What the Future Holds for Artificial Intelligence and Financial Technology

What the Future Holds for AI and FinTech

Despite all the buzz since the launch of ChatGPT, fintech companies have been using AI.

From a product development standpoint, the most immediate application for AI is in the R&D phase. Specifically, we can expect more and more fintech companies (and enterprise tech, as well) to leverage generative AI in product design, using it to create myriad iterations of product UI and exploring non-intuitive layouts beyond what a small staff of designers could accomplish on their own.

We also imagine a near future wherein predictive AI can help us generate probable use cases and run product testing, identifying bugs and design gaps more quickly, ultimately reducing the time it takes to move from alpha and beta phases to general release.

Outside of product development, we see generative and predictive AI being more front and center in the products themselves. For instance, we can imagine a future wherein our bill pay and automated AR platform comes equipped with a virtual bookkeeper, or CFO bot companion to help assist in closing the books at month's end. Perhaps predictive AI will be able to monitor a customer’s spending habits and help them better inform their future purchasing and the impact that will have on cash flow over time.

Smarter, More Agile Go-To-Market Teams

AI will also have a great impact on how go-to-market teams prospect, nurture, and close customers within the fintech ecosystem.

At Ampla, we already have a very robust system for scoring prospects that pulls from zero-party, first-party, and publicly available data to assign viability scores to certain brands. This AI-based scoring system has helped to improve our conversion metrics by double digits, cutting back on time spent prospecting clients who are not a good fit or who are not likely to close. We foresee this type of AI and machine learning-driven prospecting cascading down from the largest companies that have built systems in-house to smaller firms that may find the solution via a SaaS-like tool.

In the near future, we can also imagine a world wherein every B2B has an embedded AI technology that helps them identify upsell and cross-sell opportunities by analyzing customer data and predicting their future needs. This can help sales teams to provide targeted recommendations and drive additional revenue from existing customers. Big tech companies such as Meta and Google famously use these predictive AI-driven algorithms to keep us glued to our phones with content they know we’ll love. We imagine the same type of technology will be mass adopted at the B2B fintech level, as well.

Winners and Losers in AI and Fintech

Who will win and who will lose in the race for AI supremacy? That’s a question that many find themselves asking, particularly as geo-political tensions continue to mount, with a particular focus on the US and China. We believe the conversation is far more nuanced and speaks to how this technology differs greatly from some of the technologies of the past.

Whereas being a leader in drug development or computer hardware relies on developing and protecting IP and patents, AI by nature is a much more open technology. Success in implementing AI is more about having access to large, clean data sets than having the best technology or the brightest engineers.

Through that lens, any company – regardless of geography – can quickly establish a leadership position in the field, with the obvious advantages going to large corporations that are already collecting large swaths of data. That said, smaller fintechs around the globe, particularly ones who have always taken a data-first approach and serve specific customer segments wherein data is not as widely available - can also have a competitive advantage.