Using Artificial Intelligence in Fintech:
A Comprehensive Overview
Artificial intelligence (AI) is transforming a wide range of industries, and financial services is no exception. AI and its related technologies, like machine learning and natural language processing, are being rapidly adopted within fintech, the industry term for technology-driven financial services innovation.
In 2024, AI is playing an increasingly important role in how consumers and businesses manage their money and conduct financial transactions. This article will provide an in-depth look at the current and near-future applications of AI in fintech. We’ll explore the major areas where AI is improving services and enabling new products, as well as some of the risks and challenges involved with using these still-emerging technologies.
To understand AI in fintech, we first need to step back and define artificial intelligence itself. At a basic level, AI refers to computer systems that are designed to perform tasks that would otherwise require human intelligence. This includes capabilities such as visual perception, speech recognition, decision-making, language translation and more.
The most common subset of AI development services used today is machine learning. This involves “training” algorithms on large sets of data, allowing the software to learn, identify patterns and make predictions or decisions with minimal human intervention. The more quality data it has access to, the smarter the machine learning application becomes over time.
Fintech refers to the wave of innovation applying cutting-edge technology to the world of financial services. This includes everything from mobile payment apps and automated investing platforms to cryptocurrency exchanges and AI-powered credit decisions. Many of today’s most exciting fintech breakthroughs involve artificial intelligence in some capacity.
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So, how is AI used in fintech? By combining AI with vast amounts of financial data and transactions, fintech companies are building intelligent services that simplify money management, provide insights and recommendations, enhance security and compliance, and expand access to financial products.
Artificial intelligence is making inroads across nearly every aspect of fintech in 2024. Here are some of the most important areas seeing rapid AI adoption:
The interface between most consumers and fintech artificial intelligence is their digital banking app or website. Leading platforms from vendors like Bank of America, Capital One and Chase are using AI to provide personalized recommendations, alert customers about unusual account activity, answer common customer service questions and simplify mobile check deposits.
Chatbots and voice assistants are being widely integrated into digital banking channels, leveraging natural language processing to handle routine customer requests involving account balances, bill pay, funds transfer and more. Capital One’s Eno chatbot can manage card lock/unlock, report lost credit cards and provide credit score updates.
One of the most active areas for AI in fintech is expanding access to loans and credit while improving risk modeling for financial institutions. By applying machine learning algorithms to thousands of applicant data points, lenders can reach more credit-worthy individuals, often with near-instant loan decisions.
Upstart, a leading AI and fintech lending platform, claims its underwriting model can expand credit access by 30% compared to traditional FICO-based lending. Applicants provide basic personal details which feed into Upstart’s AI system. It evaluates variables well beyond the credit score, including education, employment history and other signals. This allows more personalized rates and decisions.
On the institutional side, banks and other lenders are using AI to improve their overall risk modeling. Machine learning analyzes countless historical repayment records, macroeconomic trends, psychographic data and more to optimize interest rates and better predict delinquencies across entire lending portfolios. This prevents both missed revenue opportunities and excessive defaults.
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Algorithmic trading powered by AI represents over half of all buying and selling activity in US stocks. These lightning-fast transactions rely on complex machine-learning algorithms to analyze markets and execute millions of orders per second.
While mostly limited to institutional investors, fintech and AI are also infiltrating mainstream retail trading and investing apps. The AI features in leading investment apps like Robinhood and Acorns allow everyday traders to optimize portfolios, take advantage of market movements and implement automated strategies that were once exclusive to hedge funds.
In passive investing, robo-advisors like Betterment and Wealthfront use AI to automate portfolio management, rebalance asset allocations and minimize tax implications. They provide algorithm-driven investment recommendations and personalized financial planning tailored to an investor’s goals and risk tolerance, all for a fraction of a human advisor’s fees.
With digital financial crime growing in scale and sophistication, AI now plays a crucial role in fraud prevention and security. Visa claims that $40 billion in fraudulent activity was blocked in 2023 thanks to its AI-powered fraud protection tools—nearly twice as much as in 2022. This allows them to spot criminal activity that would easily slip past rule-based systems.
Many banks are also implementing AI software to monitor transactions, catch money laundering schemes and analyze biometrics for multi-factor authentication and access controls. In addition, chatbots are being used in customer-facing security applications, gathering data to verify identities before allowing account access or transactions.
While less visible to end users, AI is transforming important backend processes for many financial institutions. Intelligent algorithms are automating tedious tasks like reviewing and approving loans, opening customer accounts, processing insurance claims and handling compliance reporting, saving costs and boosting efficiency.
JPMorgan Chase built an AI-based software robot named COiN that reviews commercial loan agreements in seconds. This robot does, in minutes, what takes lawyers and loan officers 360,000 hours per year. Thanks to AI-driven operations automation, the bank projects it will save over 350,000 work hours annually.
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Hundreds of startups, along with leading tech firms, are driving AI innovation in financial services. Some key players across major AI fintech sectors include:
In addition, tech giants like Amazon, Apple, Facebook, Google and Microsoft have entered fintech AI. They are infusing AI capabilities into payment tools, smart assistants, credit and lending APIs, along with data analytics solutions.
The most ambitious is Google’s Plex smart checking and savings accounts, launching in 2022. They will have no monthly fees or minimum balances and be tightly integrated with Google’s AI-powered money management features.
While artificial intelligence fintech innovation brings immense promise for transforming financial services, it also involves notable risks and challenges around issues like ethics, bias, privacy and security. The adoption of these still-emerging technologies requires responsible considerations regarding transparency and managing unintended consequences.
According to an Accenture study, financial services executives believe AI will have a moderate or high impact on ethics within the industry. As financial institutions weigh the pros and cons of expanded AI capabilities, some major benefits as well as areas of concern include:
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As artificial intelligence capabilities continue advancing over the next decade, virtually every aspect of managing money will become smarter, more personalized and increasingly automated for consumers and institutions alike.
Chatbots with natural language processing will be able to handle complex customer service issues, as well as provide proactive notifications and money-saving recommendations tailored to each user. Digital banking apps may function as personalized financial assistants and advisors.
On the institutional side, end-to-end process automation will streamline everything from customer onboarding to application reviews, documentation analysis, regulatory compliance and fraud monitoring. This will reduce overhead costs and back-office staffing needs.
However, expanded AI for fintech adoption also faces barriers in the form of strict data regulations, demands for explainable algorithmic decisions and potential biases that must be addressed responsibly. User trust also remains an open question.
As financial institutions balance innovation with ethics, one thing is certain — artificial intelligence will be integral to the future of how the world manages, moves and interacts with money.
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