How banks struggle with foreign bank account statements
Foreign income assessment still relies on PDFs. This creates cost, errors, and fraud risk in underwriting.
A foreign bank statement arrives as a PDF. The format is unfamiliar. Payment descriptions, which the bank relies on to classify transactions, are in another language. Income is not explicitly marked. Recurring expenses sit inside free-text entries.
An analyst reconstructs the applicant’s financial situation line by line, often translating transactions using tools like Google Translate’s camera feature. Banks we work with describe this as a standard step when assessing non-domestic applicants.
Under CCD2, banks are expected to assess creditworthiness using complete and verifiable information, including cross-border data. Many assessments still rely on manual document interpretation.
The issue is the lack of structure in how foreign income, liabilities and spending patterns are presented. This creates a process that is manual, error-prone and difficult to verify.
The problem: statements are not built for credit decisions
Bank account statements were designed for account holders, not for credit assessment.
They are:
- formatted differently across countries and institutions
- structured inconsistently
- dependent on language-specific transaction descriptions
For a domestic applicant, this rarely creates friction. Banks understand the language and data structures, and credit bureaus provide structured repayment history, existing obligations and long-term behaviour.
For a foreign applicant, that layer is often missing. The bank falls back on documents.
At that point, both accuracy and verification depend on manual review.
What this looks like in practice?
Foreign income is typically assessed from PDF bank statements. Transactions are reviewed line by line, income is identified from payment patterns, and transaction descriptions are translated where needed. Spending behaviour is approximated rather than clearly defined.
This process leads to inconsistent outcomes. Two analysts can reach different conclusions from the same statement. Income may be missed or overstated, and recurring obligations are not always recognised. At the same time, the bank has limited ability to verify whether the document is complete or authentic.
The issue is not the availability of data, but its format. Bank statements contain the necessary information, but not in a structure that can be used directly in underwriting. Income, obligations and spending must be interpreted rather than clearly identified.
When this data is structured before it reaches the underwriter, income is defined by type, source and amount, obligations are linked to lender and monthly payments, and expenses are grouped into categories. The underlying data remains the same, but the input becomes consistent and usable across cases.
Operational impact
This process does not scale.
As volumes increase:
- Risk teams spend time validating inputs
- Operations teams handle document-heavy workflows
- Decision times increase
Processing foreign applicants takes longer and introduces additional risk.
Longer cycles lead to drop-offs.
Unclear inputs lead to conservative decisions.
Manual handling increases the likelihood of errors.
❗What this means for credit assessment❗
Relying on foreign bank statements in raw PDF form creates structural weaknesses in the credit assessment process:
- High operational cost to process it manually
- High probability of errors in manual processing
- High fraud risk, as the PDF itself may not be authentic
For a core input into underwriting, this is a weak foundation.
The solution
Mifundo provides structured cross-border credit data by combining categorised bank statement data with credit bureau information.
The platform connects verified data from 20+ European countries, covering nearly 70% of Europe.
Banks receive data that:
- standardises bank statement data across countries
- categorises income, obligations and expenses
- combines this with credit bureau information
- fits into existing credit processes
Banks typically start with a defined segment and compare outcomes against their current process.
There is no API requirement to begin. Teams can access the data through a web interface and evaluate its impact before integration.
