Introduction
Finance has seen an incredible evolution over the past few decades. Traditional finance once relied on manual processes, in-person banking, and legacy systems that often struggled to keep up with the fast-paced demands of global markets. With the rise of fintech, technology began to reshape how financial services were delivered. The introduction of digital banking, mobile payment systems, and robo-advisors allowed individuals to manage their money from the palm of their hands, while institutions used new tools to make markets more efficient.
Enter AI in Fintech: the next frontier. AI takes fintech to a whole new level by integrating machine learning, natural language processing, and predictive analytics into financial systems. This enables not only automation but also intelligent decision-making, real-time risk analysis, and personalized services at scale.
In this article, we’ll dive into AI use cases in fintech, looking at where it’s making a real difference now, where a hybrid human-AI model works best, and where AI is still not quite ready to replace human expertise.
Use Cases That Have Been Proven Success with AI in Fintech
AI in Customer Service and Relationship Management
AI-driven chatbots and virtual assistants are transforming the customer service landscape. They handle inquiries, manage simple transactions, and even provide financial advice, all while reducing operational costs. I’ve personally worked on integrating a chatbot as detailed in my article here, which resulted in greater efficiency.
The adoption rate of AI chatbots in fintech is high. Chatbots are commonly used across industries for routine customer service tasks. For example, Bank of America’s Erica, an AI-driven virtual assistant, helps millions of users manage their accounts, answer queries, and track spending in real-time.
In addition, banks can take AI-driven customer service further by replacing Relationship Managers for premium accounts with 24/7 premium concierge bots, supplemented by human agents when needed.
AI in Personal Finance Management
AI-powered personal finance tools have become essential for individuals seeking to better manage their spending and budgets. These tools use machine learning to categorize transactions, identify patterns in spending, and offer real-time budgeting advice tailored to each user’s financial habits. AI in fintech, specifically personal finance management not only simplifies complex financial decisions but also provides a more granular understanding of one’s finances.
A standout example of AI's impact on personal finance is Mint (now moved to Credit Karma), which uses AI to automatically categorize spending, recommend budget adjustments, and provide personalized financial advice. AI-driven personal finance tools like this empower users to track their spending with real-time updates, helping them to better manage their money and meet their savings goals.
AI-powered chatbots, like Cleo, take personal finance management a step further by offering real-time insights on how to save money. Cleo connects with users' bank accounts to analyze spending habits, provide encouragement, and suggest personalized savings tips, all while keeping the experience engaging and conversational.
In today’s world, with billions of transactions happening through digital payment systems like PayPal, Visa, and Mastercard, AI has the potential to process massive amounts of data to benefit both payment providers and consumers. AI can analyze spending patterns to provide personalized tips, identify opportunities for savings, and even detect inefficient spending habits. For example, it can flag recurring subscriptions that users may have forgotten, empowering them to take control of their financial health.
Moreover, fintech companies are increasingly using AI to predict future financial trends for individual users. By analyzing past spending behaviors and upcoming bills, AI tools can forecast financial challenges or opportunities, enabling users to adjust their habits before a problem arises.
This is particularly significant as new generations who are growing up in a digital-first world are embracing technology in managing their finances. In my own experience, I’ve used ChatGPT to help calculate my savings plan for paying off loans and planning for retirement, something that would have taken much longer using traditional methods. This natural adoption of AI as part of our personal finance management shows the growing need for fintech companies to leverage AI in building more innovative products that cater to this demand.
Basic Robo-Advisor
Robo-advisors have revolutionized wealth management, allowing users to manage their portfolios with little to no human interaction, and at a fraction of the cost. Traditionally, wealth management fees were based on a percentage of assets under management, which can quickly add up. Robo-advisors like Wealthfront have reduced these fees significantly by automating portfolio rebalancing and offering basic investment advice through AI.
The adoption rate is high, particularly for individuals seeking low-cost investment management. These robo-advisors handle basic tasks such as diversification and risk management, with minimal human intervention. For those who only need straightforward investment management, this approach is highly effective.
What makes Basic Robo-Advisors even more appealing is their ease of accessibility and low minimum investment requirements, which have opened the doors for a wider audience to invest. Unlike traditional financial advisors who might require a large portfolio to even consider you as a client, robo-advisors allow anyone with a smartphone or computer to start building their wealth with as little as $500 or less.
Moreover, robo-advisors are designed to offer services that adjust to your goals over time. They provide essential financial planning features like automatic portfolio rebalancing, tax-loss harvesting, and retirement goal-setting. These tools, typically reserved for high-net-worth clients with traditional advisors, are now accessible to the average user. Additionally, many of these AI systems are integrated with machine learning, so they can learn from market trends data available, helping clients optimize their investment strategies over time.
Fraud Detection via Predictive AI
Fraud detection is one of the most critical applications of AI in fintech. Predictive AI models analyze massive datasets in real-time, flagging suspicious activities that might be missed by traditional rule-based systems.
