Apart from the unbanked, every adult—and sometimes even children—interacts with financial services. The rise of fintech has revolutionized how we do this; it is rapidly taking over in-person financial services with software and apps that provide banking, lending, payments, investment management, and more. And, in turn, AI is rapidly taking over fintech. 

Although fintech was already on the rise, it was the Covid-19 pandemic that gave things a push, with consumers shifting to digital and contactless services. The number of fintech startups worldwide more than doubled over 2020–2023, reaching over 26,000 in 2023 (~40% of US startups) from over 12,000 in 2020.

Fintech AI: A Gamechanger 

As of 2022, nearly 80% of Americans said they used financial apps for payments, tax filing, online banking, investing, budgeting, and lending, among other things. AI technologies like machine learning, natural language processing, and predictive analytics enhance the efficiency of financial services. 

So what does this mean for the end user? AI in fintech can personalize customer interactions, manage risk and detect fraud, carry out credit scoring, manage investment portfolios, ensure compliance with regulations, and more. AI is no longer just a buzzword, but a catalyst for major change in the fintech landscape. 

And the industry’s projected growth agrees—the market size of AI in fintech is estimated at USD 14.79 billion in 2024 and is expected to hit USD 43.04 billion by 2029, indicating a CAGR of 23.82%. 

With all this growth and investment in fintech AI, the applications are evolving as rapidly as the technology itself. Here are a few of the biggest ways AI is being used in fintech:

Personalized banking and customer support

AI-powered chatbots and virtual assistants are revolutionizing customer service in banking. These tools rely on natural language processing to understand and respond to inquiries in a human-like manner, interpreting varying sentence structures, colloquial language, and even emotional cues. They are available 24/7 and can handle multiple conversations simultaneously, surpassing the scale of human agents.

AI-driven tools also provide personalized recommendations, assist with account queries, and guide customers through complex financial decisions. For example, by analyzing spending behavior, AI can offer customized savings plans or investment opportunities tailored to individual financial goals and habits.

Managing risk and detecting fraud 

AI can analyze vast amounts of data quickly, making it a great tool for fraud detection. AI and machine learning algorithms are excellent at identifying "outlier" transactions—those that deviate from typical patterns, which could be anything from unusual transaction locations or amounts to changes in spending frequency or patterns that could indicate a stolen card or account compromise. Machine learning models can also detect unusual patterns in financial transactions and alert financial institutions to potential fraud in real time. By crunching past transaction data, AI can also predict and assess credit risks.

Credit scoring 

AI is transforming the way credit scores are calculated. Traditional credit scoring systems rely heavily on historical financial data, which can exclude people with limited or no credit history. AI-driven credit scoring models, however, use alternative data sources such as social media activity, spending behavior, and mobile phone usage to assess creditworthiness, making credit more accessible to diverse groups of people. As mentioned earlier, it can also evaluate credit risk and other factors, enabling more informed decision-making on lending.

For example, it can quickly analyze large datasets of historical financial performance, including stock prices, company earnings reports, economic indicators, and geopolitical events, and identify hidden relationships between these. And with time series analysis, AI can predict market trends or asset performance based on past patterns. It can also be used to gauge market sentiment by analyzing huge chunks of unstructured data from news articles, social media, earnings calls, and financial reports.

Managing investments

AI is also changing the world of investment management. In traditional finance, investment decisions are based on human analysis. But now, AI algorithms can process vast amounts of market data to predict trends, automate trading strategies, and optimize portfolio performance. Investment managers are increasingly using alternative data, such as social media sentiment, web traffic, and supply chain data, alongside traditional financial data to gain unique insights into market trends. AI can process these much more efficiently than human analysts, allowing firms to capture signals from areas of the market that were previously overlooked. These algorithms can learn from data and continuously improve to maximize returns while minimizing risks. 

