For many financial executives, the monthly close is still a lengthy, 10-day process. What slows it down isn't the amount of data, but its complexity and inconsistency. The core problem lies in outdated financial software that is unable to handle these new data challenges.
This complexity gives rise to systemic operational failures that legacy, rule-based systems are simply not equipped to solve.
In this expert opinion, we spoke with Mykhailo Shakhov, Data Scientist at ELEKS, about why these challenges persist and how AI can fundamentally reshape the financial close process.
What are the main challenges in the financial close?
There are three main operational challenges that slow down financial close and create risk:
1. The multi-format & classification crisis
Banks face a digital mess of formats. Teams receive clean Excel files from some sources but PDFs, scans, and image files from others.
Template-based automation fails because it requires data to be structured and machine-readable. It treats document scans and images as unreadable pictures (pixels). Traditional systems lack built-in Optical Character Recognition (OCR) to convert these images into usable text, and even if they did, their rigid templates cannot understand inconsistencies like variable labels or misplaced data fields in different documents. This mandates manual intervention for classification and data extraction, slowing the process.
Employees must manually classify files into categories: Statement of Financial Position, Statement of Comprehensive Income, Statement of Changes in Equity, Statement of Cash Flows, or Notes to the Financial Statements.
Document structures are inconsistent. The Statement of Comprehensive Income might be complete or condensed. Breakdowns have no set format. Pattern scripts can't handle these variations, so teams must process everything manually, which slows the process.
2. Failure in entity-matching
This dependency on manual work creates a significant risk: data ambiguity. Legacy systems require exact-text matching, but the data they receive is "fuzzy" and fundamentally human-centric.
For example, an analyst knows that Apex Solutions, Apx Solutions, and Apex Solutions Inc. are likely the same entity. But they also know that Apex Consulting, a separate firm, is a different legal entity.
A rules-based algorithm can't make these nuanced distinctions. It either fails to match obvious variations like simple typos, or it incorrectly groups similar names like "Apex Consulting" with "Apex Solutions". The problem intensifies with abbreviations: ABC Mfg. Ltd. must be matched to ABC Manufacturing Limited, yet legacy systems can't recognise they're the same entity without exact character-by-character matching.
3. The consolidation mutual transaction deadlock
This "fuzzy data" makes automated consolidation impossible. Consolidation, in this context, is the critical accounting process of combining the financial statements of a parent company and its subsidiaries into a single, unified report. To do this accurately, mutual transactions (internal eliminations) must be subtracted before aggregation of the Statement of Financial Position and the Statement of Comprehensive Income.
But how can a system automatically eliminate transactions from "Apx Solutions" if the main ledger only recognises "Apex Solutions Inc."? It cannot identify the match.
The process stops, reverting to the manual queue, which is the core bottleneck that paralyses the monthly close.
How does an AI-driven consolidation engine solve these challenges?
A new approach is required. Instead of fragmented tools, a single, LLM-based engine can be engineered to manage the entire workflow. This AI-driven system:
- Ingests all formats: Reads scans, images, PDFs, and Excel in one unified process, leveraging intelligent document processing.
- Classifies and understands documents: Identifies "free-form" breakdowns and trial balances without rigid templates.
- Executes contextual matching: Correctly matches Apx Solutions to Apex Solutions while distinguishing them from Apex Consulting, and links abbreviations to their full names.
- Embeds accounting logic: Automatically identifies and eliminates mutual transactions, then verifies that eliminations balance across accounts (e.g., checking that intercompany receivables match intercompany payables).
The result is a unified consolidation dashboard that accelerates monthly close, reduces manual effort, and improves accuracy. This is not blind automation. Large language models can still make errors with low-quality scans, so expert validation remains essential.
What value does AI bring to the financial close process?
The value lies in augmentation: the AI engine presents a final table highlighting all transactions, matched counterparties, and suspected errors, essentially serving as a powerful assistant.
The human-in-the-loop (HITL) approach is crucial for this solution, meaning analysts review the AI's output, validate complex judgements, and handle exceptions (like low-quality scans), ensuring data integrity. While HITL significantly reduces human workload and error risk, it is also a kind of disadvantage, as it confirms the system cannot yet achieve true, unsupervised end-to-end intelligent automation.
Nevertheless, the solution dramatically transforms the process from 10 days to a few hours by providing a single, consolidated picture instead of many unstructured results, thereby eliminating tedious manual matching.
How will AI-driven financial close continue to evolve?
Looking ahead, the artificial intelligence engine will evolve to incorporate predictive analytics. This will enable it to forecast cash flow fluctuations and future risks based on consolidated data. Over time, it will also advance toward continuous accounting, providing real-time insights instead of a delayed monthly snapshot.
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