Industry
Banking
Banking Services Transformation: Unlocking Exponential Value
Highlights
- Increasing AI adoption in the BFSI industry is giving rise to new risks around ensuring responsible AI, emphasizing the need for effective oversight.
- Inadequate AI governance can have severe consequences: unfair lending practices, data breaches, regulatory penalties, trust erosion, and financial instability. While the elements for defining an efficient AI governance strategy are available, BFSI firms lack practical guidance on implementation.
- To design transparent, trustworthy, and compliant AI systems, BFSI firms must define a unified AI governance strategy that effectively integrates regulatory policies, standards, frameworks, and platforms.
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Introduction
AI is fundamentally reshaping the banking, financial services, and insurance (BFSI) industry.
BFSI firms are embedding AI technologies into essential processes across functions such as fraud detection and management, risk assessment, customer service, and regulatory compliance. As the adoption of AI continues to grow, the associated risks and ethical concerns increase, underscoring the need for careful oversight. Robust AI governance through a comprehensive set of regulatory policies, standards, frameworks, and platforms has therefore emerged as a critical imperative for BFSI organizations to strike the right balance between AI innovation and the foundational principles of governance, spanning trust and accountability.
Furthermore, regulatory activity in AI governance has intensified with regulators defining legal frameworks and mandates. The European Union’s (EU) EU AI Act and the US’ Algorithmic Accountability Act set the benchmark for responsible AI practices. Additionally, the International Organization for Standardization (ISO) has published standards such as ISO 42001 and ISO/IEC 27001, which provide guidance for secure and ethical AI deployment. Frameworks to govern AI lifecycle risks and integrate risk management into AI design, development, and deployment have emerged. In addition, platforms are available to support compliance and accountability.
For BFSI firms, the cost of inadequate AI governance can be steep: unfair lending practices, data breaches, regulatory penalties, trust erosion, reputational damage, and financial instability. Though the components needed to establish a responsible AI practice are available, BFSI firms lack direction on the practical aspects of implementing a robust AI governance strategy. We believe that BFSI firms must define a unified responsible AI approach, combining regulations, standards, frameworks, and platforms, to build trustworthy and compliant AI systems.
Elements of robust AI governance
A holistic responsible AI practice in BFSI requires the integration of regulatory acts, standards, frameworks, and platforms.
The EU AI Act and the US’ Algorithmic Accountability Act establish the regulatory baseline while ISO 42001 and ISO/IEC 27001 offer a systematic governance structure. Frameworks such as the NIST AI Risk Management Framework (NIST AI RMF), developed by the National Institute of Standards and Technology (NIST) in the US, provide tools ably supported by third party platforms to operationalize these elements through technology. By integrating these components, BFSI firms can uphold the aforementioned foundational principles throughout the AI lifecycle.
Regulations
The EU AI Act is one of the world’s first comprehensive regulatory frameworks dedicated to AI. It classifies AI systems based on risk levels—ranging from minimal to unacceptable risk—and imposes specific obligations on providers and users of high-risk AI systems. For BFSI organisations, many AI applications such as credit scoring, biometric identification, and fraud monitoring are deemed high-risk due to their potential impact on fundamental rights and systemic financial stability.
In parallel, the Algorithmic Accountability Act aims to regulate organizations that use automated decision systems, requiring them to conduct impact assessments for bias, discrimination, and privacy risks. This act encourages transparency and accountability in AI systems, particularly those influencing consumer finance and lending. Both the acts are especially pertinent for BFSI firms operating globally. They mandate proactive risk assessments, robust governance mechanisms, and clear accountability structures. This regulatory environment encourages BFSI firms to embed responsible AI principles from the outset, not merely as a compliance exercise but as a strategic and functional imperative. Compliance will require BFSI firms to:
- Put in place stringent controls and policies for high-risk AI applications such as personalized credit scoring, portfolio balancing, and claims automation.
- Ensure that AI systems meet requirements around data quality, documentation, traceability, and post-market monitoring.
- Assess the legality, fairness, and safety of their AI models to avoid penalties for non-compliance.
ISO standards
ISO 42001 is the world’s first AI management system standard. It prescribes a structured approach to establishing, implementing, maintaining, and continually improving AI management systems within organizations. Although compliance with ISO 42001 is voluntary, the standard can serve as a framework for BFSI firms to comply with the EU AI Act’s governance requirements, especially in risk categorization, underscoring functional and strategic requirements.
Additionally, ISO/IEC 27001 is a globally recognized standard for information security management systems. It mandates establishing, implementing, maintaining, and continually improving information security, which is critical for BFSI firms that use AI systems and applications to process sensitive financial data.
The overlap between the ISO standards and AI regulations across risk management, data governance, and ethical AI has the potential to unlock tremendous synergies. To capitalize on this opportunity, BFSI firms must adopt ISO 42001 and ISO/IEC 27001 to systematically manage AI and the associated information security risks, ensuring that AI systems are designed, deployed, and monitored in line with responsible AI principles.
Frameworks
There are frameworks available to address the core challenges of deploying AI responsibly. BFSI firms must leverage these frameworks to integrate risk management, security controls, and trust-building measures throughout the AI lifecycle. The NIST AI RMF offers guidance for managing AI risks through four functions: govern, map, measure, and manage.
By integrating the operational safeguards offered by these frameworks with the strategic structure provided by the NIST AI RMF1, BFSI firms must define a comprehensive approach that effectively balances technical rigor with strong organizational oversight (see Table 1).
