Democratizing AI in Financial Services: A Vision Beyond LLM and Generative AI

Rohit Bhosale
𝐀𝐈 𝐦𝐨𝐧𝐤𝐬.𝐢𝐨
4 min readSep 22, 2023

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Introduction

In the rapidly evolving landscape of financial services, the democratization of artificial intelligence (AI) has emerged as a critical imperative. As we strive to empower individuals and businesses with intelligent decision-making tools, it is essential to recognize that the path to democratization does not exclusively rely on Large Language Models (LLMs) or Generative AI. Instead, we should draw inspiration from Abraham Lincoln’s timeless call for a government “of the people, by the people, for the people” and apply it to the realm of AI systems.

Democratizing AI: A Holistic Approach

Democratizing AI demands a comprehensive approach that encompasses accessibility, transparency, and privacy. It involves more than just leveraging cutting-edge algorithms; it necessitates harnessing the collective power of data, human expertise, and technology. Let’s break down this concept by aligning it with Lincoln’s principles and focus on financial services:

  • “Of the People”: Harnessing Data Assets

In the digital age, data has become one of the most valuable resources. Financial institutions are entrusted with a wealth of information, and democratization requires utilizing this data for the benefit of all stakeholders. This involves responsibly aggregating and anonymizing data to ensure privacy, while extracting insights that can drive informed decision-making. The data of the people, no pun intended, can be utilized to create custom AI models, which don't have to be large and therefore can be more cost-effective. Making them truly democratic and inclusive, independent of excessive GPU dependence.

  • “By the People”: Inclusive Collaboration

The democratization of AI cannot be an exclusive endeavor limited to a select few experts. It should involve collaboration with diverse teams within financial institutions, drawing on the collective wisdom of professionals from various disciplines. By engaging a broad spectrum of expertise, we ensure that AI systems reflect a balanced and comprehensive understanding of the financial landscape. True democratization lies in the ability of tech companies to create frameworks which allows any, non-technical individual, to take the data they have and train small transformers. Small Transformers are transformer models, much like GPT which stands for Generative Pre-trained Transformer, but instead of relying on large language models with billions of parameters they are small and hyper focused. They rely on semantic understanding and are trained for small specific tasks making them intelligent, efficient and cost-effective, truly giving the power of creating automation agents to the masses.

  • “For the People”: Enhancing Efficiency and Easing Lives

The ultimate goal of democratized AI in financial services is to enhance the lives of individuals and businesses. By leveraging data-driven insights, we can streamline processes, optimize resource allocation, and offer personalized services that cater to the unique needs of each customer. This not only makes financial interactions more efficient, but also empowers individuals to make more informed decisions. Taking a large language model which, in quintessence a swiss army knife of AI, and relying on it to automate your processes is the opposite of what true democratization of AI should stand for. Allowing people to build models that solve the exact problem they are looking to solve, is true democratization for the people undertaking the tedious tasks.

Preserving Privacy and Building Trust

Central to the success of democratizing AI in financial services is the assurance of privacy. Lincoln’s vision extends to safeguarding the rights and interests of individuals, and this principle holds true in the realm of AI. Responsible data handling, encryption, and compliance with regulatory standards are paramount to building trust and ensuring that AI systems serve the interests of the people. Utilizing a large language model (LLM) which is a community model and can lead to cross contamination of data is hardly protecting the interests of the people. It is important that privacy remains at the center of the AI models of tomorrow which are not necessarily omnipotent but an extension of intelligence for a specific task.

The Role of Technology

While LLMs and Generative AI certainly play a significant role in AI advancements, they are not the sole linchpin for democratization. A balanced approach that incorporates a wide array of AI techniques, including machine learning, natural language processing, and computer vision, allows for a more nuanced understanding and application of financial data. A small transformer based on transfer learning and hyper focused on specific tasks seems like a good place to begin and continue to leverage other techniques to build a true ecosystem which is modular and scalable.

Conclusion: Empowering the Future of Finance

Democratizing AI in financial services is a multifaceted endeavor that transcends the boundaries of specific AI models. By adopting a holistic approach that respects the principles of inclusivity, transparency, and privacy, we can build AI systems that truly serve the interests of the people. If Abraham Lincoln were alive today, he would undoubtedly champion this vision, advocating for AI systems that are “of the people, by the people, and for the people.” In doing so, we can revolutionize financial services, making them more efficient, accessible, and empowering for all.

At Synapze this is the philosophy we live and this is the vision we are executing on an everyday basis. Come talk to us about how we enable small transformers using our framework to truly democratize the use of AI in the financial service industry.

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Rohit Bhosale
𝐀𝐈 𝐦𝐨𝐧𝐤𝐬.𝐢𝐨

As the founder of Synapze, I am working with my team to build the AI/NLP driven decision-making platform for Financial Services Industry