The Opportunity: 70% of Banks Are Already Moving

Agentic AI is now mainstream in banking. A 2025 survey of 250 banking executives found that 70% are already using or piloting autonomous AI systems.

Key Numbers:

• 16% have systems deployed in production
• 52% are running active pilot projects
• 22% are identifying use cases
• Only 2% not using yet

What It Is: Agentic AI systems perceive data, make decisions, and take actions independently within defined guardrails. Think of it as intelligent assistance working alongside humans, not replacing them.

Takeaway: Agentic AI is not future technology. It is happening now. Delay increases competitive risk.

Where Banks Are Winning: Five Use Cases

Banks report strong results in five priority applications:

1. Fraud Detection (56% highly capable)
Continuous monitoring, deepfake detection, automated risk identification. Reduces manual review workload significantly.

2. Customer Experience (41% highly capable)
Mortgage underwriting, loan approvals, dispute resolution. Decision timelines compressed from weeks to days.

3. Know-Your-Customer/AML (High priority)
Automated data collection, compliance documentation, faster onboarding. Improves accuracy while reducing costs.

4. Cost and Efficiency (41% highly capable)
Back-office automation, document processing, collections management. Direct operational cost reduction.

5. Employee Productivity
AI assistants for advisors and engineers. Better decisions, higher quality interactions.

Takeaway: Start with fraud detection and customer experience. Highest capability, fastest ROI.

Three Obstacles Blocking Implementation

Banks face three critical barriers:

Obstacle 1: Governance, Risk, and Compliance (63% cite)
Problem: Regulations lag behind technology. No clear regulatory standards exist.
Solution: Build governance framework before scaling. Use real-time metrics with kill switches. Document all AI systems.

Obstacle 2: Technology Skills Gap (58% cite)
Problem: Banks lack sufficient AI expertise. Staff need reskilling.
Solution: Invest in workforce training. Hire specialised AI talent. Build incrementally with external support.

Obstacle 3: Data Quality and Integration (54% cite)
Problem: Legacy systems are disconnected. Data inconsistencies prevent accurate decisions.
Solution: Create single source of truth. Establish data standards. Build common technology platforms.

Takeaway: Obstacles are organisational, not just technical. Fix people and processes first.

How to Implement: Three Essential Steps

Step 1: Evaluate Your Use Cases (Months 1-2)

Ask five questions about any process:

• Is it manual?
• Is it expensive?
• Is it time-lagged?
• Is it error-prone?
• Is it consequential if mistakes happen?

If three or more answer "yes," pursue agentic AI for that process.

Step 2: Build Governance Framework (Months 1-3)

Create AI system inventory linked to business owners. Define risk levels. Establish performance metrics. Implement kill switches for sensitive applications.

Example Framework (DBS Bank PURE Model):

• Purposeful: Clear business case
• Unsurprising: Expected data use
• Respectful: Privacy and social norms protected
• Easy to Explain: Transparent decision-making

Step 3: Scale Incrementally (Months 4+)

• Phase 1: Deploy single high-value use case with human oversight at every step
• Phase 2: Expand to 2-3 use cases, reduce human approval gradually
• Phase 3: Scale across business lines as confidence builds

Takeaway: Systematic implementation reduces risk. Start small, scale proven winners.

Success Metrics: What to Measure

Business Impact:

• Fraud detection improvement: Expected 75% gains
• Security: Expected 64% improvement
• Customer experience: Expected 51% improvement
• Cost reduction: Expected within 12-18 months

Operational Metrics:

• Processing time reduction (target: 50%+)
• Error rate improvement (target: 30%+)
• Manual workload reduction (target: 40%+)
• System uptime and reliability (target: 99%+)

Governance Metrics:

• Number of documented AI systems
• Real-time metric breaches and responses
• Audit readiness percentage
• Stakeholder training completion

Takeaway: Measure everything. ROI appears within 12-18 months when implemented correctly.

Take Action Now: Three Imperatives

Imperative 1: Act Immediately
70% adoption means competitive advantage goes to early movers. Waiting increases risk.

Imperative 2: Build Governance First
Do not scale without frameworks in place. Use DBS PURE model or equivalent. Implement kill switches.

Imperative 3: Invest in People
Technology is not the bottleneck. People capability and organisational change are. Invest in reskilling and training.

Final Takeaway: Agentic AI transforms banking when implemented systematically with humans remaining in the loop and clear governance frameworks in place.

Ready to launch your agentic AI strategy?
Book a Strategy Call with Fyscal Technologies →