NIST AI RMF function |
Responsible AI pillar |
Objective |
Govern |
Governance and compliance |
Establish clear policies, roles, and ethical standards for AI use. |
Map |
Model monitoring and accountability |
Identify AI systems, data flows, and potential risks across the lifecycle. |
Measure |
Bias detection and fairness, explainability and transparency |
Evaluate AI performance, fairness, and interpretability using metrics and audits. |
Manage |
Security and risk management |
Mitigate risks through robust security controls, incident response, and continuous monitoring. |
Table 1: Mapping responsible AI pillars with the NIST framework functions
BFSI firms must leverage the tools and processes offered by these frameworks to identify, assess, and mitigate AI risks, including model bias, data privacy, and operational resilience. In addition, their mechanisms can help incorporate explainability and fairness into AI systems, facilitating robust governance. By embedding risk and trust considerations into every stage of AI development and deployment, these frameworks help operationalize responsible AI in BFSI.
Platforms
Third-party platforms enable responsible data use, helping BFSI firms operationalize AI governance. They provide modules for privacy management and compliance, risk as well as ethical AI assessments and monitoring, policy automation, and compliance tracking. Using these platforms can help BFSI firms to centralize AI risk and compliance management, making responsible AI consistent across the value chain. They can also facilitate collaboration across legal, compliance, and technical teams as well as automate workflows across risk assessments, documentation, and monitoring, in turn streamlining compliance with regulations and standards.
Translating a comprehensive responsible AI approach into action will require BFSI firms to establish a cross-functional governance committee comprising stakeholders from compliance, risk, IT, and business teams. The committee should be tasked with continuously analyzing existing AI practices, benchmarking them against the EU AI Act, the Algorithmic Accountability Act, and ISO standards. Based on this analysis, BFSI firms must develop a strategic roadmap that integrates robust guardrails throughout the AI lifecycle, paying particular attention to high-risk applications, data quality, and transparency. Common challenges—such as navigating complex regulations, managing model bias, and integrating governance with business processes—can be addressed by investing in staff training, leveraging technology platforms, and fostering collaboration across departments.
Toward unified AI governance
As BFSI firms expand the deployment of AI across mission-critical functions, implementing a unified AI governance strategy is crucial for the auditable execution of core principles across the AI lifecycle.
Such a strategy must effectively integrate regulatory expectations and drive true value (see Figure 1), paving the way for responsible AI systems in the BFSI industry.
Figure 1: Components of an AI governance strategy
Before embarking on implementation, BFSI firms must pay attention to some crucial aspects:
- High regulatory scrutiny: The BFSI industry is subject to stringent regulations due to its systemic importance and handling of sensitive financial data. Consequently, BFSI firms, especially those with a global footprint, are required to comply with multiple overlapping regulations such as the EU AI Act and the Algorithmic Accountability Act. In addition, they must comply with data privacy laws such as the General Data Protection Regulation (GDPR), the California Consumer Privacy Act (CCPA) in the US, as well as geography specific guidelines mandated by central banks and national regulators. Furthermore, functional demands require compliance with global standards. All of this necessitates a multi-layered governance approach that integrates legal, technical, and ethical safeguards.
- Complex risk profiles: Banks and financial institutions are frequent targets of financial crime. Risks associated with model bias, data privacy, and operational resilience are amplified in AI applications of banks and insurers. BFSI firms will need to factor in all these risks while building AI applications.
- Stakeholder expectations: Customers, regulators, and investors demand transparency, fairness, and accountability from AI-enabled financial products and services. BFSI firms will need to put in place processes to demonstrate these aspects.
Given these unique requirements of AI applications in the BFSI sector, we recommend key best practices that banks and insurers must follow to ensure responsible AI:
- Invest in comprehensive AI governance programs that incorporate legal, technical, and ethical safeguards.
- Conduct regular risk and impact assessments, evaluating AI systems for compliance, fairness, and security throughout their lifecycle.
- Implement ISO 42001 and ISO/IEC 27001-aligned management systems and structure governance processes to facilitate continual improvement, infusing the flexibility needed to achieve alignment with new regulatory changes.
- Build a controlled environment using AI agents to govern AI.
- Commit to continual improvement by establishing ongoing monitoring and auditing mechanisms to detect and address emerging risks and compliance gaps by updating AI systems and governance structures.
Looking ahead
AI has the potential to revolutionize the BFSI industry, going beyond mere operational efficiency optimization to reinvent functions across the value chain.
Be it loan approvals, real-time risk modeling, customer interactions, fraud management—AI will deliver transformational impact.
However, as BFSI firms scale from pilots to enterprise-wide AI implementations aimed at business and revenue growth, ensuring fair, transparent, and compliant outcomes that can retain customer trust and loyalty becomes a big challenge. Consequently, responsible AI in BFSI is not just about compliance, it has emerged as a strategic necessity to unlock returns from AI investments.
The way forward for BFSI firms lies in defining a strategy that unites regulations, standards, frameworks, and platforms to ensure AI remains ethical, transparent, resilient, and compliant, while driving innovation. The time to act is now: start embedding responsibility into every AI decision today—because trust is the foundation of tomorrow’s financial ecosystem.
1 National Institute of Standards and Technology, US Department of Commerce, AI Risk Management Framework, January 2023, Retrieved January 2026, https://www.nist.gov/itl/ai-risk-management-framework