Artificial intelligence (AI) has shifted from being a futuristic concept to a present-day business enabler. From automating back-office tasks to powering customer service chatbots, fraud detection, and credit risk analysis, AI has redefined how banks operate. Yet, as adoption accelerates, so do the stakes. Without proper governance, AI could become more of a liability than a strategic advantage.

The Evolution of AI in Banking

AI’s journey began with task automation that replicated human work without creating new methods of interaction. Traditional AI brought pattern recognition, data analysis, prediction, and classification. Generative AI advanced further by creating new content such as text, images, or even code, reshaping customer engagement and operations. The latest wave is Agentic AI, which can autonomously learn and make decisions without direct supervision.

Banking has been at the forefront of this transformation. Nearly 80 percent of financial institutions    recognize AI as a source of competitive advantage, particularly in machine learning. Indonesia, although still classified as a rising contender in global AI adoption, has shown commitment through initiatives such as the National AI Strategy 2020–2045, the Ministry of Communication’s AI Ethics Circular, and the Financial Services Authority (OJK) publication “Tata Kelola Kecerdasan Artifisial Perbankan Indonesia” (Artificial Intelligence Governance for Indonesian Banking, 2025), which provides banks with guidelines to ensure AI is developed and implemented responsibly.

Opportunities and Risks in AI Adoption

The promise of AI for banks is undeniable. It offers operational efficiency, stronger fraud detection, personalized services, and improved risk management. However, risks are just as pressing. These challenges can be grouped into four dimensions:

  • Organizational: lack of clear governance structures and fragmented oversight.
  • Technology: inadequate infrastructure and lack of explainability in models.
  • Information systems: poor data quality, weak integration, and cybersecurity vulnerabilities.
  • Human resources: limited skills, unprepared leadership, and resistance to change.

If these risks are not addressed, AI adoption can undermine trust, create compliance breaches, and even amplify systemic risks across the financial sector.

Why AI Governance Matters for Banks

AI governance is not only about compliance but about embedding responsibility, integrity, and resilience into the AI lifecycle. For banks, key considerations include:

  • Organizational structures: clear mechanisms aligned with board oversight.
  • Policies and procedures: formal usage rules,
  • escalation protocols, and accountability lines.
  • AI specific risk management: cross functional
  • ownership across all three lines of defense.
  • Independent internal audit: unbiased evaluation and assurance of AI models.
  • AI impact assessments: addressing bias, accountability, product safety, and security.
  • Regulatory compliance: staying ahead of evolving banking and AI specific regulations.
  • AI KPIs: measuring not only technical accuracy but also business outcomes.
  • Role assignments: ensuring executives understand AI concepts and risks.
  • AI Committee: reporting directly to the Board and ensuring alignment with strategy.
  • Center of Excellence: consolidating expertise and driving responsible adoption across the bank.
  • Talent development: upskilling across technical and non-technical roles.
  • Computational readiness: ensuring infrastructure and data security safeguards.

Without these safeguards, banks risk reputational damage, financial loss, and regulatory penalties.

Insights from RSM’s Global Research

Findings from the RSM Middle Market AI Survey 2025 show that while enthusiasm for AI is high, governance maturity is lagging. Many organizations adopt generative AI without fully addressing data privacy, security, and bias. Executives increasingly recognize that trust in AI systems will determine long-term adoption and return on investment. For banks, this reinforces the urgency of moving beyond experimentation toward structured governance frameworks that align with both business goals and regulatory expectations.

The Call to Action for Indonesian Banks

As financial institutions in Indonesia accelerate AI deployment, the challenge is not whether to adopt AI but how to govern it responsibly. Regulators like OJK have provided minimum references through Tata 

Kelola Kecerdasan Artifisial Perbankan Indonesia, but banks must go further by embedding governance structures that reflect integrity, accountability, and resilience.

Board members and executives cannot delegate AI oversight to technical teams alone. They need to understand the AI lifecycle, support cross unit collaboration, and ensure timely corrections when risks emerge. Establishing an AI Committee, investing in Centers of Excellence, and building talent pipelines are no longer optional. They are prerequisites for    sustainable growth.

AI governance is not a box ticking exercise. It is the foundation of digital trust. For banks, trust has always been the most valuable currency. In the age of AI, it will decide who thrives and who falls behind.

 

Erikman Pardamean & Dian P Rahmasari, Technology Risk Consulting Practice
Erikman Daniel Pardamean
Information Technology GRC Consulting Partner
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