Agentic AI in Finance: The rise of artificial intelligence (AI) has begun transforming industries across the globe, and Indonesia’s finance sector is no exception. For years, banks, insurance companies, and fintech firms have relied on “traditional machine learning” for predictive modeling, credit scoring, and risk analytics. But today, the excitement is shifting toward Large Language Models (LLMs) and Agentic AI — autonomous AI systems capable of making decisions and executing tasks with minimal human oversight.
Agentic AI promises unprecedented efficiency and productivity for financial institutions. From automated reporting to enhanced auditing processes, the technology can reduce operational costs, accelerate workflows, and even uncover new business opportunities. However, Indonesia’s finance sector operates under strict regulations, robust data privacy rules, and complex governance frameworks, which can make adopting autonomous systems challenging. Regulatory compliance, data security, and human oversight remain non-negotiable, creating a delicate balance between innovation and risk mitigation.
To understand the real-world implications, I attended the Agentic Finance Conference held in Jakarta on October 16, 2025. Organized by Algoritma Data Science School, the event brought together leaders from banks, insurance firms, fintechs, government institutions, and AI startups. Discussions explored how Agentic AI could reshape the financial sector, highlighting lessons from Indonesia that resonate across Southeast Asia and the broader global finance industry.
1. Measuring ROI and Strategic Value of Agentic AI
One of the first questions financial institutions ask before adopting AI is the potential Return on Investment (ROI). Implementing AI solutions can be costly, and measuring the direct financial impact is often difficult. AI benefits tend to diffuse across multiple teams and processes, making it challenging to isolate its value.
Experts at the conference suggested assessing AI adoption at different maturity levels:
- Boosting Productivity – Automating repetitive tasks and streamlining workflows.
- Achieving Technical Excellence – Improving decision-making and operational precision.
- Enhancing or Creating Revenue Streams – Generating new business opportunities or monetizing data insights.
Beyond ROI, two additional metrics are critical:
- Return on Value (ROV) – Measures broader impact, including decision quality, customer satisfaction, and internal productivity.
- Cost of Inaction (COI) – Quantifies potential losses from delaying AI adoption, such as knowledge gaps, talent shortages, missed learning opportunities, and operational inefficiencies.
Together, ROI, ROV, and COI provide a holistic framework to evaluate the strategic importance of Agentic AI adoption.
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2. Regulatory Challenges in Financial AI Adoption
Indonesia’s financial institutions operate under strict regulatory requirements. For instance, the Financial Services Authority (OJK) mandates that banks maintain data centers and disaster recovery systems within the country. This limits the use of cloud-based AI systems and encourages the adoption of on-premise or hybrid infrastructures.
Cybersecurity remains another critical concern. Data breaches and phishing attacks can have devastating financial and reputational consequences. As one conference speaker emphasized, “Everything must be secure and compliant before deployment — or it’s better not to deploy at all.” Regulatory adherence, robust data protection, and internal governance frameworks are prerequisites for successful AI implementation in the sector.
3. Agentic AI in Action
Humanless Financial Reporting
Traditionally, analysts manually collect data from multiple sources to produce financial reports — a process that demands speed, accuracy, and adaptability. Agentic AI can automate this workflow by coordinating multiple specialized agents to gather market data, analyze company filings, and generate reports in PDF or slide format. Reports can also be scheduled to run daily or weekly with real-time data, drastically reducing manual effort.
The reliability of data sources is critical. Feeding AI agents with curated, verified datasets prevents “hallucinations” — inaccurate or fabricated outputs — ensuring trustworthy reports. Platforms such as Sectors.app provide API endpoints that can supply verified market data to AI systems.
AI-Powered Audit Processes
The Audit Board of Indonesia (BPK) has already integrated AI into its BIDICS platform, transforming audit documents into a queryable knowledge base. AI assists auditors in planning, risk assessment, and preliminary analysis while maintaining strict human oversight. Sensitive financial data access is restricted to authorized personnel, highlighting the importance of combining AI capabilities with regulatory compliance.
Legal Workflow Automation
Platforms like NOTAPOS leverage AI to streamline legal processes, reducing tasks that typically take 18 hours or several days down to just 30 minutes. Initially, building domain-specific AI models required extensive manual fine-tuning using hundreds of legal documents. Today, pre-trained LLMs expedite development, reducing costs and accelerating deployment. This rapid evolution raises a key strategic question: adopt now or wait for the next leap in AI capabilities?
4. Human Impact and Organizational Readiness
Agentic AI is not intended to replace humans but to empower them. Companies must invest in targeted reskilling and upskilling programs to ensure employees can leverage AI tools effectively. Resistance to change, generational differences, and varying technological literacy levels can hinder adoption, making leadership crucial in guiding organizational mindset shifts.
As AI automates repetitive tasks, roles traditionally focused on administrative work may transition to customer-facing or higher-value positions. Continuous workforce adaptation is necessary to remain competitive and capitalize on AI-driven opportunities.
Conclusion
Agentic AI presents significant opportunities for Indonesia’s financial sector, from enhancing productivity and achieving technical excellence to creating entirely new business streams. However, successful adoption depends on regulatory compliance, robust data security, infrastructure readiness, and human preparedness.
Measuring the impact of AI requires looking beyond ROI to consider Return on Value (ROV) and Cost of Inaction (COI), ensuring a more holistic understanding of its strategic benefits. Case studies from BPK, NOTAPOS, and other organizations demonstrate that regulation and innovation can coexist, guiding responsible and impactful AI adoption.
Rapid advancements in LLMs and autonomous agents present both opportunities and dilemmas. Organizations must remain adaptable, continuously experimenting and learning, while anticipating technological evolution to avoid falling behind.
Ultimately, AI is a tool to empower human decision-making and operational efficiency. By investing in technology, workforce readiness, and governance, Indonesia’s finance sector can leverage Agentic AI to achieve both innovation and compliance in a fast-changing landscape.
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FAQs of Agentic AI in Finance
1. What is Agentic AI, and how is it different from traditional AI?
Agentic AI refers to autonomous AI systems capable of making decisions, executing tasks, and interacting with other agents with minimal human intervention. Unlike traditional AI, which requires significant human input for operations like predictive modeling, Agentic AI can perform complex workflows, such as automated reporting or financial auditing, independently.
2. How can Indonesian financial institutions benefit from Agentic AI?
Financial institutions can leverage Agentic AI to automate repetitive processes, generate real-time financial reports, enhance auditing, improve risk assessment, and create new revenue streams. By reducing manual effort, AI empowers employees to focus on strategic tasks and decision-making.
3. What are the regulatory challenges of implementing AI in Indonesia’s finance sector?
Key challenges include strict data residency requirements enforced by the Financial Services Authority (OJK), cybersecurity regulations, and the need for human oversight. Institutions must ensure that AI systems are compliant, secure, and operate under approved governance frameworks.
4. How should organizations measure the value of Agentic AI adoption?
Beyond ROI, organizations should evaluate Return on Value (ROV) and Cost of Inaction (COI). ROV assesses qualitative benefits like decision quality and productivity, while COI evaluates potential losses from delaying AI adoption. Together, these metrics provide a holistic view of AI’s strategic impact.
5. Will Agentic AI replace human workers in finance?
No. Agentic AI is designed to augment human capabilities rather than replace them. While it automates repetitive tasks, human oversight, decision-making, and customer interaction remain crucial. Organizations must invest in reskilling and upskilling programs to ensure employees can effectively leverage AI tools.
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