The rapid adoption of artificial intelligence in finance has been nothing short of revolutionary. Financial firms have embraced generative AI at an impressive rate, with 78% already using it for at least one application. This swift integration showcases how AI continues to transform financial services, from detecting fraud to creating customized banking experiences.
Artificial intelligence and machine learning are reshaping the landscape of banking and financial services. These technologies enable quick data analysis, better risk management, and precise forecasting. AI-powered solutions have also improved customer service through smart chatbots, strengthened fraud prevention systems, and made financial planning accessible through robo-advisors.
This article explores 10 practical ways financial services use AI, with examples from industry leaders. You’ll see how major banks and financial organizations utilize AI to change their operations, boost customer experiences, and drive innovation in the finance industry.
Customer Acquisition: AI-Driven Targeting and Onboarding
Financial institutions now use AI technologies to accelerate how they attract and onboard new customers. This move goes beyond simple automation and creates more intuitive, individual-specific experiences throughout the customer’s onboarding journey.
Personalized Marketing with Predictive Analytics
The financial services industry has discovered the power of data-driven customer segmentation through AI. Predictive analytics delves into customer transaction history, digital banking behaviors, financial goals, and external economic factors to create detailed microsegments based on customer likelihood to open high-value accounts. Businesses that use personalization strategies see a 10-15% increase in revenue and better customer acquisition efficiency.
More importantly, predictive analytics helps financial institutions identify at-risk customers, refine retention strategies, and deliver smart product recommendations. Banks can tailor offerings to individual priorities, which then boosts satisfaction and loyalty. However, only 29% of regional and community financial institutions believe they can deliver data-driven, relevant product recommendations effectively. This highlights a significant opportunity for improvement in AI financial analysis.
AI Chatbots Streamlining the Application Process
AI-powered chatbots have evolved dramatically, now using natural language processing to elevate customer engagement to a uniquely human level. These virtual assistants reduce friction in the application process while offering major business benefits:
- Financial institutions using conversational AI solutions have seen a 4x increase in converted sales and a 20% increase in consumer satisfaction
- AI chatbots can handle hundreds of transactions and questions simultaneously, unlike human agents who focus on one request at a time
- By 2030, the global market for chatbots in banking, financial services, and insurance is expected to reach USD 7.00 billion, up from USD 586.00 million in 2019
These digital assistants guide customers naturally through complex processes such as loan applications and account openings. They collect critical information before escalating to human agents when needed. Over 43% of customers in the USA now prefer using chatbots for banking problems rather than visiting branches, showcasing the growing acceptance of AI banking solutions.
Digital Identity Verification Using Computer Vision
Branchless challenger banks and remote onboarding needs have made digital identity verification essential. AI-powered solutions now verify government-issued ID documents and use biometric validation to ensure the person in a selfie matches their ID document.
Computer vision combined with machine learning can verify thousands of global ID types through forensic analysis quickly. Banks can now implement fully digital onboarding without paper forms or branch visits. This removes accessibility barriers that exist in traditional manual processes and enhances cybersecurity measures.
AI verification solves serious problems in traditional onboarding, including time-consuming paperwork, security risks with physical documents, accessibility issues, limited service hours, and high operational costs. Customers can now be onboarded securely in minutes instead of days, demonstrating how to use AI in finance effectively.
Deloitte’s research shows that 86% of financial services AI adopters believe AI will be very or critically important to their business’s success in the next two years. This highlights how these technologies are reshaping the way financial institutions acquire and engage customers, underlining the benefits of AI in finance.
Financial Decision Support Through AI Advisors
AI has become a powerful partner in financial decision-making, offering customized advice that was once only available through costly human advisors. Smart systems now analyze complex financial data to provide practical insights to both institutions and individuals, showcasing the potential of AI solutions for finance.
Morgan Stanley’s Next Best Action Platform
Morgan Stanley transformed advisor efficiency with its Next Best Action (NBA) platform in 2018. This AI-driven system serves two main goals: it creates customized client communication and works as a smart recommendation engine for investment ideas. The platform showed impressive results when advisors used it over 11 million times in just the first two months of the pandemic.
