“Lack of GenAI battle-tested scenarios” thwarting its use in financial products, says German fintech Solaris

Generative AI is playing a significant role in many industries, in some cases jet-rocketing productivity.

The finance industry is also attracted by its potential.

Examples include Capital One and JPMorgan Chase leveraging GenAI to bolster their fraud and suspicious activity detection systems, Goldman Sachs using GenAI to develop internal software and Morgan Stanley introducing an AI tool that helps its financial advisors find data.

But here Carina Grühser, VP engineering, German banking-as-a-service fintech Solaris, which recently raised €96 million in a funding round, outlines some of the challenges financial institutions face when deploying GenAI in a financial environment.

Why is Solaris not using ChatGPT?

“Deploying large language models such as ChatGPT internally across a regulated organisation is a significant undertaking. Care must be taken when using AI, as security and data protection are always top priorities.

“For example, some AI apps may use-third party AI-model providers, often sharing data is part of the terms of use. When using any software as part of the Solaris tech stack, we must ensure that clear processes and controls are in place to safeguard customer data from unauthorised access, breaches, or misuse.

“Additionally, continuous monitoring and validation processes need to be set up.

“Instead of deploying tools such as ChatGPT, our approach is to build internal expertise first, as expertise in AI is scarce, especially in the regulated environment with banking know-how. 

“A deep understanding of the business context is crucial to identify the application areas that really add value, especially in generative AI.

As part of this process, one use case our AI taskforce has identified is SolarisGPT, a chatbot based on all the information and knowledge from our Solaris systems to enable our employees to have every information at hand and be most efficient.

“They designed the architecture and implemented a proof of concept already successfully. Developing such systems internally means that it has been designed with operating in a regulated environment at its core and gives us greater control over data and privacy usage.”

 Why is deploying GenAI in a financial environment a significant undertaking?  

“We are always evaluating improvements that new technologies can bring to increase partners’ experience and our own operational excellence. At the same time, our customer and partners data needs to be safe above everything else. 

“With proven third-party software, including those using normal AI or ML, there are lots of existing policies, know-hows and measures available to ensure we can use them in a robust and regulatory friendly manner.

“However, when it comes to GenAI applications, because the technology is in its infancy, there are much less proven use cases and proof points when working within regulated financial environments.

“This means that every potential opportunity must be analyzed in-depth from scratch, resulting in a significant and often time-consuming process.”

 Is GenAI a long way from making into any Solaris product? 

“While we see lots of great potential in using AI and machine learning in certain scenarios, such as risk assessment, fraud detection, personalised services and automated customer services, the wider deployment of Gen AI is still in the exploratory phase. 

“The lack of proven battletested scenarios for Gen AI in regulated environments means we are not yet at the stage where we anticipate using Gen AI in Solaris products.”