Analysis of the FiDA Proposal from a Credit Rating Agency Perspective

The proposed Financial Data Access Regulation (FiDA) aims to enhance competition and innovation in the European financial market by facilitating data sharing. However, the Deutsche Kreditwirtschaft (DK) has raised significant concerns regarding its implementation, arguing that it imposes excessive burdens on financial institutions and lacks a well-defined scope. From the perspective of a credit rating agency (CRA), the proposed regulation presents both risks and opportunities in assessing creditworthiness and maintaining data integrity.

Potential Benefits of FiDA for Credit Ratings

1. Increased Data Availability:

• FiDA could provide credit rating agencies with broader access to financial data, improving the accuracy of risk assessments.

• More comprehensive financial data from banks and financial institutions could enhance predictive models for credit scoring.

2. Standardization of Financial Data:

• If FiDA establishes uniform data-sharing standards, CRAs may benefit from improved comparability between different institutions.

• Standardized financial records could reduce discrepancies in credit assessments and enhance market transparency.

3. Enhanced SME Credit Evaluation:

• Small and medium-sized enterprises (SMEs) often face challenges in accessing credit due to limited financial histories.

• If FiDA enables structured access to SME financial data, CRAs could better assess their creditworthiness, potentially expanding financing opportunities.

Concerns from a Credit Rating Perspective

1. Risk of Data Misinterpretation:

• The DK warns that FiDA could lead to the use of incomplete or misinterpreted financial data, resulting in inaccurate credit assessments.

• Inconsistent or outdated financial records could undermine the reliability of rating models.

2. Regulatory and Compliance Challenges:

• The DK highlights that FiDA does not sufficiently account for existing financial regulations, such as MiFID II, which mandates that certain data must be collected directly from clients.

• If CRAs are required to rely on data sources that lack regulatory validation, there is a risk of non-compliance with existing EU financial regulations.

3. Increased Operational Costs:

• Financial institutions and CRAs may need to invest in new IT infrastructure to integrate and analyze the additional data flows mandated by FiDA.

• The DK argues that a broad and undefined scope of data-sharing obligations could lead to excessive costs without guaranteeing clear benefits.

4. Competitive Disadvantages for European Firms:

• The DK raises concerns about the security of sensitive financial data, especially regarding potential access by non-EU companies.

• If FiDA does not include sufficient safeguards, European financial institutions may be at a competitive disadvantage compared to global players who are not subject to similar regulations.

Key Recommendations for FiDA Revision

• CRAs would benefit from a more targeted approach, focusing on essential financial data rather than including all customer-related records.

• The DK suggests limiting FiDA to standardized financial data that can be objectively assessed.

2. Maintaining Control Over Proprietary Risk Models:

• The DK warns against mandatory data-sharing that could interfere with proprietary credit risk assessment models used by banks and CRAs.

• Ensuring that financial institutions retain control over sensitive risk metrics would help maintain market confidence.

3. Phased Implementation and Pilot Programs:

• Given the complexity of financial data-sharing, a gradual rollout of FiDA could allow for adjustments based on initial findings.

• Pilot programs involving select financial institutions and CRAs could test the feasibility of the regulation before broader implementation.

4. Stronger Security and Compliance Measures:

• A clear framework should be established to protect financial data from unauthorized access and misuse.

• Aligning FiDA with existing regulatory frameworks like GDPR and MiFID II would help prevent conflicts in data governance.

Conclusion