From Documents to Reliable Financial Data

Electronic document exchange isn’t just about receiving and sending documents. The real value comes when the data in those documents is accurate, complete, and usable.

An invoice can be received without any technical issues and still contain incorrect information. The VAT number may be missing, the Chamber of Commerce number may not match the supplier’s name, a bank account may differ from the master data, or an order number may not be found in the ERP system.

Data validation therefore serves as a crucial layer between document exchange and further financial processing. Especially when documents are already being converted to an internal format, this presents a logical opportunity to centrally verify data and enrich it where possible.

Normalize first, then validate

Documents can arrive in various formats and structure the same data differently each time. It is therefore inefficient to develop validation logic separately for each format.

A logical approach is to first convert incoming documents to a single canonical or internal data model. Within this model, a supplier name, VAT number, or invoice amount always has the same meaning and position, regardless of the original format.

Afterward, the same validation logic can be applied to all documents:

receive → convert → validate → enrich → convert to output format → deliver

This decouples validation from format and transport channel. A check for, say, a Chamber of Commerce number, IBAN, or VAT number only needs to be set up once and can then be applied to every document stream.

The same principle applies to outgoing documents. The ERP system provides data in the agreed-upon internal format. That data is first checked and, if necessary, enriched, and only then converted to the format and channel required by the recipient.

The central data model thus serves not only as an intermediate format for conversion but also as the central point from which data quality can be monitored.

Validation Against Fixed Rules

The first layer of data validation consists of checks that can be performed directly on the data itself.

For example, a system can verify whether required fields are present, whether dates are valid, and whether identification numbers conform to the required structure. For invoices, financial checks can also be performed.

• Does the total of the invoice lines add up correctly?
• Do the net, VAT, and gross amounts match?
• Is the invoice number present?
• Is a valid currency used?
• Does the VAT number have a valid structure?
• Is the IBAN technically valid?
• Are the required company and address details included?

Many of these checks are entirely deterministic. Nothing needs to be interpreted: a calculation is either correct or incorrect, a required piece of information is either present or missing, and an identification number either conforms to the required pattern or it does not.

For structured electronic documents, the rules of the relevant standard or the profile being used can also be applied. A document may be technically correct in its structure yet still fail to comply with the required business rules.

Validation therefore goes beyond simply checking whether a file can be processed technically. The relevant question is whether the data is substantively reliable enough to proceed further into the financial process.

Validation Against ERP and Master Data

Validation becomes even more valuable when data from a document is compared with information the organization already has on file.

For example, ERP systems and other source systems contain master data on suppliers, customers, contracts, orders, and legal entities. This allows for a much more targeted verification of whether a document actually aligns with the known financial reality.

For example, an incoming invoice can be compared with:

• supplier name and supplier number;
• Chamber of Commerce and VAT numbers;
• IBAN and other bank details;
• address and contact information;
• purchase order or contract;
• Currency and payment terms;
• the legal entity to which the invoice is addressed.

For example, an IBAN may be technically valid but still differ from the account number registered for the supplier in the ERP system. Technically, this is not a problem, but from a business perspective, it is a reason to review the invoice more closely.

The same applies to company identity. A supplier name alone is not always reliable enough. Companies may have similar names or spell their trade name differently from their legal name. By verifying the name, Chamber of Commerce number, VAT number, address, and bank account in conjunction, a much more reliable picture emerges.

Process data can also be validated. When an invoice refers to a purchase order, it is possible to verify whether that order exists, belongs to the same supplier, is still open, and logically corresponds to the invoiced goods or services.

This shifts validation from merely checking documents to verifying them within the context of the Purchase-to-Pay or Order-to-Cash process.

Third-party sources as an additional layer of verification

Not all relevant information needs to come from the company’s own ERP system. Data can also be verified against external sources.

Examples include business registries, VAT registries, address databases, bank references, or commercial data sources. This allows you to verify, for example, whether a Chamber of Commerce number exists, whether a VAT number is valid, whether an organization is still actively registered, and whether the name and address match official records.

External sources are particularly valuable when a supplier or customer is new and the company’s own master data is still limited. They can also be used to periodically verify existing data.

It is important to distinguish between validation and overwriting. If an external source shows a different company name or address than the ERP system, this does not automatically mean that the external value should be adopted without verification.

A discrepancy is, first and foremost, a warning sign. Only then can it be determined which source takes precedence and whether an adjustment is necessary.

This is also important from a governance perspective. For financial data, it must always be traceable where the information came from, what checks were performed, and on what basis any data adjustments were made.

From Validation to Enrichment

The same infrastructure that validates data can also enrich it.

Validation primarily asks: Is this data correct? Enrichment adds a second question: What reliable information can we add so that the document can be processed further?

For example, an invoice contains a supplier name and VAT number, but not an internal supplier number. If the supplier can be unambiguously linked to the master data, that internal number can be added automatically.

In the same way, a contract number, internal customer code, legal entity, purchase order reference, cost center, or item code can be added.

Enrichment can also involve standardizing data. A company name may appear in different spellings, an address may be structured differently, and country codes may be provided in various formats. By normalizing this data to a single internal standard, comparison and further processing become easier.

However, it is important to distinguish between factual enrichment and interpretive enrichment.

Adding a supplier number based on an exact match with the VAT number can be deterministic. Predicting a general ledger account based on historical invoices is not. AI can play a role here, but the result is an interpretation or probability estimate, not an established financial fact.

A reliable architecture keeps these two types of enrichment separate. Objective data can be added automatically, while interpretive results can be proposed, verified, or processed within predefined frameworks.

A single validation layer for the entire document flow

The strength of this approach ultimately lies in centralization. Since documents are already received, converted, and forwarded via an exchange layer, that same layer serves as a logical point for monitoring data quality.

An incoming invoice can first be converted to the internal model. There, amounts, company data, VAT information, bank details, and references are checked against fixed rules, master data, and, if necessary, external sources. Missing information can be supplemented where appropriate.

Only then does the document proceed to the ERP or Purchase-to-Pay process.

For outgoing documents, the process works exactly the opposite way. The ERP system provides the data, after which the same validation layer checks whether the information is complete and correct before the document is converted to the desired output format and sent.

As a result, the same checks apply across different document flows, trading partners, formats, and channels. Validation rules do not need to be rebuilt from scratch in ERP systems, integrations, or separate mappings.

This makes format conversion and data validation closely intertwined. Format conversion brings different documents back to a single common data structure. Validation and enrichment then ensure that the data within that structure is reliable and usable.

The result is not only improved document exchange but also a stronger financial foundation. Issues are identified before data proceeds further into the Purchase-to-Pay or Order-to-Cash process or reaches a trading partner.

In this way, data validation becomes not just a one-off post-processing check, but a structural part of the document flow: a single central layer where data is checked, compared, and, where possible, enriched before it is processed further.