In contrast to traditional AI or automation in finance, AI agents for banks feature autonomy. Unlike others, they are capable of perceiving and analysing data independently, not in a prompts-based manner. Their goal-centered approach increases the solution’s effectiveness in handling multiple business processes with minimum downtime, error, and friction (hence such a wide variety of use cases banking for AI agents).
The technology adoption rate for AI agents for banks is expected to increase from less than 1% as of 2024 to around 33% by 2028. Given the real-time adaptation capacity of these software programs, it should also come as no surprise that the expected ratio of daily operational decisions on sites that can be performed fully autonomously by agentic AI will also rise, namely, from 0% in 2024 to 15% in 2028. AI agents shouldn’t be seen as simple, standalone, algorithm-based technology: they are capable of much more.
What are AI agents for banks?
AI agents for banks serve as autonomous, goal-driven, and action-forward systems that analyze and operate a wide range of data and contexts to generate the desired outcomes. In this setting, they are engineered to assist with multi-step banking workflow optimization without non-stop supervision.
The functionality of such programs goes beyond addressing customer inquiries. Thanks to their objective-centered approach, they can be effective for various use cases banking scenarios, from omnichannel support and reporting to risk management. Their operational mode is consistent, designed to upscale the workflows’ speed. They are highly adaptable, so they can be tailored to multiple business processes.
Difference between an AI agent and a chatbot
While both solutions can support an innovation-forward and customer-centered approach on sites, the primary purpose of the two is pretty distinctive. AI chatbots are commonly utilized for elevating the brand’s communication channels. With Intelswift, you can acquire a shared inbox to keep your messages across social media in one intuitive interface. In turn, AI agents are designed to handle more complicated workflows and deal with multi-step troubleshooting paradigms without difficulty.
Thinking about uses cases banking for AI agents and chatbots, the first category is capable of working with a vast number of databases, accessing APIs, etc., to execute tasks independently. More monitoring efforts are required to adjust the performance of chatbots. What’s more, those can’t change business processes internally, namely, no task completion outside of the chat window in most cases.
Let’s highlight the fundamental differences between AI agents and chatbots in more detail. Check the following.
| Parameter | AI agents | AI chatbots |
| Performance strategy | Goal-driven reasoning and planning, due to retrieval-augmented generation (RAG) and other mechanisms | Based on NLP, LLMs, and machine learning mechanisms |
| Setup complexity | Medium to high | Medium |
| Task execution | A wide range of tasks can be completed without supervision, including multi-step, complicated workflows | Medium-high inquiries |
| Autonomy | High, with independent operations support available | Limited because of their prompts-oriented nature |
| Response to errors | Self-correction, with independent troubleshooting supported | Fallback + clarification attempts |
| Language perception | Deep semantic understanding + contextual memory | Intent + context |
| Learning ability | Continuous learning and optimization loops | Learning from the provided data and training |
| Limitations | More complex optimization and higher operational costs | Risks of data misinterpretations |
How banks use AI agents
According to McKinsey’s report, the need to outperform competitors and produce A+ results ensures that the development of new solutions in this industry will thrive, especially when it relates to agentic AI. Moving forward, the experimentation phase with AI agents for banks will deliver more and more prominent use cases banking scenarios. Here is how this technology can support banking functions and processes at the moment.
Adaptive risk scoring & fraud detection in real time
Take full fraud-response workflows to the next level. The AI-centered approach allows for continuous data monitoring and real-time changes when it matters the most:
- behavioral signal analysis across channels, including wire transfers and mobile banking;
- dynamic adjustments of client risk scores;
- automated initiation of step-up authentication procedures;
- automated “limit” and “freeze” commands on transactions if they don’t match security and other thresholds in the brand;
- analytics and reporting for human agents, for instance, fraud analysts.
AI-forward KYC and AML policies for customer onboarding and identity verification
Data screening to confirm the user’s identity requires a lot of resources and effort from manual verification specialists. In this regard, AI-empowered automation will advance your progress and assist with appropriate follow-up messages to clients, due diligence triggers for high-risk profiles with suspicious behavioral patterns, inconsistency detection in provided vs. factual data per user, and so on.
AI agents for banks’ omnichannel customer support
AI agent deployment doesn’t just serve communication goals, ensuring high response rates and first-contact ticket resolution on your end. It can also assist with automated task rerouting, Knowledge Base updates, account maintenance, and more. With Intelswift, for instance, teams can benefit from communication history records available in the uniform interface, although the interaction itself may take place on Telegram or via email.
Payment operations, exceptions, and transaction recovery
This infrastructure “encourages” a great deal of operational complexity, particularly when it comes to multi-bank payment rails and cross-border transactions. AI agents for banks will stand out in terms of continuous monitoring of such money transfer flows. They are trained to respond to any anomaly signals early, before they become events that can negatively affect customers or the system itself. The assistance range includes instantaneous detection of delayed or failed financial operations, custom notifications to responsible human agents, and issues’ root cause identification, to mention a few.
Credit underwriting and lending intelligence
In this case, advanced AI agents for banks may validate applicant information automatically, analyze repayment behavior patterns, and draft underwriting summaries. They can also simplify your experience with covenant compliance tracking in the post-loan-issuance period. This sets up a dynamic performance environment with business portfolios that keep up with fluctuating macro- and microeconomic terms.
Chargebacks, disputes, and collections optimization
Another way to utilize AI agents for banks is to add intelligent automation to dispute resolution and collections workflows. These software programs can prioritize delinquent accounts by recovery probability, monitor regulatory timelines, and prepare chargeback documentation.
How Intelswift AI agent works for banks
Intelswift’s AI agents can come in handy and streamline independent task completion for uninterrupted and secure financial business processes — up to 80% workflows handled without human intervention. Let’s walk you through how to acquire these AI agents for banks. Onwards!
Register at Intelswift
The signup procedure is straightforward. You can use your Google account to access Intelswift’s services for personalized use cases banking or fill out a simple form — email, name, and company name. You can individualize your experience by adding more information to your profile afterwards.
Create an AI agent
This intuitive interface supports intuitive action on your end. All you need to do is hit the “Create New Agent” button in the menu to get started. The next step is to select the best large language model (LLM) to coordinate the performance flow of your soon-to-be created assistant.
The available variety is accessible in the drop-down list form. It also highlights the functional capacities of each, letting you make a data-driven choice. This decision impacts the response speed, scalability, safety, etc., of your AI agent. High-quality solutions come from Moonshot, OpenAI, and Google.
Train it using your company’s data
What you need to do next is select the desired performance type of this agentic AI instrument. Choose from voice and text operational models while adjusting its language profile (mono or multilingual toggle).
At Intelswift, you can safely upload your business documentation and train this AI agent for your needs. This supports the service’s hyper-personalization value. PDF files, FAQs, and other data formats will come in handy.
Test it in the sandbox
For an even more brand-centered approach from the get-go, run several tests to see how your agent deals with “” and unforeseen circumstances in communication. The purpose is not just to estimate the instrument’s live handoff, but also to verify its accuracy and qualifications in both simple and out-of-the-box problem-solving scenarios.
Launch your AI agent for banks
Now that all the preparations are done, you are ready to release your creation. It can be integrated into your business system near-instantly and operate out of the box without significant configurations. Discover how AI agents support versatile, secure, and compliant banking operations of any caliber first-hand.
