The financial services industry has always been racing to implement the newest technologies. Back in the 1960s, various financial institutions competed to introduce ATMs. In the 2020s, it’s AI’s turn to deliver the utmost value to fintech customers.
Modern finance infrastructure relies on AI-based fintech trends and solutions. Applications such as Venmo, Paypal, Wise, Apple Wallet, and other apps are the primary examples. With them, users can purchase insurance, apply for loans, or buy cryptocurrency without leaving their homes.
With the growing demand for fintech services, the rise of AI is rapidly reshaping the future of fintech itself. According to NVIDIA’s State of AI in Financial Services: 2024 Trends Survey Report, 43 per cent of global financial services professionals already use generative AI in their organization. Forty-six per cent of them are already using large language models (LLMs), too.
Catching up with AI trends is mandatory in maintaining a competitive edge. The NVIDIA report reveals that 97 percent of surveyed companies plan to quickly invest in more AI tools. By next year, projections suggest that the global AI in finance technology market will rise to $26.6B.
Here are ten of the top AI trends expected to influence the fintech industry:
1. Customer Insights
Many AI tools enable analysts to crack customer behaviour and preferences. From the data, fintech companies can craft even more personalised experiences.
Customer insights can be inferred from various sources. For example, HSBC’s AI tool analyses a customer’s transaction history, coupled with their social media activity, to provide investment advice and product offerings. The approach has been said to improve customer satisfaction and retention rates.
Another digital banking company, Revolut, uses machine learning algorithms to perform similar tasks. It provides AI-based budgeting and investment advice, as well as financial planning strategies.
2. Robo-advising
More financial institutions are exploring chatbots and virtual assistants with the ability to provide recommendations. According to research by Polaris, the Robo-advisor market is anticipated to grow from $7.39B in 2023 to $9.5B in 2024.
NVIDIA’s report also reveals that 34 per cent of financial services professionals sought AI’s help to enhance the experience of their customers. For instance, Bank of America’s virtual assistant, Erica, is equipped with AI insights to provide customers with real-time assistance.
3. Customer Onboarding with AI
It is commonly known that customer onboarding processes, especially in financial services, are often time-consuming. Many companies are looking to counter this by using AI tools that can automate compliance checks and document processing.
For example, the Oxford startup Onfido uses its proprietary AI, Atlas, to automate identity verification during customer onboarding. Atlas’s method include cross-referencing documents like passports and driver’s licenses with facial biometrics.
4. Robotic Process Automation (RPA)
Robotic Process Automation, as the name suggest, is a way to automate repetitive tasks. In various companies across the world, this technology has been transforming back-office operations.
By increasing effectivity, RPA allows companies to focus on value-added activities. JPMorgan Chase, for example, is able to cut the time to analyse legal documents through its COIN (Contract Intelligence) platform. The bank claims that COIN allows it to reallocate its resources to more strategic business endeavours.
5. Investment Management
More often than not, companies that use artificial intelligence systems to manage their investment benefit from better portfolio diversification. Independent investors who have converted to AI-driven services, too, seek ways to maximise the returns on their investment.
Wealthfront, a California-based investment firm, is a standout example of how a company can wield AI to improve its investment services.
Its platform formulates personalised investment plans based on risk tolerance and financial goals. With Wealthfront, investors also gain access to continuous portfolio optimization and tax-efficient investing.
6. Credit Scoring with AI
Traditionally, scoring models only process limited data. This can often lead to biases, especially for outdated models. In comparison, AI-based credit scoring that analyze broader data sources can assess creditworthiness in a more accurate manner.
This means improved access for underserved populations, on top of reducing default rates for lenders. California-based Zest AI, for instance, offers an AI-powered credit scoring platform that uses a tool called FairBoost to give a more holistic view of a borrower’s creditworthiness.
Ant Financial from Alibaba Group also utilises an AI tool called Zhixiaozhu 1.0 in credit scoring and risk management. Similarly, it uses machine learning algorithms to assess creditworthiness based on alternative data sources.
7. Regtech
Regulatory technology, which demand jumped last year, is a resource-intensive area for financial institutions. Therefore, AI automation has been a huge help in streamlining its processes.
In the field, artificial intelligence helps to guarantee financial institutions adhere to regulatory standards more efficiently and effectively. For example, De Nederlandsche Bank uses AI data analytics to detect networks of related entities. The process assesses the exposure of financial institutions to networks of suspicious transactions.
8. Payment Processing with AI
A lot of fintech companies are looking into AI to perfect their payment processes in terms of speed and security. The integration results in increased customer satisfaction, both for B2B and B2C companies.
The multinational finance company Stripe, Inc., for example, use AI tools to empower its digital payments processing. Now, customers can manage recurring billing effortlessly thanks to its advanced AI agents.
Stripe has also collaborated with Microsoft’s Azure OpenAI team to integrate GPT-3 for its support services.
9. Blockchain and Cryoto-related Services
AI improves the security and efficiency of blockchain and cryptocurrency transactions drastically. Some tools can perform difficult tasks such as predicting price movements, and optimise trading strategies.
A standout example is the American blockchain firm Chainalysis. For some time, the company has been helping prevent fraud and other illicit activities in the crypto space.
10. AML Compliance
Created to prevent financial crimes, Anti-Money Laundering (AML) regulations can benefit from the use of artificial intelligence. When integrated into the system, AI tools can efficiently detect malicious activities, which results in expedited AML processes.
For example, the financial crime detection company AyasdiAI creates AI application Sensa to help institutions with anti-money laundering (AML) compliance. AyasdiAI’s platform identifies suspicious activity patterns that traditional methods might miss. Its method reduces false positives in AML compliance efforts and increases overall accuracy.
AI in Fintech’s future
The trends outlined in this article represent the future of the fintech industry.
AI’s role in fintech will only continue to grow with more companies investing in its development. Soon, artificial intelligence will take on more sophisticated tasks that add to the value of fintech products and services.
- Artificial Intelligence in FinTech
