AI Agents: The next frontier in AI for Banks.

Sarah Chen, the CIO at Pacific Bank, stands at her office window overlooking the San Francisco skyline, contemplating the latest wave of AI transformation sweeping through the banking industry. While her team has successfully implemented several Large Language Models (LLMs) for customer service and document processing, she keeps hearing about "Agentic AI" and "AI Agents" at every industry conference and board meeting.

"Everyone's talking about how they're not just another iteration of chatbots, but rather AI systems that can actually DO things - make decisions, execute tasks, and operate across multiple systems autonomously", she muses.

The questions keeping her up at night are both strategic and practical:

• How are AI Agents fundamentally different from the chatbots and automation tools her bank already uses?
• What happens when you give AI systems the ability to not just analyze but act?
• Could AI Agents truly handle complex banking processes end-to-end without human intervention?
• And most importantly, how would this transform the roles of her 15,000 employees?

These aren't just theoretical concerns for Sarah. With their biggest competitor recently announcing a major investment in AI Agents for wealth management, the pressure to understand and act on this technology has never been greater.

The promise of AI Agents to revolutionize everything from customer onboarding to risk management seems compelling, but separating reality from the hype remains her biggest challenge.

Let me define AI Agents and explain their transformative potential, particularly for financial institutions:

AI Agents are autonomous software systems that combine large language models with the ability to interact with external tools, make decisions, and execute actions to accomplish specific goals.

2 Things are important here :

1) It can perceive the environment and take actions

2) It has a specific goal to achieve.

Think of them as assistants that can not just understand and respond, but actually perform sequences of actions to complete tasks. They're considered the next big thing in AI adoption because they bridge the gap between AI's analytical and reasoning capabilities and real-world actions.

That are the next level in RPA - in the sense everything has to pre-set in RPA, but with AI Agents, LLM can Observe, Organize the information, and then take Actions with an appropriate tool (API) - all in a loop.

For banks and insurance companies, this means moving from AI that simply provides answers to AI that can actively handle processes and transactions.

Here are some ways AI Agents augment traditional LLMs:

• Tool Integration and Action: Unlike standard LLMs that can only process and generate text, AI Agents can actively use external tools and APIs. For example, in banking, an agent could not just understand a customer's request for a loan refinancing, but actually access the relevant systems, run calculations, verify eligibility, and initiate the paperwork process.
• Memory and Context Management: While LLMs process information in isolated exchanges, AI Agents maintain persistent memory across interactions and tasks. They can track complex financial transactions over time like mortgage application over time, remember customer preferences, and maintain context across multiple sessions.
• Goal-Oriented Planning and Execution: LLMs respond to prompts in one shot, but AI Agents can break down complex objectives into manageable steps and execute them systematically. For instance, in insurance claim processing, an agent could break it down as following logical steps: gather documentation, verify policy details, assess damage reports, calculate payouts, and coordinate with different departments.

There are many other use cases where Sarah and executives like her can utilize the power of Agentic AI.

Front Office AI Agent Use Cases:

• Account opening agent that handles entire KYC process including video verification, document collection and validation, and credit checks
• Loan origination agent that guides customers through application, collects documents, and processes pre-approvals
• ATM assistance agent that helps customers troubleshoot issues and coordinates with maintenance teams in real-time
• Personalized banking concierge that proactively suggests products based on life events and handles cross-selling

Mid Office AI Agent Use Cases:

• Risk monitoring agent that continuously scans transactions for fraud patterns and initiates investigations
• Regulatory compliance agent that tracks changes in regulations and updates internal policies automatically
• Treasury management agent that optimizes cash positions across branches and ATMs
• Collateral management agent that monitors valuations and triggers margin calls when needed

Back Office AI Agent Use Cases:

• Reconciliation agent that matches transactions across multiple systems and resolves discrepancies
• Data quality agent that continuously monitors and cleanses data across systems
• IT support agent that diagnoses system issues, implements fixes, and manages access controls
• Financial reporting agent that compiles reports from multiple sources and ensures regulatory compliance

After a bit more research, Sarah realizes that most of her competitors are already ahead in implementing Agentic AI in a big way.

JPMorgan Chase started testing an AI assistant called "IndexGPT" that helps investors analyze financial documents and company reports. It can extract relevant data points and generate insights from multiple sources.

UBS deployed an AI system that helps wealth managers by automatically analyzing earnings calls, financial reports, and news to generate investment insights and recommendations for their high-net-worth clients.

DBS Bank in Singapore implemented an AI-powered engine that could predict and resolve potential transaction processing errors before they impact customers, reducing manual interventions in their back office.

One months and countless research hours later, Sarah's perspective has fundamentally shifted.Sitting in her office preparing for next week's board presentation, she reflects on her journey from skepticism to conviction about AI Agents' transformative potential.

"This isn't about replacing our existing AI investments or our people," Fatima notes in her draft presentation. "It's about elevating both."

AI Agents are the connective tissue that can bring together our disparate systems, processes, and teams in ways we never imagined possible."

Her proposal to the board is bold but pragmatic:

• A three-year roadmap to systematically implement AI Agents across key banking operations.
• Phase one will focus on customer experience and loan processing, areas where competitors are already gaining ground.

"We can't afford to treat this as just another tech trend," she rehearses her closing argument. "Every month we delay is a month our competitors are advancing. AI Agents aren't the future of banking – they're the present we need to embrace."

As she finalizes her presentation, a notification pops up – their competitor has just announced another AI Agent initiative, this time in commercial lending.

Sarah smiles, knowing that soon, Pacific Bank won't just be keeping up; they'll be leading the charge.

"The tides of transformation don't wait for the hesitant. In banking, just as in nature, those who recognize the winds of change and adjust their sails first don't just survive - they discover new horizons others haven't even dreamed of. Yesterday's impossibilities become tomorrow's competitive advantages, but only for those bold enough to embrace them today."

- Sarah Chen, Chief Innovation Officer, Pacific Bank