The NBA system significantly reduces the time needed to create custom investment recommendations. “It used to take about 45 minutes to come up with a personalized investment idea for a client; now it’s instantaneous,” says Jeff McMillan, Morgan Stanley’s Wealth Management Chief Analytics and Data Officer.
The platform works in conjunction with BlackRock’s Aladdin Wealth risk management system to continuously screen client portfolios for various risk factors. This partnership sends proactive risk alerts that lead to valuable discussions between clients and their advisors, demonstrating the power of AI for financial services in enhancing decision-making processes.
AI-Powered Budgeting Apps Transforming Personal Finance
AI-powered budgeting tools have revitalized personal financial management. These tools track spending patterns, sort transactions automatically, and provide tailored recommendations. Users can see their financial position clearly while getting practical insights based on their spending history.
Cleo stands out as an AI budgeting assistant. It tracks monthly bills, analyzes spending, sends low balance alerts, and creates custom plans. Advanced artificial intelligence helps the app sort expenses and find spending patterns, giving detailed insights and timely reminders.
Other platforms use forecasting technology to predict monthly expenses and suggest the best savings strategies. Smart analysis warns users before they exceed budget limits or need extra funds to avoid overdrafts. In fact, AI budgeting apps help people of all income levels by offering valuable views and guidelines for smart money management, showcasing how AI financial analysis can benefit individual consumers.
Retirement Planning with Intelligent Forecasting Tools
AI-powered calculators and forecasting tools have made retirement planning more sophisticated. These solutions help assess retirement readiness and explore ways to optimize your financial future, demonstrating practical AI applications in finance.
Charles Schwab’s Retirement Calculator gathers information about current age, planned retirement age, and investment style. It determines if you’re saving enough for expected retirement expenses. Fidelity’s Retirement Score assesses savings strategies through six simple questions. It then shows how changes to monthly savings, retirement age, or lifestyle expectations might affect long-term results.
WealthTrace offers detailed retirement projections with daily account updates for those seeking more thorough analysis. This planning software helps users check their retirement funds, spot potential resource depletion points, and test various scenarios to overcome financial challenges. The simple individual plan costs $229 a year, though many quality tools like Stash’s retirement calculator are free.
Small Business Lending Decisions Enhanced by AI
AI systems have revolutionized small business lending by looking beyond traditional credit metrics. These platforms analyze business finances to determine lending eligibility within minutes, which helps avoid denials that could hold up funding for months.
AI algorithms process vast amounts of data to assess transaction histories, market trends, and individual financial behaviors. They create custom lending recommendations that align perfectly with business needs. Financial institutions that use AI-based underwriting have seen approval rates for small businesses in protected categories jump from 38% to 82%.
This technology accelerates assessments and makes them more automated, providing SMBs with quick financing solutions. AI-powered cash flow analysis helps lenders identify high-risk borrowers early, allowing them to focus on quality applications while reducing customer acquisition costs. This use case demonstrates how AI solutions for finance can improve efficiency and accuracy in lending decisions.
Transaction Intelligence: Making Payments Smarter
AI technologies are reshaping payment systems worldwide by boosting security, streamlining operations, and improving cross-border transactions. Smart solutions that protect customers while creating seamless payment experiences have caught the attention of financial institutions, showcasing the potential of AI in banking and finance.
Visa’s AI Fraud Prevention System: Preventing $25 Billion in Fraud
Visa leads the charge in AI-powered fraud detection and prevention, saving an estimated $25 billion in losses each year through advanced machine learning algorithms. The company blocked $40 billion in fraudulent activity during 2023, which doubled from the previous year. Their AI system stopped 85% more suspected fraud on Cyber Monday 2023 compared to 2022, even as fraud attempts jumped by 200%.
The company has upgraded its Visa Account Attack Intelligence (VAAI) with a new VAAI Score. This system uses generative AI to identify and score enumeration attacks as they happen. More than 500 different attributes are analyzed for each transaction, with risk scores helping to spot potential fraud. Visa’s network handles about 300 billion transactions yearly, and AI examines each one for possible threats, demonstrating the power of AI in maintaining financial stability and enhancing cybersecurity.
Smart Receipt Categorization and Expense Management
AI has transformed how businesses handle expenses through smart receipt processing and sorting. Modern AI systems can now pull information from receipts automatically. They match data to purchase orders and process payments, which cuts down on manual entry mistakes. These systems use OCR technology to scan receipts and invoices, extracting key details like merchant names, dates, totals, and taxes without human intervention.
