Jun 25, 2024

Autonomous Agents for Finance

With only 32% usage of AI in finance, Autonomous AI agents that operate independently and make decisions based on their programming are the future of financial institutions.

AI for Banking, AI for Finance, How banks use AI, Banks and Technology

The world today is changing at a very fast pace and Technology stands at the very core of it. Think about it, in 1990 we used desktops and big computers, and then came mobile phones and now nanotech with the flavor of AI in it. We are progressing towards an autonomous age, a world where most of the boring and repetitive work would be done by machines. Imagine having 1 bank manager and 4 employees running an entire bank with no human employees and everything transparent in terms of money. That is the future we are talking about today.

Autonomous Agents: What are they?

Well, If we go by definition, Autonomous AI agents are artificial intelligence systems designed to operate independently and make decisions based on their programming, available data, and environmental inputs. Now that you have the definition let me explain you in simple terms with an example: Let's suppose there is a team of 3 people working on a task. Now one of them works on the front end, one on the backend, and one does the research. The work of all 3 is independent of each other, but to complete the task assigned to them, they have to work collaboratively and talk to each other. So we can take this analogy to autonomous agents in AI. 3 AI agents work together to achieve a common task. I believe it all makes sense now.

The possibilities of revolution!Where we are leading in the future is pretty exciting and sometimes scary at the same time. Saying that I believe we are living in the same age as 90’s people lived in the Industrial Revolution, with everything changing and machines taking up. Since I gave you a glimpse of what AI agents are, I will now take you into a world full of endless possibilities of these AI agents. Imagine a bank with all the support agents as AI talking to each other, working collaboratively, each having knowledge of thousands of webpages and books and knowing exactly what customer needs. This may seem very futuristic at first glance but believe me when I say this, it's already done and you will see a detailed example next.

Are we talking of an AI-run bank now, No humans?

To some extent, yes. Lemme give you a vision to set things up for you and envision an AI bank for you. It's important to understand that all AI tech I am talking about here is already being made and you can try that too. It's called Autogen. Now let's understand, what are the important components of a bank. A Manager, A Cashier, a Risk analyst, and one accountant (there may be more but let's consider them for now). Now if I were to make an AI bank, I would set up 3 AI agents and 1 Proxy agent (an agent that instructs other agents what to do, following the analogy of a manager and employees). Since we are moving towards digitalization and we have custom APIs for everything, these AI can understand given documentation and how to handle everything (similar to what Fluid AI understands everything when submitted by your company). Imagine when we can envision a bank now with current tech, what happens 5 years down the lane?

Adapt or Rule out: The two possibilities The world is moving fast and whether on an individual level we like this change or not, we need to adapt it. We all know what happened to Nokia and Blackberry. According to a survey, only 32% of the Finance Sector uses AI for predictive analytics, speech recognition, and other things. All of us understand that the Finance Sector is the backbone of any nation and any change in this sector is progressively slow due to the criticality involved. We at Fluid have been working with banks and other financial institutions across the globe to make an impact and help organizations adapt to this change and be competitive. After all, the rule is still the same. Adapt and Move or be ruled out.

Decision pointsOpen-Source LLMClose-Source LLM
AccessibilityThe code behind the LLM is freely available for anyone to inspect, modify, and use. This fosters collaboration and innovation.The underlying code is proprietary and not accessible to the public. Users rely on the terms and conditions set by the developer.
CustomizationLLMs can be customized and adapted for specific tasks or applications. Developers can fine-tune the models and experiment with new techniques.Customization options are typically limited. Users might have some options to adjust parameters, but are restricted to the functionalities provided by the developer.
Community & DevelopmentBenefit from a thriving community of developers and researchers who contribute to improvements, bug fixes, and feature enhancements.Development is controlled by the owning company, with limited external contributions.
SupportSupport may come from the community, but users may need to rely on in-house expertise for troubleshooting and maintenance.Typically comes with dedicated support from the developer, offering professional assistance and guidance.
CostGenerally free to use, with minimal costs for running the model on your own infrastructure, & may require investment in technical expertise for customization and maintenance.May involve licensing fees, pay-per-use models or require cloud-based access with associated costs.
Transparency & BiasGreater transparency as the training data and methods are open to scrutiny, potentially reducing bias.Limited transparency makes it harder to identify and address potential biases within the model.
IPCode and potentially training data are publicly accessible, can be used as a foundation for building new models.Code and training data are considered trade secrets, no external contributions
SecurityTraining data might be accessible, raising privacy concerns if it contains sensitive information & Security relies on the communityThe codebase is not publicly accessible, control over the training data and stricter privacy measures & Security depends on the vendor's commitment
ScalabilityUsers might need to invest in their own infrastructure to train and run very large models & require leveraging community experts resourcesCompanies often have access to significant resources for training and scaling their models and can be offered as cloud-based services
Deployment & Integration ComplexityOffers greater flexibility for customization and integration into specific workflows but often requires more technical knowledgeTypically designed for ease of deployment and integration with minimal technical setup. Customization options might be limited to functionalities offered by the vendor.
10 ponits you need to evaluate for your Enterprise Usecases

We at Fluid AI stand at the forefront of this AI revolution and help companies kickstart their AI Journey. If you looking out for a solution for your organization, you are at the right place. Book a free demo call with us today and let us help you in making your organization future ready.

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