NLP techniques can be applied to identify mismatches between the journal entry and the attached documentation.
3. Integration with S/4  Systems:
• The solution can seamlessly integrate with existing S/4  or accounting systems to "park" the journal entries and raise alerts automatically when discrepancies are found.
4. Automated Alerts and Notifications:
• Once anomalies are detected, the system can send automated alerts to relevant personnel (accountants, managers, auditors) with clear descriptions of the issue and suggested corrections.

Key Benefits

• Improved Accuracy: By automating the validation of manual entries before posting, the AI system can significantly reduce the likelihood of errors.
• Cost and Time Savings: Automating the review and validation process saves time and money that would otherwise be spent on manual corrections and audits.
• Compliance and Risk Mitigation: Ensures that all entries comply with financial regulations and internal policies, reducing the risk of non-compliance and potential penalties.
• Audit Readiness: The system maintains a comprehensive log of all validation checks, providing a clear audit trail for future reviews.

Type of Journal Entry	Description	S/4HANA AI Automation
Standard Entries	Monthly accruals for expenses like utilities or salaries.	S/4HANA uses AI to predict and automate recurring standard entries based on historical data, improving efficiency and accuracy.
Recurring Entries	Lease payments or subscription fees that occur regularly.	AI algorithms in S/4HANA identify patterns and automate the setup and posting of recurring entries, reducing manual intervention.
Adjusting Entries	Year-end adjustments for prepaid expenses or inventory changes.	S/4HANA's AI capabilities assist in suggesting necessary adjustments and automate postings based on predefined rules and criteria.
Accrual Entries	Accruing unpaid invoices for services received but not yet billed.	AI in S/4HANA predicts and accrues expenses and revenues, ensuring timely recognition and minimal manual oversight.
Reclassification Entries	Reclassifying expenses from one department to another.	AI tools help automate reclassifications by analyzing transaction data and suggesting necessary reallocations.
Tax-related Entries	Journal entries related to tax provisions or deferred tax liabilities.	S/4HANA utilizes AI to ensure tax compliance by automating complex calculations and adjustment entries according to tax laws.
Adjustments for Errors	Correcting a previously recorded entry due to misclassification or numerical error.	AI-driven error detection and correction features in S/4HANA help identify discrepancies and propose corrections efficiently.

In conclusion, implementing an AI-powered validation system for parked manual journal entries will streamline the accounting process, reduce errors, ensure compliance, and save significant time and effort during month-end, quarter-end, and year-end closing periods.

Srinath Ganesan