Consumer credit: how Open Banking strengthens fraud prevention

Credit granting is a prime target for fraudsters. Identity fraud, falsified income documents, hidden account data, cyberattacks: the scenarios are varied, often combined, and increasingly sophisticated, especially with AI tools.

In the face of these threats, Open Banking, already recognized as a key lever for creditworthiness assessment, also serves as a powerful tool for reducing fraud risk. Here’s why.

1. The main fraud scenarios in consumer credit

Fraud risks are not uniform: they vary depending on the type of loan or financed product, the distribution channel, and the level of digital maturity of the origination journey. However, four major categories can be identified.

Identity fraud

This involves using the personal data of a third party, real or synthetic, to obtain credit in their name. In a fully digital journey, without physical interaction, identity verification based solely on document submission is easily bypassed.

Falsified income documents

Edited payslips, forged bank statements, altered tax assessments: financial document falsification is now accessible to anyone with basic digital editing skills. This risk exists across all consumer credit products, but is especially critical for higher loan amounts.

Concealment of bank account data

Distinct from document falsification, this type of fraud involves hiding part of the applicant’s financial reality to deceive analysis systems. Three increasing levels of sophistication can be distinguished:

• Hiding a poorly managed account: the fraudster does not connect the account showing payment incidents or low balance, and instead presents a “clean” account.
  
  
• Building a “perfect account”: through artificial transfers and payments over at least three months, the fraudster simulates stable income and healthy financial behavior.
  
  
• Creating a circular transfer network: in more organized cases, multiple accounts transfer funds among themselves to simulate incoming income. This is the most sophisticated and hardest to detect, often involving structured fraud networks.
  
  

Cyberattacks

Interception of personal data during application journeys, targeted phishing, or session hijacking are all attack vectors exposing institutions and customers, especially in fully digital processes.

‍

2. How Open Banking structurally reduces these risks

The fundamental contribution of Open Banking lies in the nature of the data it provides: data sourced directly from the customer’s bank via secure, regulated APIs, not self-declared by the applicant. By design, it is much harder to falsify.

On identity fraud

Open Banking relies on strong customer authentication through their banking app, using their trusted device. This provides a first layer of identity verification.

Beyond authentication, Open Banking enables verification that the account holder’s name matches the name declared in the application. This is a strong signal: a fraudster using someone else’s identity will generally not be able to access and authenticate on that person’s bank account.

Transaction data analysis also allows verification of geographic consistency between spending habits and declared address. For example, an applicant claiming to live in Bordeaux while all transactions occur in the Paris region is a relevant red flag.

On falsified income documents

This is likely the most direct benefit of Open Banking. When income is reconstructed from actual banking transactions (incoming transfers, regularity, employer labels), reliance on declared documents becomes largely unnecessary. The risk of falsified payslips or tax documents is mechanically eliminated for Open Banking-based cases.

On concealment of bank account data

This is where the inherent limitation of bank-data-based systems lies: sophisticated fraudsters may attempt to hide part of their financial situation rather than falsify documents. Open Banking mitigates this in several ways.

A short account history can reveal recently opened accounts often used in fraud. Internal transfers without visible counterparties suggest hidden accounts. Missing typical flows, regular income, card payments, direct debits, also raise alerts, as such accounts likely do not reflect the applicant’s true financial situation.

Beyond binary indicators, behavioral transaction ratios can detect outliers compared to reference populations: unusual proportions of incoming transfers, abnormal use of cash or checks, disproportionate outgoing flows. Individually, these signals are not proof of fraud, but combined they form an actionable suspicion score.

On cyber risks

Strong authentication on the customer’s active bank account, as implemented in PSD2-compliant Open Banking flows, provides robust protection against session hijacking and data interception.

‍

3. Going further: combining Open Banking data with other sources

Open Banking is a foundation, but its power increases significantly when combined with other verification sources.

Digital identity wallets

Solutions like France Identité replace the transmission of ID copies, intrinsically risky, with one-time, state-certified identity proofs that cannot be reused. This significantly improves security for remote processes while reducing data leakage risks.

Combined with bank account holder name verification via Open Banking, this creates two independent strong authentication sources: certified identity and banking identity. The likelihood of bypassing both is significantly reduced.

Telecom data cross-checking

Sending OTP SMS is common in digital journeys, but can be compromised via SIM swapping or call forwarding fraud. Combining Open Banking data with telecom operator services, verifying that the declared phone number matches the active SIM, strengthens reliability and reduces this attack vector.

Geographic cross-verification

Proof of address can be falsified, but spending patterns reveal real-life geography. Cross-checking declared address with geographic spending data from Open Banking transactions helps detect inconsistencies between claimed and actual residence.

‍

Conclusion

Fraud scenarios in consumer credit depend on the product type and distribution channel. A fully online revolving credit does not carry the same risks as an in-store auto loan. There is no universal solution, only a combination of measures tailored to context.

What Open Banking fundamentally changes is the quality and objectivity of the data used for risk assessment. By relying on real banking data that cannot be manipulated by the applicant, it structurally reduces several major fraud vectors, and provides a solid foundation for increasingly advanced complementary solutions.

‍

Charles Ozanne, COO at Algoan.