Artificial Intelligence Development in FinTech: Opportunities and Challenges

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Is there something AI can’t do in FinTech today? Machine learning algorithms help investors optimize their clients’ portfolios. LLM-powered personal finance management advisors become a competitive advantage for banks. Predictive analytics models calculate risk scores based on hundreds of data points to speed up loan underwriting.

Let’s break down the four opportunities that AI represents for FinTech. We’ll also take a look at four key challenges that may hinder the success of your AI project.

The State of AI in FinTech

According to NVIDIA’s 2024 report, the most common forms of AI used in finance are:

Data analytics (69%)
Data processing (57%)
Natural language processing (47%)
Large language models (46%)
Generative AI (43%)

As for the business functions that use AI solutions the most, operations are in the lead (46%), with risk and compliance (45%) close behind it. Marketing (34%) and sales (27%) functions are also among key AI adopters.

How AI Can Benefit FinTech: 4 Opportunities

Financial organizations leverage AI to power fraud detection, virtual financial advisors, and more.

Preventing Financial Crime

Failing to detect and prevent financial crime means financial losses – and non-compliance penalties from regulators. AI-powered data analytics can help avoid those penalties by detecting suspicious transactions and account activity more accurately and at scale.

Being Always There for Customers

The next generation of chatbots can do more than answer the customer’s questions, thanks to LLMs and generative AI. For example, Wells Fargo’s virtual assistant can complete tasks like paying bills for its users. The same bank also offers LiveSync, a chatbot that serves as a personal financial planning assistant.

Enhancing Risk Assessments for Faster Decision-Making

Risk management is the leading AI use case among financial organizations, according to NVIDIA’s report. After all, AI models can ingest hundreds of data points to:

Automate AML and KYC checks
Calculate risk scores during loan and insurance underwriting
Identify portfolio risks and appropriate mitigation strategies

Streamlining Compliance

With the help of AI developers, risk and compliance teams can leverage ChatGPT-like productivity tools to summarize and analyze regulatory documents. For example, Citibank’s team used one such tool to sum up 1,089 pages of new capital rules.

4 Challenges to Watch Out For

Adopting AI in financial services comes with its own set of challenges, from data issues and legacy infrastructure to AI expertise recruitment and retention.

Data Quality and Availability Issues

AI models require large amounts of high-quality data for training. Data issues represented the most pressing challenge for financial organizations in 2024, according to NVIDIA’s report.

These issues include:

Privacy concerns
Disparate locations
Data sovereignty

Legacy Infrastructure

The average age of a universal bank’s IT estate reaches 14 years, as opposed to 3 years for a digital bank, according to McKinsey. Outdated applications and infrastructure pose performance, interoperability, and integration issues for AI solutions.

So, financial organizations with substantial legacy estates and technical debt are likely to need to modernize their infrastructure before implementing AI solutions.

Evolving Regulatory Requirements

With the AI Act passed in the European Union, financial organizations have to ensure their AI models remain accurate and reliable. U.S. regulators across the states also have multiple AI bills targeted at their use in financial services on the agenda.

As the technology is relatively new, however, regulatory requirements may develop and evolve quickly across jurisdictions.

AI Expertise Challenges

To develop, implement, and maintain a reliable, scalable AI solution, you need solid AI/ML and data science expertise. Recruiting and retaining experts in these fields remains the second most prevalent challenge for financial organizations, according to NVIDIA’s 2024 report.

If you struggle to hire AI experts in-house, you can always turn to AI and data science companies to secure the needed expertise.

Final Thoughts

AI has the power to streamline compliance, prevent fraud more accurately, and improve customer experiences – and that’s just a short overview of its potential use cases.

However, implementing AI requires grappling with evolving regulatory requirements, legacy infrastructure, and data issues. Securing the right AI expertise to see your project through is also easier said than done.

Looking for a reliable AI development partner with experience in tackling challenges inherent to FinTech? Discuss your project with S-PRO, an AI partner with a decade of experience in multiple industries, including financial services.