Compliance 

This is one of the most critical facets of the financial sector, and AI is playing an increasingly important role in ensuring adherence to regulatory requirements. It can automatically monitor transactions and detect potential violations, reducing the need for manual oversight. Anomaly detection is a key process in this, as are real-time alerts and behavioral analytics. AI-driven behavioral analytics can identify changes in user behavior, which could indicate fraudulent activity.

Leading fintech AI startups and how AI drives their offerings

While there are many fintech AI companies, several startups stand out in how they leverage AI to create innovative solutions that are disrupting traditional financial services. Here are a few of the top ones:

Upstart

Traditional loan approval processes rely heavily on things like FICO scores, which can exclude potential borrowers with limited credit history. Upstart’s AI-powered lending platform measures creditworthiness by analyzing alternative data points like education, employment history, and even the job market. This allows Upstart to offer more personalized loan options and better risk assessment, ultimately reducing defaults. Also, by combining machine learning with traditional credit metrics, Upstart has significantly reduced the cost of lending while widening access to credit for individuals who might otherwise be overlooked.

Kabbage

Kabbage’s AI-powered online lending platform uses data to provide fast and flexible small business loans. The company collates and analyzes various data from the borrower’s bank accounts, accounting software, and even social media activity. This allows Kabbage’s AI models to provide quick loan decisions, often in minutes, contrasting sharply with the time-consuming traditional processes. 

Zest AI

Zest AI is another startup in the AI-driven lending space. Its machine learning models help financial institutions make more accurate credit decisions by analyzing thousands of data points instead of relying on traditional credit scores. By using AI to predict credit risk more accurately, Zest AI helps lenders approve more loans while minimizing the risk of defaults. This has proven especially helpful to underrepresented and underserved populations, who may have limited credit histories.

Plaid

Plaid is a well-known name in the fintech space, offering a suite of APIs that connect consumer bank accounts to apps for payments, investments, and financial planning. It uses AI to enhance data extraction, financial insights, and secure transactions.

Trends in AI and fintech

‍Although AI-related partnerships saw a decline last quarter, AI- and GenAI-related product launches, collaborations, and integrations will continue to be a key priority for fintech companies, mainly owing to demand from consumers for higher efficiency and lower costs. 

Here are some key AI in fintech trends to watch:

AI-driven personalized financial services

Consumers are increasingly seeking personalized financial products and services, and AI is making this possible on a massive scale. Whether it’s insurance technology (InsurTech) or financial planning, AI is increasingly driving unique, custom experiences.

AI in InsurTech

Insurance is another area where AI is making significant strides. AI-powered InsurTech startups use machine learning to automate claims processing, risk assessments, and pricing models. AI can also predict claims based on historical data, allowing insurers to offer more competitive pricing and better manage risk. For consumers, AI-driven insurance products offer more customized policies and quicker claims resolutions.

Increased AI regulation

As AI increasingly integrates into financial services, regulators are looking closer at the ethical implications and potential risks. In response, we expect tightening regulation around AI in fintech, particularly in areas like data privacy, transparency in decision-making processes, and accountability for AI-driven actions. Ensuring that AI in fintech is fair, transparent, and bias-free will be a key priority.

AI-powered financial planning tools

Robo-advisors have already disrupted the wealth management space by offering algorithm-driven investment advice at a fraction of the cost of traditional financial advisors. AI will take this further by offering highly personalized financial planning tools that consider not just investment goals but also tax optimization, retirement planning, and other aspects of financial wellness. These will enable individuals to make smarter financial decisions with minimal human intervention.

For information on the broader trends impacting all of fintech, read our blog post on fintech trends. 

Conclusion‍

AI in fintech is ushering in a new era of financial services—one that is more accessible, efficient, and personalized. As AI in fintech continues to evolve, the possibilities for innovation are endless. Financial institutions, startups, and consumers alike will benefit from smarter, faster, and more secure services, making the future of finance brighter.

And if you’re looking for more information that will help you quickly make confident decisions with deeper insights into fintech and beyond, explore our platform with a personalized demo.