Bank Statement Analysis: How to Ensure Data Accuracy and Gain Better Financial Insights

Why is bank statement analysis crucial for businesses?

Analyzing bank statements reveals information that is often invisible at first glance. It provides a clear picture of your cash flow, spending habits and the overall financial health of your business.

By having this data, you can make better budgetary decisions, quickly detect possible anomalies and ensure safer and more responsive financial management.

This analysis process is an essential lever for effectively managing finances. It facilitates the monitoring of cash flows, the control of bank movements and the forecasting of working capital needs. However, in many structures, this task is still largely manual, time-consuming and unstructured. This lack of automation not only leads to time losses, but also increases the risk of errors and limits the ability to act.

Major financial and regulatory challenges

The challenge is not limited to saving time. Misinterpreting the readings may result in:

• Bank reconciliations errors
• Oversights in the follow-up of supplier or customer regulations
• A poor assessment of available cash flow
• Risks of non-compliance in the event of a fiscal audit or an external audit

At a time when financial transparency, anti-fraud or accounting justification obligations are being strengthened, it is crucial that data from bank statements be reliable, traceable and exploitable quickly.

What data can I extract from a bank statement?

To automate the analysis of a bank statement, it is imperative to precisely identify the data to be extracted by OCR. Although the layout may vary from one banking institution to another, the essential components remain generally similar.

This structured or semi-structured data is the basis for any reliable financial analysis.

A bank statement consists of two main parts: general account information And the detailed list of transactions.

Each line of operation presents key data such as:

• La date of the operation And the Value date ;
• The worded of the transaction (e.g.: “SEPA Transfer - Client X”);
• The mounting, positive (credit) or negative (debit);
• The transaction type : direct debit, card, check, bank transfer;
• The Intermediate balance, sometimes displayed after each line.

Analyzing a bank statement

Analyzing a bank statement is not limited to extracting transaction lines. To derive real financial value from it, several dimensions must be examined systematically.

Here are the fundamental elements to be integrated into any automated analysis process:

• Classification of transactions : Each transaction must be automatically categorized according to its nature, collection, disbursement, internal transfer, payment of expenses, etc.
• Expenditure analysis : It involves segmenting cash outflows (supplier payments, salaries, fixed costs, taxes) in order to identify the main cost items and monitor their evolution over time.
• Revenue tracking : Cash inflows, such as customer payments, refunds, or financial products, need to be traced to measure business performance and profitability.
• Evolution of assets and liabilities : The bank statement reflects cash flow variations, but also movements related to loans, investments, or loan repayments.
• Checking the balances : The final balance must correspond to the cumulative flow calculations to ensure accounting consistency and anticipate possible differences.
• Recognizing trends : Thanks to AI, it is becoming possible to detect recurring transactions (rents, subscriptions) or anomalies that may reveal fraud or an unexpected payment.

Key use cases for bank statement analysis

Error detection, compliance, and auditing

The automated analysis of bank statements makes it possible to have a detailed transaction history, essential to justify each flow during an audit or tax audit.

It also guarantees better reliability of accounting and tax declarations, while ensuring compliance with regulatory obligations (fight against fraud, internal compliance, RGPD, etc.).

Automated bank reconciliation

One of the most common uses is the reconciliation between bank data and internal records (accounting, ERP, invoices).

In the event of a discrepancy or error, the tool makes it possible to quickly identify the line in question, to avoid omissions or duplicates, and to correct anomalies before they generate accounting or fiscal errors.

Cash flow analysis

In-depth flow analysis makes it possible to:

• Identify regular expense items (salaries, subscriptions, recurring invoices)
• Track consistent revenue and identify spikes or irregularities
• Better anticipate overdrafts and adjust cash flow forecasts
• Highlight possible falsified or inconsistent statements, potential signs of fraud

This visibility allows financial departments to make more informed decisions and to secure short-term financial balances.

Risk assessment and solvency

Bank statements can also be used to assess the financial health of a third party: customer, supplier, or loan applicant.

The analysis of movements, receipts and expenses gives a clear picture of its repayment capacity, its commitments and its economic stability, useful in an approach to Know Your Supplier or the granting of credit.

Preparing for future commitments

Finally, this analysis makes it possible to verify whether the available cash is sufficient to cover future obligations (payment of social security contributions, suppliers, bank deadlines). It also helps identify unnecessary expenses, rationalize costs, and improve operational efficiency.

Categorizations of banking transactions

The extracted transactions are automatically classified into categories: salaries, supplier purchases, reimbursements, social security contributions, taxes, customer receipts, etc. This categorization can be based on:

• Deterministic rules (by keywords in the labels),
• Or on artificial intelligence models trained to recognize patterns.

This is a key step in building readable and understandable dashboards.

Barriers to automating bank statements

Despite technological advancements, analyzing bank statements continues to present some challenges. These are often linked to the heterogeneity of source data, to the quality of the documents or to the complexity of financial environments. Here are the most common obstacles and recommended solutions.

Open Banking: towards automated and secure access to bank statements

Automating the processing of bank statements can rely on two main ways of accessing data:

• extraction from documents sent by the customer (PDF, scan or image),
• or direct access to bank accounts via Secure Open Banking APIs.

Two approaches to accessing banking data

1. Manual transmission of statements (PDF, scan, photo)

In many cases, businesses or individuals prefer send their statements in the form of documents (downloaded from their bank space, or scanned). This is the most common method, especially when:

• The customer refuses to give bank access authorization,
• Access via API is not not technically available (bank not compatible),
• The context is punctual or limited (e.g. loan file, audit, control).

These documents must then be processed by recognition tools (OCR) to extract key information.

2. Direct access via Open Banking (API)

When the customer gives their explicit consent, the data from its bank accounts can be retrieved in an automated and secure manner, directly from financial institutions. This approach is based on European directives. PSD2, which strictly regulate:

• The perimeter of the access (read-only, limited time),
• The security of the connections (strong authentication, encryption),
• Traceability of accesses and treatments.

Benefits of Open Banking for statement analysis

Access via banking API has several concrete advantages:

Limits to take into account

Despite its numerous advantages, Open Banking is not yet established as a universal solution, in particular in the following cases:

• Some customers are reluctant to share banking access, even temporary, for reasons of confidentiality.
• Not all banks offer a compliant and stable API.
• The implementation requires a technical infrastructure specific to manage authentication, connections and data processing.