By Steve Carpenter, Country Director for North America, Creditsafe
You can’t go a day without hearing the term ‘AI’ in the world of fintech. Thought leaders, influencers and companies across the industry are constantly talking about new AI-driven solutions, promising advancements that will transform credit scoring, risk management and financial decision-making.
But as someone who works in the fintech space, I’ve grown skeptical of these claims. More often than not, the term ‘AI’ is misused to make existing technologies look more innovative than they are. I believe that genuine AI-driven transformations in fintech remain elusive.
Fintech AI: The hype vs. reality
The phrase “AI-powered” is frequently used to market products and services, creating an illusion of cutting-edge innovation. But a closer examination reveals that many of these so-called AI advancements are merely repackaged versions of existing technologies. This trend is particularly evident in the credit information sector, where AI is expected to transform credit scoring and risk assessments. But it hasn’t become a reality just yet.
Consider credit scoring, a process that traditionally takes years of planning, statistical modeling and back-testing to update. You’d think that AI could dramatically expedite this process so that real-time adjustments can be made based on the latest data. But despite the hype, we haven’t seen significant AI-driven developments in this area. Most credit scoring systems continue to rely on traditional automation and algorithms, with little evidence of true AI at work.
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Misuse of AI terminology
One glaring example of how the term AI is misused in fintech is the case of expense management software. Recently, I saw expense management software that automatically populated the category field of a receipt labeled “Uber” with “public transport,” claiming this was achieved using AI. But the truth is that it was just using simple logic or coding—if the receipt mentions “Uber” or other well-known public transit methods, then it categorizes the expense as public transport. This isn’t AI; it’s basic automation.
This mislabeling of simple automation as AI happens a lot in the fintech industry. Companies tend to use the term ‘AI’ to create buzz and to create a perception of innovation, even when their products offer no significant advancements.
What true AI looks like in fintech
To understand what genuine AI looks like in fintech, we need to define true AI feature value. In the context of financial risk, AI should involve a machine learning process that continuously analyzes data and updates its algorithms in real-time without human intervention. True AI applications in fintech should demonstrate self-learning capabilities, adapt in real-time and deliver insights or predictive analytics that were previously unattainable. These criteria help distinguish real AI solutions from those that merely leverage AI as a buzzword.
Why it’s so hard to assess AI capabilities
One major challenge with assessing AI capabilities in fintech solutions is data confidentiality. Finance professionals need to be cautious about using open-source AI platforms when working with sensitive financial data. For example, uploading confidential financial statements into a free AI tool like ChatGPT could inadvertently expose proprietary information, as the data might be used to train future models. By making sure AI solutions operate within secure, closed systems, organizations can both protect data privacy and maintain regulatory compliance.
Another pitfall is the tendency to equate faster decision-making and automation with authentic AI innovation. While AI can certainly improve efficiency and speed, these benefits alone don’t constitute true AI capabilities. Finance professionals need to critically evaluate whether an AI solution offers unique capabilities beyond what traditional automation can achieve.
Identifying true AI solutions
To navigate through the AI noise and identify bonafide AI solutions in fintech, finance professionals should look for key indicators of real AI value. These include:
- Self-learning systems: AI applications that continuously improve their performance based on new data inputs.
- Real-time adaptation: Solutions that dynamically adjust their algorithms and outputs without human intervention.
- Advanced predictive analytics: AI that provides insights or predictions that were previously unattainable, offering a competitive edge.
These capabilities can help you differentiate authentic AI-driven solutions from those that simply exploit the AI buzzword. Plus, they can help you to avoid paying a bigger price tag for a product marketed as having AI capabilities but that simply has basic automation built into it.
The future of AI in fintech
Looking ahead, I believe the hype surrounding AI in fintech will continue to grow and that AI truly will make our jobs easier one day. As more AI innovations emerge, their impact on the industry will become more pronounced, leading to exponential growth in AI adoption. But for now, the reality tends to fall short of the promises.
AI has the potential to redefine the fintech industry, bringing innovations that were once in the realm of science fiction. But this potential can only be realized if we approach AI with a critical and informed mindset. It’s crucial to challenge the hype and push for actual breakthroughs that meet rigorous standards of what true AI should be.
The promise of AI in fintech is great, but so are the pitfalls of overhyped solutions. By distinguishing real AI innovations from mere marketing gimmicks, we can pave the way for AI to truly transform financial services, fostering advancements that are not only cutting-edge but also impactful. Let’s steer the conversation towards meaningful progress, making sure that AI delivers on its promise and creates a tangible, positive change in the financial industry.
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