Challenges the fintech industry faces with generative AI | TechTarget (2024)

Feature

As the new technology has exploded in other industries, financial organizations are also exploring how they can apply it. However, regulatory requirements hinder fast adoption.

Challenges the fintech industry faces with generative AI | TechTarget (1)

By

  • Esther Ajao,News Writer

Published: 17 Jan 2024

Generative AI is shaping every industry, including financial technology.

While generative AI holds much promise for fintech, the industry is still slow to adopt the technology.

One reason for that is the limited regulation that has emerged during the popularization of generative AI. Many financial institutions must be careful in their implementation of the new technology so that they can comply with regulatory requirements.

In this Q&A, Ronen Assia, managing partner at venture group Team8, talks about some of the problems facing the fintech sector in adopting generative AI and how the technology will shape the industry in 2024.

Team8 invests not only in fintech companies, but also in healthcare and other tech- and cybersecurity-oriented enterprises.

Editor's note: This interview has been edited for clarity and conciseness.

How has generative AI affected the fintech industry?

Ronen Assia: The evolution of generative AI has several impacts across different industries in different domains. [In fintech] we're getting more concerns when it comes to fraud, but also a lot more opportunities.

Challenges the fintech industry faces with generative AI | TechTarget (2)Ronen Assia

When it comes to the application level, a lot of institutions are using GenAI to accelerate or make more efficient direct customer support flows, which is probably the lowest-hanging fruit in terms of what you can do with this technology.

Generally, people are more focused on finding opportunities within the application layer versus finding more opportunities within the back end of the organization. If you think traditionally of a bank, the application is probably 5% of the effort, and the back offices 95% of the effort.

Driving efficiency through this technology makes more sense than just the application layer.

What is one concern for fintech companies in the generative AI market?

Assia: The big question is around regulation. Whenever there is a technology that creates so much buzz and so much concern, there will be regulation in this space -- and I would estimate some very significant regulation.

We see some early signs of new directives, specifically in the European Union, but also President Biden's AI executive order and several others in Asia that are starting to create some noise. But there is no enforcement yet, and there is no clear framework yet.

But we've seen, across the past two decades now, similar cases where regulation really stepped up and became something that everybody needs to comply with. For example, GDPR, the California Consumer Privacy Act and HIPAA.

When you think about GenAI, because of its implications for the organization, it's actually segmented. There's no single point of contact within the organization that just does AI. There's the engineering consequences. There's the data consequences. There's the modeling consequences. There's the application consequences. There's the actual regulation, if it comes to specific verticals. For example, hiring people in New York City now [requires a check for] bias within the models.

So we'll see more specific regulation going toward specific industries -- if it's recruiting people, if it's having models that have to do with credit, with underwriting, with screening clients or onboarding. We can think about different processes within financial organizations that will be probably treated differently.

There's also the more traditional financial-based compliance, accounting practices and things like that, which also, some people say, 'OK, it will make accounting easier or will make legal work easier.' But I don't see a judge yet getting through to the phase of 'Hey, this was done by ChatGPT, and now, please rule in favor or against.' It's much like this debate of if there is an autonomous vehicle, who's in charge if there is an accident?

You can't suddenly hand off all these human-based processes to a machine and say, 'OK, it's done.' So I think that's where a lot of the challenge lies. I do think that in 2024 and 2025, so probably two or three years, we'll see more regulation coming into the space.

What are some of the goals of fintech companies in using generative AI in 2024?

Assia: What I see is companies working on the application level. [For example], trying to replace what has traditionally been the role of the financial adviser -- that's one holy grail. It's almost unachievable today, thinking about replacing a human adviser, because there's so many different aspects that go into the space, but it's definitely a long-term vision, which we'll get there.

Basically, replacing all kinds of services-based financial advice and consulting with machines.

We see a lot of traction using GenAI with taxes, GenAI with legal work, GenAI with compliance, where understanding the text is not just 'write a summary of this Shakespeare play.' It's 'write a summary of the latest legal developments in Iowa when it comes to specific state center DML [direct mortgage loan] regulation.' Today, you would need a fancy law firm, which will cost you hundreds of thousands of dollars to get you that type of opinion. But maybe in the future, this type of work can be done way more efficiently using machines.

When you're dealing with fintech in general, the level of accuracy that is required is so high.

Having said that, the thing that we do see is that you still need the human in the middle, a professional.

If it's in the taxes space, you'd need a CPA who will actually make sure that the machine does what it should, because the technology is simply not there yet. There are concerns as I've stated before about regulation and quality assurance. If you give the GenAI engine a piece of a legal document, it might still be 50% wrong.

When you're dealing with fintech in general, the level of accuracy that is required is so high. The data hygiene factor has to be absolutely spotless if you want to give financial services. It's not just writing a script, birthday card or poem. The level of trust and liability has to be way, way beyond that.

The next sort of jump in technology between 'write me a nice birthday greeting for my 10-year-old son' versus 'write me a good summary of this legal document,' there's a big leap there. With the development of technology, we'll definitely get there, but it will take a bit longer in fintech.

How will the fintech market change in 2024 as it relates to generative AI?

