Government readies AI and semiconductor roadmap to drive financial product innovation
Indonesia is drafting a national roadmap for artificial intelligence, semiconductors, and supporting technologies to accelerate innovation in digital finance. Coordinating Minister for Economic Affairs Airlangga Hartarto outlined the plan at FEKDI x IFSE 2025 in Jakarta.
The roadmap targets improvements in digital financial services through automation, intelligent analytics, fraud detection, and alternative credit scoring. It also covers digital wallets, asset tokenization, and smart contracts-areas where clear legal footing and technical standards are needed.
Why it matters
Digital payments are now mainstream. The Quick Response Code Indonesian Standard (QRIS) has reached 56 million users, with around 93% coming from MSMEs, expanding access and lowering transaction friction across small businesses.
Scale brings new risk. Payment system security, public digital literacy, and trust were flagged as immediate priorities to sustain growth without increasing fraud and misconduct.
What the roadmap covers
- AI in finance: Automation for operations, intelligent analytics, stronger fraud detection, and alternative credit scoring to widen access to formal finance.
- Emerging tech rails: Legal clarity for digital wallets, asset tokenization, and smart contracts to support compliant product launches and interoperability.
- Semiconductor ecosystem: Incentives and policy to strengthen local compute and supply capacity for AI workloads.
- Data centers and sovereignty: Domestic data center development to keep strategic public service and transaction data within Indonesia, with responsible cross-border data sharing.
Existing policy anchors
The government has released a White Paper on the National Digital Economy Strategy with six pillars guiding long-term development. It also launched the National Council for Financial Inclusion (DNKI) to expand access in remote areas.
For context on QRIS standards, see Bank Indonesia's official page here. For data rights and governance, review the Personal Data Protection Law resources from KOMINFO here.
Implications for government leaders
- Regulatory sandboxes: Expand entry criteria and timelines for AI credit models, tokenization pilots, and programmable payments. Require risk, audit, and exit plans up front.
- Model risk management: Set minimum standards for data quality, bias checks, explainability, and post-deployment monitoring for any AI used in underwriting or fraud.
- Data policy: Clarify data classification, localization requirements, and cross-border transfer mechanisms. Align with incident reporting and breach notification SLAs.
- Security baselines: Mandate encryption, key management, secure model serving, and zero-trust access for payment and identity systems; map to ISO 27001/27701 where practical.
- Public trust and literacy: Fund MSME-focused training on safe digital payments, consent, and dispute resolution. Tie government incentives to verified completion.
- Procurement playbooks: Publish standard terms for AI vendors: data ownership, model transparency, red-teaming, uptime, and rollback procedures.
Implications for product teams
- Build on common rails: Design for QRIS and domestic data center requirements first, then plan for cross-border features via approved gateways.
- Privacy by default: Minimize data collection, log consent, and support deletion/portability requests. Keep a clear data inventory and lineage.
- Fair, explainable scoring: Use interpretable features, document rationale, and provide adverse action notices that customers understand.
- Fraud and KYC/AML: Combine behavioral analytics with rule engines; test for drift and adversarial patterns. Keep human-in-the-loop for edge cases.
- Operational readiness: Set SLAs, rate limits, and kill-switches for AI features. Run chaos tests and incident drills with clear escalation paths.
- Vendor diligence: Verify compliance, security posture, data residency, and model update policies before integrating third-party AI or wallets.
What to watch next
- Publication of the AI, semiconductor, and emerging tech roadmaps with milestones and standards.
- Updates to financial innovation sandboxes and guidance on tokenization and smart contracts.
- Incentives for domestic data center build-out and requirements for public-sector workloads.
- Mechanisms for responsible data sharing with foreign partners and private firms.
The direction is clear: build useful products, protect citizens' data, and make compliance the default. If your team needs structured AI upskilling for policy or product delivery, explore role-based paths here or see current certifications here.
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