Financial authority participated in drafting report on enactment of fiscal regulations for the year 2000.
In the ever-evolving world of RegTech, businesses are embracing the power of Explainable AI (XAI) to ensure their AI-driven systems are transparent, interpretable, and auditable, thereby meeting legal and ethical standards.
The first step in this journey is regulatory mapping and risk assessment. Organisations systematically identify and analyse applicable regulations, such as the GDPR, AI Act, and sector-specific rules, and map compliance requirements to AI system components and processes. This foundational step informs where explainability is critical for compliance.
Next, XAI techniques come into play. These methods explain AI decision-making processes, detecting potential biases, and making the rationale behind outputs clear. Techniques include feature importance scores, counterfactual explanations, rule extraction, and confidence scoring. They help demonstrate compliance with anti-discrimination laws and audit requirements.
To further reinforce this transparency, businesses are integrating AI compliance frameworks and tools. Adopting frameworks like the NIST AI Risk Management Framework or ISO 42001, they embed explainability controls in AI lifecycle management. Compliance platforms and Governance, Risk, and Compliance (GRC) systems automate linking technical explainability evidence with regulatory obligations.
AI is also being used to automate compliance monitoring. AI tools can flag non-compliant clauses, track regulatory changes, and generate audit-ready explanations in real time. This proactive approach ensures businesses stay on top of evolving regulations.
Lastly, human-machine collaboration is essential. Combining XAI outputs with periodic expert review ensures explanations are accurate and actionable, reinforcing trust and regulatory adherence.
In summary, technical explainability in AI compliance is achieved by deploying XAI methods that provide transparent decision rationale, supported by compliance mapping and automated tools to meet evolving regulatory requirements and enable auditability. This supports risk management, reduces biases, and builds regulatory trust in AI-driven systems.
Modern AI solutions in the RegTech sector are also increasingly standardizing the use of AI for testing complex frameworks like DORA, demonstrating the growing importance of XAI in this field.
The integration of AI compliance frameworks and tools, such as the NIST AI Risk Management Framework or ISO 42001, aids in embedding explainability controls in AI lifecycle management. Businesses are automating compliance monitoring using AI tools that flag non-compliant clauses and generate audit-ready explanations. Human-machine collaboration is crucial, combining XAI outputs with periodic expert review to ensure explanations are accurate and actionable. In the context of the finance and data-and-cloud-computing industry, technical explainability in AI compliance is instrumental in meeting evolving regulatory requirements, reducing biases, and building regulatory trust in AI-driven systems. This approach supports risk management and enables auditability, which is particularly important in the industry.
