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Hottest Trends and Predictions for AI in Finance: A Glimpse into 2024

In the ever-changing landscape of the finance industry, several key AI-driven trends are set to dominate 2024.
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The rapid evolution of technology, coupled with the widespread availability of big data, has transformed the financial industry into a fertile ground for the implementation of innovative AI-powered solutions.

In the ever-changing landscape of fintech, several key trends are set to dominate 2024, promising to reshape traditional banking practices, enhance cybersecurity measures, revolutionize cryptocurrency trading, and transform customer interactions. 

In this comprehensive article, The Recursive reached out to industry experts and entrepreneurs to unveil their insights on the hottest trends for AI in finance in 2024.

Enhancing precision and risk assessment

One of the most significant advancements facilitated by AI in the financial sector is the improvement of credit assessment processes. Traditional credit scoring methods have been overhauled by integrating alternative data sources, including social media data and telecommunication records. 

Credit evaluations to determine how risky giving a loan to a client might be are getting more precise thanks to the usage of alternative data sources. Such companies as  ZestFinance and Upstart use social media data, information from cell phones, telecommunication data, and other unconventional sources to create a more complete picture of clients’ solvency,” Dmytro Zarakhovych, co-founder and managing partner at Ukrainian national payment service UAPAY and TechFin company IGAMA, tells The Recursive.

Furthermore him, this trend is expected to continue in 2024, leading to more precise and reliable lending decisions.

“A hot trend will be investors using AI to get a third-party perspective to help fine-tune the risk profile for different allocation models for clients since AI will have access to more relevant data. This trend can also extend to regular people starting their own portfolios and can afford AI guidance,” Christopher J. Day, founder of Days Global Advisors, tells The Recursive.

Automatic trading and investments through AI-driven strategies

AI has significantly expanded the scope of automated investment solutions. Platforms like Robinhood leverage AI algorithms to facilitate automated trading across various markets, allowing investors to create custom strategies that are executed by AI systems. 

These advancements empower investors to optimize their trading strategies, automatically execute buy or sell orders, and respond swiftly to market fluctuations. This trend is set to continue, offering investors innovative tools for efficient portfolio management, experts point out. 

“AI-driven trading bots can manage multiple trading pairs simultaneously and respond to real-time market fluctuations, leading to faster and more precise executions,” says Mykhailo Romanenko, co-founder of global fintech ecosystem and crypto exchange Kyrrex.

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Customer support systems with 24/7 assistance through AI chatbots 

The integration of AI-powered chatbots and interactive client support systems has revolutionized customer service in the financial industry. These systems operate round-the-clock, providing users with instant access to information and assistance. Not only do these solutions enhance customer satisfaction, but they also alleviate the workload on support departments, leading to significant cost savings for financial institutions. 

Soon it will be standard that Generative AI communicates directly with customers from answering account questions and personal financial needs. Further down the road, we expect it to be common for Generative AI to share financial advice, helping to further democratize financial access,” Zor Gorelov, CEO and co-Founder of Kasisto, a company that provides conversational AI solutions for banking and finance, tells The Recursive.

And as AI technologies continue to advance, customer support systems are poised to become even more sophisticated and user-friendly.

“In 2024, we also see further integration of AI across teams and channels. Right now many institutions are siloed and AI data isn’t being maximized across teams. As AI is integrated beyond digital channels, those silos will be broken down to serve both customers and employees. For example, in a bank setting, we expect AI to be used across physical locations, ATM locations and mobile banking in 2024 and beyond,” Gorelov explains.

The art and science of Generative AI in finance

When talking about Generative AI, it is indeed a very hot trend that will steadily transform the financial industry, experts claim.

“Let’s say you are a finance professional who wants to create data visualizations for key stakeholders or clients. AI image generators are becoming sophisticated enough to turn bland investment charts or spreadsheets into visually appealing graphics. Other real-world applications for using generative AI in finance include customer service interactions (AI chatbots), ideation for investment strategies, or using a generative AI model to automate tasks like risk assessment and detecting fraud transactions,” Ryan Doser, US-based entrepreneur and and VP of inbound marketing at Empathy First Media, tells The Recursive.

Leveraging AI for detection and fraud prevention

The rising incidence of financial fraud necessitates advanced solutions for detection and prevention. For example, AI-driven algorithms, developed by companies like Feedzai and Kount, analyze vast datasets to detect fraudulent activities and identify deceptive behavior patterns, UAPAY’s Zarakhovych adds.