Machine learning algorithms have grown better at sorting expenses automatically and now interpret data more accurately. These systems analyze receipt contents and categorize expenses independently, providing businesses with up-to-the-minute tracking of their finances. Companies that use AI-powered expense management save up to 10 hours each month by automating these tasks, showcasing how AI can enhance operational efficiency in finance.
Cross-Border Payment Optimization Using Machine Learning
Cross-border payments used to be inefficient, but AI has brought major improvements. Smart algorithms now find the best payment routes by studying past transaction data to discover budget-friendly and efficient channels. They look at currency exchange rates, fees, regulations, and network traffic to cut costs and speed up processing.
AI systems continuously monitor currency markets and use predictive analytics to identify the best times for transactions based on market patterns and exchange rates. These systems also detect fraud in cross-border payments by spotting unusual patterns faster than ever before, enhancing both efficiency and security.
AI has revolutionized how international payments handle compliance by automating Know Your Customer (KYC) and anti-money laundering (AML) checks that once took too much time and had too many errors. Natural language processing and machine learning now check documents, spot fakes, and verify identities faster and more accurately than human teams, demonstrating how AI can improve regulatory compliance in financial services.
Wealth Building: AI Use Cases in Investment Services
AI tools have transformed investment services by managing wealth and making savings work harder without human input. Both fintech companies and traditional banks now provide smart AI solutions that make investment strategies available to everyone, showcasing the democratization of finance through AI.
Wealthfront’s Automated Portfolio Management
Wealthfront leads the way in AI-driven investment management. Their algorithms build tailored portfolios based on each person’s risk assessment. Users need just $500 to start investing with a simple 0.25% yearly fee. The AI system takes care of all trading and keeps investments balanced for the optimal asset mix.
The platform’s standout feature is Tax-Loss Harvesting. This smart system identifies opportunities to reduce tax bills by selling investments at a loss to offset gains. Most clients save at least 6 times more than what they pay in fees. The platform also lets users pick from hundreds of funds in dozens of areas like social responsibility, clean energy, tech, and cryptocurrency. This variety helps users build portfolios that match their values, demonstrating how AI can personalize investment strategies.
Goldman Sachs’ Marcus: AI-Powered Savings Optimization
Goldman Sachs has entered the AI arena with their digital bank, Marcus. The platform creates AI-powered money management tools that leverage the bank’s 151-year-old financial expertise. Adam Dell, partner and head of product at Marcus, paints an exciting picture: “Imagine if there was an AI-powered digital assistant that had all the financial expertise of Goldman Sachs in their brain, and it was, in real time, helping you think about how to optimize your savings account, your checking account, manage your expenses, manage your income, manage your investments”.
Marcus distinguishes itself from competitors by operating on net interest margin instead of fees, positioning it as a better deal for customers. After seeing record deposits from customers in 2020, Marcus continues to enhance its AI capabilities. This becomes increasingly important as interest rates fluctuate and more digital banks enter the market, showcasing how traditional financial institutions are adapting to the AI revolution in finance.
Customer Retention and Relationship Management
Customer retention costs five times less than acquiring new customers, making it a critical priority for financial institutions. Major banks now leverage AI to predict customer behavior and build personalized experiences, demonstrating the power of AI in enhancing customer relationships.
Predictive Churn Models at American Express
American Express transformed its retention strategy by developing advanced predictive models. These models analyze historical transactions and 115 variables that include demographics and spending patterns. Their Australian market model identifies 24% of accounts that might close within four months. The system examines 18 months of historical data to forecast potential departures instead of using static business rules like declining use. This radical shift from reactive to preventative retention has allowed Amex to expand this technology in Asia Pacific and eventually worldwide, showcasing how AI can improve customer retention in financial services.
Sentiment Analysis of Customer Communications
Banks increasingly use sentiment analysis to gain evidence-based insights from customer interactions. This AI technique examines support tickets, surveys, product reviews, and phone calls to identify negative and positive sentiment drivers. Banks can better understand customer expectations through these data points and create a sentiment score that informs improved decisions. Sentiment analysis also helps banks proactively address customer issues and strengthens their acquisition and retention efforts, demonstrating how AI can enhance customer relationship management in finance.
