Top 7 Use Cases for Agentic AI in Banking and Financial Services

TL;DR

• Agentic AI goes beyond automation: Unlike RPA or bots, agentic AI learns, reasons, and makes autonomous decisions-transforming complex banking processes like credit risk assessment, AML compliance, and wealth management through adaptive intelligence.
• Process intelligence is essential first: Before deploying agentic AI, banks must map actual human workflows and decision patterns - not documented procedures - to identify where autonomous decision-making creates the most value.
• Seven high-impact enterprise AI use cases: Credit underwriting, AML monitoring, wealth management, commercial banking, fraud detection, customer personalization, and treasury management represent the most significant opportunities for agentic AI transformation in financial services.

Agentic AI for Banking is Here. Are you Targeting the Best Use Cases?

The banking and financial services industry faces unprecedented challenges from rising regulatory complexity, evolving customer expectations for personalized service, and intensifying competition from fintech disruptors. Traditional rule-based systems and manual processes that once defined banking operations are becoming strategic liabilities that threaten market position and profitability.

Agentic AI, powered by autonomous AI agents that learn, reason, and make independent decisions in real-time, is emerging as the transformative solution financial institutions need. Unlike rigid RPA bots or static rule engines, agentic AI delivers dynamic, context-aware solutions that adapt to changing conditions and make nuanced decisions previously requiring human expertise.

According to McKinsey's "The State of AI in 2024" report, which was published earlier this year, AI adoption in financial services had grown to 63% of companies reporting they use Gen AI for at least one function. Perhaps unsurprisingly, financial services leads the industry in usage within risk and compliance operations (21%).

Knowing how to select the right use cases requires an understanding of where autonomous decision-making and adaptive learning create the most value. Choosing the wrong use case is a great way to get stuck in "pilot purgatory."

Below, we explore seven critical use cases that demonstrate how agentic AI is driving unprecedented transformation across banking and financial services.

Check out our framework on how to choose the best agentic AI use cases.

The Foundation for Successful Agentic AI: Capturing Human Intelligence

Traditional AI agents fail because they're trained on incomplete documentation. Policy manuals and system records miss the nuanced decision-making that separates your top performers from average ones. But the real expertise lies in how your best people actually work.

Banks need to solve this fundamental training problem:

• Loan officers develop sophisticated risk assessment shortcuts
• Compliance analysts build intuitive pattern recognition for suspicious activities
• Wealth advisors create personalized client engagement strategies

These undocumented decision-making processes represent the greatest opportunities for agentic AI transformation. Skan AI's process intelligence platform captures this reality by observing actual human-system interactions and decision patterns across all applications.

Instead of training AI on what should happen, you can now train agents on how your experts actually perform their work. This includes every click, application switch, exception handling method, and contextual decision that makes the difference between good and exceptional performance.

When you observe how work really happens versus how it's documented, you discover the hidden expertise that can be transferred to AI agents—turning your best human insights into scalable AI capabilities.

For example, we had an F100 banking customer achieve $30M in savings by removing redundant activities and adding new automation. All based on having the most complete view of human work.

1. Credit Risk Assessment and Loan Underwriting with Human-Trained Agents

Banking loan underwriting requires sophisticated analysis of creditworthiness, market conditions, and regulatory requirements. Traditional underwriting AI relies on static models that miss the nuanced judgment calls expert underwriters make daily.

Challenge: Most AI systems are trained on credit policies and historical data, but miss how expert underwriters actually assess applications. They can't replicate the subtle pattern recognition and exception handling that experienced professionals use.

Agentic AI Opportunity: Instead of training AI on credit manuals, train it on how your top underwriters actually work. Observe how expert underwriters navigate between systems, what red flags they prioritize, and how they handle edge cases that aren't documented anywhere.

AI agents learn from complete human workflows:

• How experts balance quantitative metrics with qualitative factors
• Which data points get the most attention during different market conditions
• How experienced underwriters adapt their approach for different loan types
• What workarounds top performers use when systems don't provide clear answers

Result: AI agents that make underwriting decisions with the same expertise as your best human underwriters, while freeing those experts to focus on complex client relationships and strategic advisory services.

2. Anti-Money Laundering with Expert-Trained Intelligence

Anti-money laundering compliance represents one of banking's most complex pattern recognition challenges. Rule-based systems create operational burdens while sophisticated financial crime networks adapt faster than traditional detection methods.

Challenge: Current AML systems generate massive false positives because they're trained on rules, not on how expert analysts actually identify suspicious activity. They miss the contextual reasoning that experienced compliance professionals use.

Agentic AI Opportunity: Train AI agents on how your best compliance analysts actually work. Observe their complete investigation process—not just their final decisions, but how they navigate through data, what patterns catch their attention, and how they distinguish between legitimate and suspicious activities.

Agents learn expert investigation patterns:

• How analysts prioritize alerts based on contextual factors
• Which data combinations trigger deeper investigation
• How experts adapt their approach based on transaction patterns
• What investigation shortcuts save time while maintaining accuracy

Result: AI agents that investigate alerts with the same expertise as your best human analysts, dramatically reducing false positives while catching sophisticated financial crime patterns.