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An improvement of algorithms in deception spotting and fraudulent behavior patterns is a crucial task in light of the increasing financial attack numbers,” he says.

In turn, such sophisticated models will also enhance the industry’s ability to combat financial crimes, ensuring secure transactions and bolstering customer trust. 

Mitigating biases and ensuring ethical AI practices

As AI permeates various facets of the financial industry, mitigating biases and ensuring ethical AI practices are paramount. Transparent training of AI models, regulatory compliance, and accountability are essential steps in mitigating risks associated with AI implementation, especially in credit assessment processes.

“The main path forward in mitigating risks is through accurate training of models, transparency, accountability, but also regulation. The consumer lending process needs to be monitored, and financial institutions must be able to transparently demonstrate to customers how the approval process is conducted, and how assessments and decisions have been made. The regulator also has to be involved in monitoring this process: we are aware that the proposed AI Act by the European Parliament outlines that AI lending platforms used for services like credit scoring have to comply with high-risk AI requirements,” Christian Wolf, head of strategic partnerships and ecosystems at Raiffeisen Bank International, tells The Recursive.

Enhancing monitoring, detection, and defense

The integration of AI in cybersecurity measures offers a robust defense against evolving cyber threats. AI algorithms enable real-time monitoring of data access and user behavior, providing accurate risk analysis and enhancing security protocols. While the adoption of AI in cybersecurity is promising, organizations must remain vigilant about the quality and biases present in AI models. 

“As it is with most of the other current AI use cases, the applications in cybersecurity are focused on data extraction or generation. Here specifically, AI can enable monitoring data access and suspicious behavior, verifying users through behavioral data, providing risk analysis, etc. Similar to other fields of applications, the risk is in the quality of the AI models used – how precise they are and what biases they have,” Wolf points out. 

Additionally, AI technologies can be harnessed to develop proactive defense mechanisms, such as identifying malicious content within files and safeguarding sensitive data from cyberattacks.

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Balancing innovation with regulation in AI-driven algorithmic trading

The popularity of AI-driven algorithmic trading introduces new challenges related to market stability, as regulators look to strike a delicate balance between embracing technological advancements and ensuring adherence to legal standards. Critical evaluation of the potential benefits and risks associated with AI-driven trading solutions is essential. 

“It is important to make a clear connection between the potential of the technology and the responsible use of it. The task of the regulator is also to ensure our adherence to legal requirements and standards. The balance between the two must be aimed at critically weighing the potential of the technology and its newest advancements, versus the risks and legal issues  of implementing certain solutions,” Wolf notes.

Furthermore, by supporting a collaborative approach between industry stakeholders and regulators, the financial sector can use the transformative power of AI-driven trading while maintaining market stability and investor confidence.

AI as an enabler, not a replacement for humans

The growing role of AI in finance is transforming the job landscape within the industry. While AI automates operational tasks and reduces manual work, it allows employees to focus on creative and impactful work, fostering innovation and long-term strategic planning. 

Rather than replacing human jobs, AI serves as an enabler, enhancing productivity and efficiency across various sectors within the financial industry. 

“AI technology is an enabler that will continue to develop – it is up to us to learn about it and utilize it in a way that supports our work, rather than blindly relying on it to do it,” Wolf argues.

Looking towards the future, the financial industry is poised for further AI-driven innovations. Generative AI, which allows large language models like ChatGPT to generate diverse forms of content, is expected to revolutionize customer interactions, automate tasks, and enhance ideation for investment strategies. 

In the realm of cryptocurrency trading, AI is set to play a pivotal role in analyzing market data, predicting price movements, managing risks, and optimizing trading strategies. Furthermore, the use of AI in software development, particularly in creating adaptive technologies, promises a more streamlined and user-friendly experience across different devices and platforms.

Thus, as these trends continue to shape the landscape of fintech in 2024, the fusion of AI and finance will herald a new era of efficiency and security within the financial industry.


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Bojan is The Recursive’s Western Balkans Editor, covering tech, innovation, and business for more than a decade. He’s currently exploring blockchain, Industry 4.0, AI, and is always open to covering diverse and exciting topics in the Western Balkans countries. His work has been featured in global media outlets such as Foreign Policy, WSJ, ZDNet, and Balkan Insight.