Assia: Every startup I see has 'GenAI' in the first paragraph. A year from now, that will not make sense anymore. It's just going to be that everybody's using it because it's a good and efficient way to use technology. It becomes part of the toolbox. You will not have to explicitly say that you're using GenAI to do this or that.

When interest rates change, it intrinsically changes the product. That's why fintech has been hit harder by interest rate hikes versus other industries. With interest rates stabilizing, I think there will be more optimism within the broader fintech industry. We will see the return of more DTC [Depository Trust Company] plays because the cost of creating an application or cost of creating a business will become lower using some of these technologies.

I'm definitely optimistic. I think we will see more funding, more companies and more exciting stuff that goes to market.

Esther Ajao is a TechTarget Editorial news writer covering artificial intelligence software and systems.

Next Steps

Businesses confront reality of generative AI in finance

Dig Deeper on Enterprise applications of AI

Challenges the fintech industry faces with generative AI | TechTarget (2024)

FAQs

What are the challenges of AI in fintech? ›

But before you go implementing a new AI solution into your fintech product, consider the known challenges first. Data security, bias, exclusivity and a flawed user experience are major risk factors.

What are some of the challenges faced in generative AI? ›

Generative AI Problems
  • Data quality and bias. ...
  • Ethical and regulatory considerations: Walking the tightrope. ...
  • Robustness and security: Protecting the system and data. ...
  • Interpretability and explainability: Building trust with agents. ...
  • Scalability and efficiency: Balancing resources and results.

What are the generative AI use cases in fintech? ›

Among the top generative AI use cases in fintech, experts have named fraud detection, transformation from digital into intelligent organization, augmenting human capabilities through automation, accelerating regulatory compliance, and mitigating economic crimes.

How will AI impact fintech? ›

The future of AI in fintech

The integration will span customer service, risk management and regulatory compliance. New AI applications and use cases will emerge. These include AI-powered financial planning, automated investment strategies and predictive maintenance for fintech infrastructure.

What is the biggest challenge facing AI? ›

The Biggest AI Challenges & How to Address Them
  • Ensuring Data Security.
  • Handling Large Datasets.
  • Optimizing Data Management.
  • Computational Power and Energy Consumption.
  • Efficient AI Computation.
  • Compatibility Issues.
  • Seamless Integration Solutions.
  • The Need for Adaptive AI Systems.
May 29, 2024

What is the most famous generative AI? ›

The best generative AI tools at a glance
CategoryBest for
Wondershare FilmoraAI video toolsAI video editing
MidjourneyAI image toolsHigh-quality results
Adobe PhotoshopAI image toolsAI-powered editing
DALL·E 3AI image toolsEase of use
16 more rows
Jun 7, 2024

Is generative AI a threat? ›

Generative AI can create malware that adapts and evolves to evade detection by traditional antivirus and malware detection tools. Generative AI can analyze individuals, systems, and software for vulnerabilities to launch more targeted attacks.

What are the applications of AI in fintech? ›

What does AI Mean to Fintech Exactly?
AI ApplicationBenefits in Fintech
Payment Processing AutomationReduces errors, speeds up transactions, and lowers operational costs.
Credit Scoring AlgorithmsProvides more accurate risk assessments and offers greater financial inclusion.
2 more rows

Why are FinTechs struggling? ›

Challenges And Opportunities Ahead

One of the main issues is cybersecurity since fintech businesses handle sensitive financial data, making them easy targets for cyberattacks. Furthermore, there is increasing competition in the fintech sector as both new and existing businesses compete for market share.

What is lacking in the fintech industry? ›

The fintech industry faces multiple challenges. We can point out such significant ones as repetitive security breaches, low transparency, high competition, legal regulations, and a poor user experience. You can mitigate these issues when the right tech expertise is applied.

What are the risks of fintech technology? ›

These risks can arise from various situations, such as data breaches, contractual breaches, fraud, or any other financial losses. Fintech companies are particularly susceptible to liability risks since they handle large amounts of money and assets.

What are the challenges of artificial intelligence in financial services? ›

The 10 biggest challenges in AI adoption for financial services
  • Algorithmic bias in financial decision-making. ...
  • Data security in financial transactions. ...
  • Deployment lag time for financial models. ...
  • Cybersecurity risks in financial AI. ...
  • Data privacy and compliance in financial AI. ...
  • Opacities in financial AI models.

What are the threats of AI in finance? ›

However, hallucination, algorithmic bias and vulnerability to data quality issues present risks to the accuracy of AI predictions. If financial entities base their decisions on faulty AI predictions which are not checked, this could lead to outcomes that may result in economic losses or even disorderly market moves.

Which of the following are challenges of AI? ›

I look forward to career advancement and participation in solving industry-aligned Artificial Intelligence and Machine Learning problems.
  • AI Ethical Issues. ...
  • Bias in AI. ...
  • AI Integration. ...
  • Computing Power. ...
  • Data Privacy and Security. ...
  • Legal issues with AI. ...
  • AI Transparency. ...
  • Limited Knowledge of AI.
Jul 31, 2024

What are the challenges of AI in accounting and finance? ›

AI in accounting faces challenges such as data security, the cost of technology adoption, potential programming biases, and the need for continuous updates.

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