1. Machine learning biases

AI models inherit biases present in the data sets they are trained on. It’s basically garbage in, garbage out. Suppose the training data contains false information, cognitive prejudices, or is not representative of the population as a whole. In that case, the AI’s output will learn and amplify these biases, making them more prominent in its predictions and decisions. 

Unchecked biases lead to unintentional unfairness in how you treat customers. Let’s say you use AI software to review loan or mortgage applications. If the training data contains racial prejudices, the AI might automatically refuse applications from people from a particular demography despite meeting the loan or mortgage requirements. 

The best way to reduce bias is to train your AI model on diverse data sets. Ensure that the data used to train the AI model represents the population that the model will make predictions or decisions about. This can be done by collecting data from various sources and removing sensitive variables that can skew the AI’s outputs. 

If you have no control over the AI model's training data set — like in the case of large language models such as Bard and GPT4 — have humans review the AI’s decisions based on well-defined criteria.

2. Lack of transparency

Most AI models are black boxes. They make decisions but do not explain their reasoning for these decisions. In other words, it's hard to tell how or why the AI model chose one output over other options. 

“Just like our human intelligence, we have no idea of how a deep learning system comes to its conclusions. It “lost track” of the inputs that informed its decision-making a long time ago. More accurately, it was never keeping track.” says Samir Rawashdeh, Associate Professor at the University of Michigan-Dearborn. 

Without transparency, it's nearly impossible to troubleshoot or fix your AI software when it produces the wrong output. Let's say two people with the same profile and credit score apply for a loan, and your AI model rejects one of them. You cannot trace the AI’s thought process and see why it made this decision or explain the reasoning to the affected customer. This can lead to customer distrust, regulatory scrutiny, and dispute resolution difficulties.

To solve the black box problem, Dr. Marco Ortolani, in a conversation with S.I Ohumu on the You and AI podcast, advises companies to subject their AI models to the explainability test, especially when dealing with sensitive decision-making. 

On a basic level, the explainability test means probing the why behind every AI output. It's not enough for the AI model to make decisions; you need to be able to provide logical justifications for every decision in a way that makes sense to a human. Anything else and the decision-making process lacks transparency.

3. Regulatory challenges

There aren't enough laws governing how FinTech companies should integrate and deploy AI technology in their workflows. We’re just getting the first AI Regulation Act which addresses a limited aspect of this technology’s complexities. And there's a high chance that regulations will always play catch up with AI developments, given the rapid development of technology.

Without adequate regulations, companies risk using AI technology in unethical ways that expose customers’ data (and their own frameworks) to hacks and breaches. Take Amazon, for example, which had to restrict employees' ChatGPT usage over concerns that some of them had inputted sensitive company information into the LLM (Large Language Models) chatbot. 

In the absence of AI-specific laws, FinTech companies must ensure that their AI tools are transparent and accountable and operate within the boundaries of the existing industry regulations. Read the software’s usage terms to be sure that it adheres to copyright, data protection, and privacy regulations, before deployment. 

FinTech companies can also create internal AI regulations to guide the technology’s usage within the company. Have your legal and cybersecurity team review new AI tools and draft usage guides for the rest of the company to follow. These guides can be based on the existing industry laws and your company’s internal tech requirements. 

As a company with vast experience in building FinTech solutions, at STX Next we always ensure that our software meets all regulatory requirements. Our clients can rest assured that their products are of top-notch quality and compliant to the required standards.

4. Data breaches

Using AI software means sharing your data with large model databases that you have little or no control over. 

For example, any information shared with large language models might be automatically added to LLMs' training data and used to improve response quality for other users. Say you input your mobile app code into an LLM AI tool; it might become public information that your competitors or, even worse, hackers can access easily. These AI models retain these pieces of information — even after you delete your accounts. 

A data breach can lead to financial losses for FinTech companies due to legal penalties, regulatory fines, compensation to affected customers, and costs associated with remediation efforts. Stolen financial and personal data can be used for identity theft, fraud, and other malicious activities.

So how do you protect your information from AI data breaches and unauthorized access? It’s simple: Don’t share any personal private information (PPI) with AI tools. Keep information like customers’ financial records, company financial reports, codebase, and the like off the platform. 

Before inputting any data into the AI software, ask yourself, “Will I want millions of people to have this data?” If yes, go ahead with it. If not, keep that piece of information off the AI tool. 

Also, choose an AI model that’s willing to help you protect your data. Review the AI model’s usage terms to know how it uses shared data, and the security standards of the model. Check whether the system supports data encryption and its data privacy guidelines. 

If there are no clear security programs and data controls in place, it’s a sign that the AI software isn’t the best option for your organization.

At STX Next we place security at the top of our list, ensuring our products remain secure at all times. As a company, we’re currently in the process of acquiring ISO certification, which will help us even further secure our clients’ data.

5. Customer trust and acceptance

People are protective of their money. So, naturally, they will have some reservations about entrusting critical financial matters to a machine that even the experts do not fully understand. Cue the doomsday predictions about AI, and what you have are customers who believe that AI-driven systems are incapable of acting in their best interests. 

The key to winning customers’ trust? Lead with transparency. When customers are unfamiliar with AI algorithms or have concerns about data privacy and security, they will become uncomfortable entrusting their financial matters to the machines. 

FinTech companies must provide crucial information about their AI model, including: 

  • How the algorithm works
  • How they integrate AI into existing workflows
  • How they will train the AI model, and which data sets will be used for this purpose

Create and share a detailed AI policy with customers from day one. Of course, this document will change as the technology evolves, but your customers need to know you have a well-established plan for how you will use AI without risking their experience and data security.

6. Lack of skills

Many businesses lack the in-house expertise necessary to design, develop, deploy, and manage AI systems effectively. They mostly rely on knowledge from third-party providers who can easily skimp on core security and regulatory requirements. 

Before investing in an AI model, conduct internal training for your staff to help them understand the basics of the technology. This way, they can collaborate effectively with third-party experts and align expert recommendations with your organization’s AI regulatory requirements.

The threats of AI in FinTech are real but surmountable.

AI systems can boost your efficiency, improve fraud detection, and help you deliver high-quality customer experiences. However, this technology is fallible. Without proper guardrails around deployment, AI models can cost your organization customer trust and lead to long-drawn battles with regulators. 

The risks and challenges we’ve shared in this article aren’t to discourage you from integrating AI systems into your FinTech company’s workflows. 

Instead, this knowledge will help you use AI ethically, and proactively address these risks and challenges before they become bigger issues that affect your organization and customers.

At STX Next, we build custom solutions powered by AI for various types of industries, including FinTech. We have successfully collaborated with all sizes of companies, from startups to big enterprises, and with various budgets. We approach each client individually, always putting quality and security as our top priority.

If you feel like you would love to know more about FinTech development, click the link to go to our website.

And if you’re currently looking to implement AI within your FinTech software, are thinking about expanding your offering, or need expert help and guidance, don’t hesitate to reach out. Let us guide you through your digital transformation and prepare your business for the future.

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