Personalized Loyalty Programs Driven by AI
Mastercard helps client firms create individualized offers based on a comprehensive view of transactional data. Their SVP of Offers and Loyalty states that “it’s not possible to have personalization without loyalty”. AI enables predictive engagement by analyzing billions of transactions daily across 80 million merchants. HSBC goes beyond simple rewards and uses AI to predict how customers might redeem credit card points, leading to more targeted marketing of its rewards offerings. This application of AI in finance showcases how personalization can drive customer loyalty and engagement.
Wells Fargo’s Proactive Customer Service Interventions
Wells Fargo selected Pega’s Customer Decision Hub as the foundation of its AI-driven engagement strategy. The system automatically gathers customer data from multiple sources and creates comprehensive profiles in real time. The bank analyzed 4 billion digital interactions to determine the “next best conversation” for each customer, resulting in a 3-10x increase in engagement rates. Their virtual assistant Fargo handles over 20 million customer interactions yearly and assists with tasks ranging from bill payments to fraud reporting, demonstrating how AI can enhance customer service in banking.
Conclusion
Artificial intelligence in finance has evolved from a technological novelty to become a vital tool for financial institutions worldwide. Major players like Morgan Stanley, Visa, and Wealthfront demonstrate how AI streamlines processes in their core business operations, showcasing the transformative power of AI in the finance industry.
AI improves operations in five crucial areas. Smart customer acquisition now utilizes predictive analytics and automated onboarding. Evidence-based financial guidance comes through AI advisors and budgeting tools. Advanced security systems prevent billions in fraud each year. Professional investing becomes accessible through automated wealth management. Predictive modeling and tailored engagement help retain customers effectively.
The advantages of adopting AI solutions for finance are clear. Visa blocks $25 billion in annual fraud while Morgan Stanley provides instant investment recommendations. These results show that AI isn’t just experimental technology anymore – it’s fundamental to modern financial services.
AI will continue to reshape how financial institutions serve customers, handle risk, and build value. The examples we discussed prove this change is happening now, and innovative solutions will emerge over the coming years. As financial technology continues to advance, we can expect to see even more sophisticated applications of AI in banking and finance, further enhancing financial stability, consumer protection, and overall efficiency in the sector.
FAQs
Q1. How is AI transforming customer acquisition in financial services? AI is revolutionizing customer acquisition through personalized marketing, predictive analytics, and streamlined onboarding processes. Financial institutions use AI-powered chatbots to guide customers through applications, while computer vision technology enables digital identity verification, allowing for faster and more secure customer onboarding.
Q2. What role does AI play in financial decision support? AI advisors provide personalized financial guidance by analyzing complex data. For example, Morgan Stanley’s Next Best Action platform offers instant investment recommendations, while AI-powered budgeting apps help individuals manage their finances more effectively. AI also enhances retirement planning tools and improves small business lending decisions, demonstrating various AI applications in finance.
Q3. How does AI improve transaction security in the financial sector? AI significantly enhances transaction security, particularly in fraud detection and prevention. Visa’s AI-powered system prevents an estimated $25 billion in fraud annually by analyzing hundreds of attributes for each transaction in real-time. AI also optimizes cross-border payments by identifying the most efficient and cost-effective routes while ensuring compliance with regulations, showcasing how AI contributes to financial stability and cybersecurity.
Q4. What are some examples of AI applications in wealth management? AI is transforming wealth management through automated portfolio management and savings optimization. Wealthfront uses AI algorithms to build personalized investment portfolios and automatically harvest tax losses. Goldman Sachs’ Marcus platform is developing AI-enabled money management tools to help customers optimize their savings and investments, demonstrating how AI is democratizing access to sophisticated financial services.
Q5. How are financial institutions using AI for customer retention? Banks leverage AI for customer retention through predictive churn models, sentiment analysis, and personalized loyalty programs. For instance, American Express uses AI to identify accounts likely to close within months, while Wells Fargo employs AI to determine the “next best conversation” for each customer, significantly increasing engagement rates. These examples showcase how AI enhances customer relationship management in the finance industry.