For example, I came across a story about a customer whose identity was compromised across multiple bank accounts. The Fintech company used AI to detect a potentially fraudulent transaction within an hour, sending the customer an alert before other banks even realized the scam had occurred. It created a viral moment and somehow, it increases the company’s branding.
AI-driven fraud detection systems like those used by PayPal and JPMorgan Chase are now essential in preventing financial crime, making the adoption rate for AI in this area very high. These systems allow fintech companies and banks to stay ahead of sophisticated fraud tactics.
Use Cases of Hybrid AI in Fintech, A Nice Blend with Human Intervention
Robo-Human Advisors and Market Analysis
While basic robo-advisors are excellent for handling routine investments, hybrid models that combine AI with human financial advisors are gaining traction, especially for more complex financial needs. Vanguard’s Personal Advisor Services is a prime example of this, blending AI-driven investment strategies with human oversight for high-net-worth individuals. Betterment, which positions itself as the largest independent digital financial advisor, released a survey revealing that 4 out of 5 financial advisors are now incorporating AI into their daily work.
The adoption rate for this hybrid model is moderate but definitely growing. AI’s ability to process and analyze large volumes of market data helps financial analysts generate real-time reports. It serves as a tool to optimize data analysis, but humans make the final investment decisions, especially in volatile markets.
Credit Scoring and Loan Underwriting
In traditional lending, banks rely on data from credit bureaus and loan applications to assess creditworthiness. AI can expedite this process by analyzing data points faster and more comprehensively. Companies like Zest AI and Upstart use machine learning to assess credit risk, particularly for individuals with limited credit histories. Zest AI uses machine learning to analyze non-traditional data (e.g., online behavior, social media) to generate more inclusive credit scores. Zest AI helps lenders approve loans for borrowers who may not qualify under traditional credit models. While Upstart employs AI to assess creditworthiness using more than just FICO scores, allowing them to offer personal loans with better approval rates for underserved populations.
Human oversight remains necessary for larger loans such as mortgages or business loans, where more nuanced judgment is required. AI can reduce bias and improve efficiency, but human expertise is essential for making the final call on complex cases.
AI in Compliance (RegTech)
AI in Fintech also applies on regulatory compliance, automating processes like anti-money laundering (AML) and know-your-customer (KYC) checks. For instance, HSBC uses AI to flag suspicious transactions, but human officers still review flagged cases. Standard Chartered leverages AI to monitor transactions and ensure regulatory compliance, using machine learning models to detect fraud, while human experts handle escalated cases.
While AI handles routine compliance tasks, the complexity of financial regulations means human oversight will continue to be necessary.
AI in Insurance (InsureTech)
In the world of insurance, AI can enhance decision-making by recommending the best financial products based on customer data. However, replacing human agents entirely is unlikely, given the traditional commission-based model. In fact, I recall a protest by insurance agents after a small policy price increase impacted their ability to sell new policies; a clear indication of the importance of human relationships in the industry.
AI can streamline the process of recommending products, but the human element remains vital, particularly in maintaining customer relationships.
Use Cases Where AI Is Not Yet Practical in Fintech
Pricing Insurance Policy Premiums
In the insurance industry, actuaries use complex models to calculate risk and premiums. While AI can assist with data analysis, human actuaries are still needed to handle regulatory considerations which is usually done with meetings the regulators (in-person or online meeting) and the complexity of risk evaluation when creating a new insurance product. The work of actuaries is highly specialized and requires a deep understanding of not only the data but also the legal and financial frameworks surrounding insurance, which includes mortality and morbidity tables, loss ratios, and actuarial models. Allstate’s AI platform assists in analyzing risks and setting insurance premiums, but human experts still oversee the final decisions to ensure accuracy and compliance with regulations.
Insurance Claims Processing
While AI in Fintech (or rather, InsureTech) can automate basic insurance claims, major claims particularly in health insurance—require human intervention. Medical professionals review claims to ensure diagnoses are accurate and meet the necessary medical criteria. Medical practitioners can distinguish between conditions that are pre-existing or new, helping insurers make fair and accurate decisions. In cases where claims are disputed, such as a denial of coverage, medical experts can serve as third-party evaluators, providing expert opinions that may be used in appeals or legal proceedings. This impartial expertise is invaluable for insurers to maintain transparency and fairness.
Similar to how doctors can’t be replaced by AI in medical practice, insurance claims processing, especially for complex or high-value claims, still requires human expertise.
Investment Analysis and Stock Market Prediction
AI systems like Bloomberg Terminal are great at processing past market data and generating insights, but they cannot replace human investment bankers entirely. Human emotions play a significant role in trading decisions, something AI lacks. Emotions can lead to both poor and excellent judgment calls, particularly in volatile markets. AI can assist in providing data-driven insights, but humans are still needed to interpret the nuances of market sentiment.