3. Commercial Banking with Deal-Making Intelligence

Commercial banking involves complex relationship management, sophisticated deal structuring, and continuous credit monitoring. Each corporate client requires tailored financial solutions that adapt to changing business conditions.

Challenge: Commercial banking AI typically focuses on credit scoring and risk metrics, but misses how expert bankers actually structure deals, negotiate terms, and identify cross-selling opportunities based on deep industry knowledge.

Agentic AI Opportunity: Train AI agents on how your top commercial bankers actually manage relationships. Observe their complete deal development process—from initial client meetings through closing and ongoing account management.

Agents learn deal-making expertise:

• How bankers adapt their approach based on industry-specific factors
• Which client signals indicate readiness for additional products
• How experts structure deals that balance risk with relationship value

Result: AI agents that support deal development with the same strategic insight as your best commercial bankers, enabling them to focus on complex negotiations and strategic relationship building.

4. Fraud Detection with Investigative Intelligence

Financial fraud detection requires balancing security with customer experience. Traditional detection systems often create friction for legitimate customers through excessive false positives while missing sophisticated fraud schemes.

Challenge: Fraud detection AI is typically trained on historical fraud patterns, but misses how expert fraud investigators actually analyze suspicious activity and distinguish between fraud and legitimate edge cases.

Agentic AI Opportunity: Train AI agents on how your best fraud investigators actually work. Observe their complete investigation process—how they analyze transaction patterns, what additional data they gather, and how they make nuanced decisions about risk levels.

Agents learn investigative expertise:

• How investigators prioritize different risk factors based on context
• Which investigation techniques reveal hidden fraud patterns
• How experts balance security concerns with customer experience
• What evidence gathering methods provide the strongest fraud proof

Result: AI agents that investigate potential fraud with the same expertise as your best human investigators, reducing false positives while catching sophisticated fraud schemes.

5. Contact Centers with Service Intelligence

Banking customer experience requires a sophisticated understanding of individual financial needs and circumstances. Creating personalized experiences at scale while maintaining operational efficiency remains a key challenge.

Challenge: Contact center AI typically handles routine requests but can't replicate how expert customer service representatives build rapport, understand complex problems, and provide solutions that strengthen customer relationships.

Agentic AI Opportunity: Train AI agents on how your best customer service representatives actually help customers. Observe their complete service process—how they listen for unspoken concerns, navigate complex product offerings, and turn service calls into relationship-building opportunities.

Agents learn service expertise:

• How representatives adapt their communication style to different customer types
• Which problem-solving approaches resolve issues while building loyalty
• How experts identify opportunities to add value during service interactions

Result: AI agents that provide customer service with the same care and expertise as your best human representatives enable people to focus on complex problem resolution and relationship building as part of broader contact center optimization initiatives.

6. Treasury Management with Strategic Intelligence

Treasury management involves complex optimization across multiple variables including liquidity requirements, regulatory constraints, and market conditions. Traditional approaches struggle to adapt quickly to changing dynamics while maintaining compliance.

Challenge: Treasury management AI typically focuses on quantitative optimization but misses how expert treasury professionals actually balance competing priorities and make strategic decisions under uncertainty.

Agentic AI Opportunity: Train AI agents on how your best treasury managers actually work. Observe their complete decision-making process—how they interpret market signals, balance multiple constraints, and adapt strategies based on changing conditions.

Agents learn strategic expertise:

• How treasury professionals prioritize different objectives 
• Which risk management approaches provide the best protection
• How experts adapt their strategies when regulations change
• What decision-making frameworks handle uncertainty effectively

Result: AI agents that manage treasury functions with the same strategic insight as your best human professionals, enabling them to focus on high-level strategic planning and stakeholder communication.

7. Wealth Management with Relationship Intelligence

Wealth management requires sophisticated understanding of individual client circumstances, market dynamics, and investment strategies. The challenge lies in delivering personalized guidance at scale while maintaining the relationship-building expertise that clients expect.

Challenge: Traditional AI systems can analyze portfolios and market data, but they can't replicate how expert wealth advisors build trust, understand unspoken client concerns, and provide personalized guidance based on life circumstances.

Agentic AI Opportunity: Train AI agents on how your top wealth advisors actually engage with clients. Observe their complete relationship management process—how they prepare for meetings, what questions they ask, and how they adapt their communication style based on client responses.

Agents learn relationship expertise:

• How advisors balance technical analysis with client emotional needs
• Which conversation patterns build trust and uncover true client goals
• How experts adapt investment recommendations based on life changes
• What follow-up strategies maintain long-term client relationships

Result: AI agents that support client relationships with the same personal touch as your best human advisors, enabling advisors to focus on complex financial planning and high-value strategic guidance.

Next Steps: Transforming Banking Operations

Agentic AI trained with human intelligence represents a fundamental transformation in how financial institutions create value, manage risk, and serve customers. The difference between success and failure lies in training agents on real human expertise, not just documentation.

The financial institutions leading this transformation are those systematically capturing their expert knowledge and strategically deploying AI agents that can perform with human-level expertise.

Want to identify your highest-impact opportunities for human-trained agentic AI in banking and financial services?

Let's connect on how to capture and scale your organization's expertise through AI agents that truly understand how your best people work.