Compliance Checks of the Internal Policy Framework

AI-supported compliance checks enable the automated review of internal policies, processes and documentation against regulatory requirements. The solution combines document analysis, rule matching and structured gap analysis in one integrated system. The aim is to systematically identify deviations, create transparency and improve the consistency of policies and requirements.

The internal policy framework in financial institutions is often characterized by grown structures and heterogeneous documentation. Internal policies, work instructions and processes are regularly adapted to new regulatory requirements, but often in decentralized structures and with high manual effort. The comparison between internal requirements and regulatory requirements is often carried out manually and is correspondingly time- and resource-intensive.

The efficient assurance of compliance in the internal policy framework is hindered by several factors:

• Increasing regulatory requirements and frequent changes
• Fragmented policies and inconsistent documentation
• High manual review and coordination effort
• Limited transparency about deviations and implementation status
• Lack of standardization across organizational units

The consequences are inefficient review processes, inconsistencies in the implementation of regulatory requirements, and increased risks in the context of audits and inspections. The AI-based solution automates the comparison between internal requirements and regulatory requirements and enables a structured gap analysis:

• Automated rule matching: Internal policies, processes and documents are systematically compared with regulatory requirements.
• Structured gap analysis: Each regulatory section is assessed and classified (e.g. "conform" vs. "deviation").
• Detailed justification and traceability: Each assessment is complemented by a transparent analysis and traceable decision logic.
• Context-based assessment: Domain-specific knowledge, internal structures and risk profiles are integrated into the assessment in order to ensure well-founded results.
• Integration of best practices and expert logic: The analysis follows a structured, section-by-section review approach analogous to the procedure of compliance experts.
• Support for deriving measures: Identified gaps are transformed into concrete recommendations for action to adapt policies and processes.

The result is reduced manual effort, increased transparency, consistent implementation of regulatory requirements, as well as a standardized and scalable internal policy framework.

The AI Compliance Checker analyzes internal rule sets and regulatory requirements in a structured, multi-level review and assessment logic.

• Input layer (data integration): The system processes regulatory requirements as a reference basis, as well as internal policies, processes and documentation as the content to be reviewed. In addition, contextual information such as organizational structure, priorities and risk profiles is incorporated.
• Document parsing and structuring: The incoming documents are broken down into analyzable units and structurally prepared, so that individual regulatory requirements can be specifically mapped to individual sections of internal documents.
• Section-by-section comparison engine: The AI carries out a systematic section-by-section comparison between regulatory requirements and internal requirements. The review approach of compliance experts is reproduced in order to ensure a consistent assessment.
• Gap classification and scoring: Each section is classified with regard to its conformity (e.g. "gap present" or "no gap"). In addition, an assessment with regard to relevance or risk can be carried out.
• Explainability and evidence layer: The results are complemented by detailed justifications, references to relevant text passages and traceable decision logic, in order to ensure transparency and auditability.
• Human-in-the-loop integration: Compliance experts can review, validate and, if necessary, adjust the results. This ensures the quality of the analysis and builds trust in the solution.
• Output generation: The results are prepared in a structured gap analysis, complemented by overview presentations of coverage, deviations, as well as possible measures to remedy identified gaps.

Under the EU AI Act, an AI-supported compliance check of the internal policy framework can, in this form, be interpreted as not high-risk; depending on the use and role, however, transparency requirements (Chapter IV) could apply. Since the system produces assessments that appear legal in nature, the requirements for AI literacy (Art. 4) are particularly relevant for the employees using it.

• Human oversight and traceability: Where high-risk AI requirements apply in a specific case, the human-in-the-loop validation and the explainability layer (source citations) already address central requirements.
• Art. 4 – AI Literacy Obligations: Because the system produces assessments that appear legal in nature, AI literacy is particularly relevant for the employees using it.

Under the GDPR, insofar as the reviewed policies and documents contain personal data (e.g. names of responsible persons), the legal basis, purpose limitation and data minimization (Art. 5, 6) must be observed. Since every assessment of the AI is validated by a human, there is no solely automated decision within the meaning of Art. 22. When using an external AI/model provider, a data-processing agreement (Art. 28) as well as data security and residency (Art. 32) must be observed.

The frameworks mentioned partly interlock; scope and specific obligations depend on the type of company, the role (provider/deployer), the implementation of the AI use case and the risk class. This must be examined in every case.

AI only delivers real added value in the financial sector when it is not only useful but at the same time compliant and trustworthy. This is exactly where BearingPoint and trail work together: BearingPoint brings the specialist industry expertise and consulting to identify and implement the right, value-generating AI use cases; trail delivers the technical structures to bring AI into operation quickly and in a compliant manner.

Talk to us if you want to implement AI solutions that deliver real added value while also standing up to regulatory requirements.