The rapid evolution of technology, coupled with the widespread availability of Big Data, has transformed the financial sector into fertile ground for the implementation of innovative AI-based solutions.
In the ever-changing fintech landscape, several key trends are expected to dominate 2024, promising to reshape traditional banking practices, improve cybersecurity measures, revolutionize cryptocurrency trading, and transform customer interactions.
In this comprehensive article, The Recursive reached out to industry experts and entrepreneurs to reveal their views on the hottest trends in AI in finance in 2024.
Improve accuracy 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 incorporating alternative data sources, including social media data and telecommunications records.
“Credit assessments to determine the risk of issuing a loan to a customer are becoming more accurate through the use of alternative data sources. Companies such as ZestFinance and Upstart use social media data, information from cell phones, telecommunications data and other unconventional sources to create a more complete picture of customer creditworthiness,” Dmytro Zarakhovych, co- founder and managing partner of the Ukrainian national payment service UAPAY. and the company TechFin IGAMAtells The Recursive.
According to him, this trend is expected to continue in 2024, leading to more accurate and reliable lending decisions.
Automatic Trading and Investing with AI-Driven Strategies
AI has significantly expanded the scope of automated investment solutions. Platforms like Robinhood leverage AI algorithms to facilitate automated trading in various markets, allowing investors to create personalized strategies executed by AI systems.
These advances allow investors to optimize their trading strategies, automatically execute buy or sell orders, and react quickly to market fluctuations. This trend is expected to continue, offering investors innovative tools for efficient portfolio management, experts point out.
“AI-driven trading robots can manage multiple trading pairs simultaneously and respond to market fluctuations in real time, leading to faster and more accurate executions,” explains Mykhailo Romanenko, co-founder of the global ecosystem fintech and crypto exchange Kyrrex.
Customer support systems with 24/7 assistance via AI chatbots
The integration of AI-based chatbots and interactive customer support systems has revolutionized customer service in the financial industry. These systems operate 24 hours a day, providing users with instant access to information and support. Not only do these solutions improve customer satisfaction, they also reduce the workload on help desks, resulting in significant savings for financial institutions.
“Soon, it will be standard for Generative AI to communicate directly with customers to answer questions about accounts and personal financial needs. In the future, 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 will also see greater integration of AI across teams and channels. Currently, many institutions are siled and AI data is not optimized across teams. As AI is integrated beyond digital channels, these silos will be broken down to serve both customers and employees. For example, in banking, we expect AI to be used in physical locations, ATMs and mobile banking in 2024 and beyond,” says Gorelov.
The art and science of generative AI in finance
When we talk about generative AI, it is indeed a very popular trend that will gradually transform the financial sector, experts say.
“Let’s say you’re a finance professional and you want to create data visualizations for key stakeholders or customers. AI image generators are becoming sophisticated enough to turn bland investment charts or spreadsheets into visually appealing charts. Other real-world applications for using generative AI in finance include customer service interactions (AI chatbots), ideation of investment strategies, or using a generative AI model to automate tasks such as risk assessment and fraudulent transaction detection,” Ryan Doser, US-based entrepreneur. and vice president of inbound marketing at Empathy First Media, reports The Recursive.
Leveraging AI for Fraud Detection and Prevention
The increasing incidence of financial fraud requires advanced detection and prevention solutions. For example, AI-based algorithms, developed by companies like Feedzai and Kount, analyze large data sets to detect fraudulent activity and identify deceptive behavior patterns. Zarakhovych of UAPAY adds.
“Improving algorithms for detecting deception and fraudulent behavior is a crucial task in light of the increasing number of financial attacks,” he says.
In turn, these sophisticated models will also improve the sector’s ability to combat financial crime, ensuring secure transactions and building customer trust.
Mitigating Bias and Ensuring Ethical AI Practices
As AI permeates various facets of the financial industry, mitigating bias and ensuring ethical AI practices is paramount. Seamless training of AI models, regulatory compliance and accountability are essential steps to mitigate risks associated with AI implementation, particularly in credit assessment processes.
“The main path forward to mitigate risks is through accurate model training, transparency, accountability, but also regulation. The consumer lending process must be monitored and financial institutions must be able to transparently demonstrate to customers how the approval process is conducted and how assessments and decisions were made. ” Christian Wolf, head of strategic partnerships and ecosystems at Raiffeisen Bank International, explains to The Recursive.
Improve surveillance, detection and defense
Integrating AI into cybersecurity measures provides a strong defense against evolving cyber threats. AI algorithms enable real-time monitoring of data access and user behavior, providing accurate risk analysis and improving 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 with most other AI use cases today, cybersecurity applications focus on extracting or generating data. Here specifically, AI can help monitor data access and suspicious behavior, verify users using behavioral data, provide risk analysis, and more. As in other application areas, the risk lies in the quality of the AI models used – their accuracy and their nature. their prejudices,” emphasizes Wolf.
Additionally, AI technologies can be leveraged to develop proactive defense mechanisms, such as identifying malicious content in files and protecting sensitive data from cyberattacks.
Balancing Innovation and Regulation in AI-Driven Algorithmic Trading
The popularity of AI-driven algorithmic trading introduces new challenges related to market stability, as regulators seek to strike a delicate balance between adopting technological advancements and upholding legal standards. A critical assessment of the potential benefits and risks associated with AI-based trading solutions is essential.
“It is important to establish a clear link between the potential of technology and its responsible use. The regulator’s task is also to ensure our compliance with legal requirements and standards. The balance between the two should aim to critically weigh the potential of the technology and its latest advances, against the risks and legal issues involved in implementing certain solutions,” notes Wolf.
Additionally, by supporting a collaborative approach between industry players and regulators, the financial sector can harness the transformative power of AI-driven trading while preserving market stability and investor confidence.
AI as an enabler, not a substitute for humans
The growing role of AI in finance is transforming the employment landscape within the sector. As AI automates operational tasks and reduces manual work, it allows employees to focus on creative, impactful work, driving innovation and long-term strategic planning.
Rather than replacing human jobs, AI serves as an enabler, improving productivity and efficiency across various areas of the financial industry.
“AI technology is a tool that will continue to develop; it’s up to us to learn about it and use it in ways that support our work, rather than blindly relying on it to do so,” says Wolf.
Looking to the future, the financial sector is ready to welcome new AI-driven innovations. Generative AI, which enables large language models like ChatGPT to generate various forms of content, is expected to revolutionize customer interactions, automate tasks and improve ideation of investment strategies.
In the field of cryptocurrency trading, AI is expected to play a central role in analyzing market data, predicting price movements, managing risks and optimizing trading strategies. Additionally, the use of AI in software development, especially in creating adaptive technologies, promises a more streamlined and user-friendly experience across different devices and platforms.
So, as these trends continue to shape the fintech landscape in 2024, the merger of AI and finance will herald a new era of efficiency and security within the financial sector.