Top 5 AI Innovations Fintech Companies Are Adopting

As an AI service company, we process dozens of monthly calls with prospective customers. Many of these result in us delving deeper into their cases and preparing detailed proposals. Some become DataRoot Labs’ clients.

Over the past six months, the sales team has noticed a spike in requests from fintech startups and financial and banking customers looking to develop their AI projects.

We've identified similarities in their requests and wanted to share them in this article. So, what are fintech companies currently looking to build with AI technology?

1) Intelligent Virtual Assistant for Better User Experience Based on a Vector Database

One of the most popular requests DRL receives is for building a communication agent capable of answering questions about specific company offerings.

Don't just think of basic things like hours of operation or services. Consider more complex conversations around cryptocurrency or technology, specific account information, and statistics. These answers require deploying and tuning an LLM to answer particular questions without hallucinations.

The next level of complexity involves allowing the agent to make recommendations based on the financial data it receives and executing commands within the application via voice or chat.

2) Chat Systems with Semantic Search Capabilities

Imagine if you are an investment firm with tons of documents, templates, presentations, and other types of content or a financial consulting firm with multiple offices across many continents. A system that combines that knowledge and can provide answers would be handy, saving significant time on research and inter-department Q&A.

The solution could be an LLM Agent that can extract details from data upon request (text or voice) and provide specific answers to specific questions. An extension of this task is fitting the information into required templates.

A working LLM Agent can extract the most relevant information and cohesively place it into the most fitting template. Such a solution requires a vector database and a vector search algorithm, such as FAISS. A simple example of such a system can be found in one of DataRoot Labs' articles here.

3) Automated Deal Flow and Outreach

As a VC firm, you must constantly scan the market and identify the most promising opportunities within your mandate. A few firms like that contacted us to build an agent to continuously monitor a preselected list of online sources and fill the CRM with relevant leads.

Some investment firms want to learn what a company is doing, so an agent must be equipped to summarize the nature of the opportunity and, based on subjective criteria, separate the wheat from the chaff. Additional filtering must be applied based on the investment firm's thesis — geography, stage, industry, team size, etc.

Finally, investors should be able to use automated and highly personalized threads to reach out to potential startups or investment prospects. At DataRoot Labs, we have implemented an analogous system for our sales that allows us to identify relevant prospects automatically and continuously.

4) Recommendation Engine for Better Matching

Another popular request from various financial platforms is the ability to automatically and effectively match X to Y. For example, angels with early-stage startups or investors with individuals selling secondary shares.

This involves building a recommendation engine that would match the two based on various parameters. Such an engine is often built on top of larger, more complex AI solutions.

5) Better Customer Experience When Interacting with the Call Center

Many financial firms rely on excellent customer support to retain customers. Think neo-banks or online brokerage firms. A popular request from such firms has been:

• Intelligent Virtual Assistants to remove the top-of-the-funnel person from the call. In this case, an agent should have a near-human voice with low latency.
• Extensive analytics on a call center to identify the most critical issues.
• Automatic matching of the customer on the phone with the best call center specialist.
• Summarize most customer service conversations to improve the overall quality of service and efficiency for similar future requests.

Most of these issues require deploying the open-source, self-hosted version of LLama 2, as customers require enhanced privacy and security in handling customer interactions.

Other typical requests include trading assistants and automated trading of certain classes, such as cryptocurrencies or commodities. These are very much R&D projects that demand time-intensive continuous hypothesis testing and long development cycles.

Whatever you want to build, our team at DataRoot Labs is here to listen to your ideas and help brainstorm tech solutions, development, and deployment.

What is on your mind?