TradFi is Leaping into AI Territory: Here’s Why

Few industries are as cautious about adopting emerging technologies as traditional finance (TradFi). Concerns over data privacy and strict regulations slows adoption, but artificial intelligence is gradually making its way into more environments.

Just this week, an AI startup known as Hebbia has raised $130 million as part of a funding round led by Andreessen Horowitz, with participation from Index Ventures, Google Ventures, and Peter Thiel.

Hebbia’s solution enables users to build AI agents to ingest structured and unstructured data from multiple sources (including regulatory findings, PDFs, audio, and video clips), and to complete end-to-end tasks.

One of the key investors, Andreesseen Horowitz noted that Hebbia has “experienced staggering demand across financial services” with analyses that used to take two to three hours now taking two to three minutes.

While this is just a single funding round, it highlights the increasing number of financial service institutions that are using AI to automate their operations.

Table of Contents Table of Contents

1. Key Takeaways
2. Why AI is Making its Way into Traditional Finance
3. Extracting Insights from Data Silos
4. Why has the Finance Industry Dragged Its Heels on AI Adoption?
5. The Bottom Line

Show Full Guide

Table of Contents

1. Key Takeaways
2. Why AI is Making its Way into Traditional Finance
3. Extracting Insights from Data Silos
Show Full Guide
4. Why has the Finance Industry Dragged Its Heels on AI Adoption?
5. The Bottom Line

Why AI is Making its Way into Traditional Finance

AI is slowly making its way throughout traditional finance, with big U.S. banks including JP Morgan, Bank of America, Citigroup, and Wells Fargo all investing in the technology to improve their operational efficiency and customer service.

David Donovan, EVP and head of financial services at Publicis Sapient, a digital consultancy that helps banks and financial institutions like Goldman Sachs and JP Morgan digitally transform, told Techopedia.

“AI can significantly enhance traditional finance institutions across various domains. One of the most immediate benefits is in customer service, where AI-driven chatbots and virtual assistants can handle a large volume of customer inquiries, providing instant responses and freeing up human agents for more complex tasks.”

Donovan also notes that “operational efficiency is another area where AI excels. By automating routine tasks such as data entry, compliance checks, and reporting, AI reduces operational costs and increases efficiency.”

Hebbia illustrates this approach but offers a solution that enables users to better query written documents with private third-party data search, proprietary internal search, and public data search. AI has the potential to be the biggest force multiplier in optimizing simple processes like this.

Extracting Insights from Data Silos

At its heart, AI offers the ability to work smarter rather than harder. This is done by offering users open access to a wide array of data in one place, which they would traditionally have to search across multiple disparate sources.

Manvir Sandhu, founder and chief innovation officer at Zennify, told Techopedia:

“Utilizing modern data solutions and large language model [LLM] technology and Retrieval-Augment Generation [RAG], they are now able to effectively extract valuable information trapped in unstructured content and data silos across the enterprise.

“Interacting with Co-Pilot functionality, employees can now search for the content they need via natural language and more efficiently utilize call/meeting summarization to facilitate training and generate key documents. Early pilot results are showing a 10-15% gain in productivity, and in certain cases, employee training time has reduced by 50%.”

This data allows employees to make decisions faster. We can also see this through Hebbia’s claims that during the Silicon Valley Bank crisis, asset managers instantly mapped exposure to regional banks across millions of documents.

Why has the Finance Industry Dragged Its Heels on AI Adoption?

Although AI can generate a lot of value, the finance industry has been slow to adopt it.

This is primarily because the industry is heavily regulated, particularly regarding how customers’ personal and financial information is to be stored and processed.

For example, research from The Economist finds that 62% of banks agree that the complexity and risks associated with handling personal data for AI projects often outweigh the benefits (PDF) to customer experience.

“Traditional finance institutions have been relatively slow to adopt AI compared to fintech startups and tech companies. This slower pace can be attributed to several factors. Firstly, many traditional finance institutions operate on outdated legacy systems that are not easily compatible with modern AI technologies, making integration challenging,” Donovan said.

“Additionally, the highly regulated nature of the finance industry means that institutions must ensure compliance with strict regulations, which can further slow down the adoption of new technologies.

“TradFi institutions also tend to be risk-averse, often cautious about adopting new technologies without a clear understanding of the potential risks and benefits.”

In this sense, many firms see that implementing AI could increase the risk of sensitive and proprietary data being leaked to unauthorized third parties.

The Bottom Line

Traditional finance may have been slow to adopt AI, but more and more companies in the sector are starting to experiment with this technology to enhance their core operations.

Arguably, the key to extracting value is to start small and begin by optimizing basic manual processes that employees rely on every day.

But small steps can be seen as large ones in a relatively conservative industry.