Five Use Cases for AI in Finance

Application of Artificial Intelligence in Finance

Artificial intelligence has become a real game changer in the world of finance. An AI system can examine millions and billions of data points, and find patterns and trends that people may miss, and even predict future patterns. Artificial Intelligence, along with natural language processing, can even be used to create conversational trees that let customers converse and perform specific actions, whether by chat or voice application. Here are five use cases for AI in financial applications. artificial intelligence in finance

1. Process Automation

With AI technology, now it's possible to automate processes to manage tasks like understanding new rules and regulations or creating personalized financial reports for individuals. Processes that previously took hours, sometimes days, can now happen almost instantaneously. For example, IBM's Watson can understand complex regulations, such as additional reporting requirements of the Markets in Financial Instruments Directive and the Home Mortgage Disclosure Act. Rather than asking financial professionals to research answers to questions, which can take hours and days, Watson can find the answer in mere moments. Similarly, wealth managers can use AI to generate more in-depth status reports for their clients quicker, which allows them to provide individualized advice to more clients. Not only that, they can do it faster and present the information in a way that's easier to understand. AI also allows bankers to make loan decisions in seconds, not months. By using artificial intelligence, bankers can assess risks and spending patterns, and even look at alternative sources of data, such as payment history of rent and utilities. By automating the decision making process, bankers can reduce their risk of default loans, as well as improve customer experience by reducing the number of abandoned applications from frustrated borrowers who are tired of the long process.

2. Financial Management

Financial Health Behavior

Just ten short years ago, if you wanted to check your bank balance, you had to log onto your computer, visit your bank's website, and look for yourself. If you wanted to know the state of your household budget, you had to look at the spreadsheet you created for yourself. Now with mobile banking apps and web portals, financial service AI — specifically Envestnet | Yodlee’s® AI Fincheck — can analyze consumers’ individual account data to see what they have, how they’re performing financially, make recommendations on future actions based on the results, and then help with automation for savings and budgeting for better financial health and behavior.

Answer Real Time Questions

In the finance industry, AI can be used to examine cash accounts, credit accounts, and investment accounts to look at a person’s overall financial health, keeping up with real-time changes and then creating customized advice based on new incoming data. Envestnet Intelligence, advanced analytics for financial institutions, enables financial institutions to easily get answers in real-time to key business questions across desktop, mobile, and Amazon Alexa-enabled devices. Providing interactive, predictive, and conversational capabilities, Envestnet Intelligence extracts information from comprehensive financial data sets to ensure financial institutions have an easy way to answer crucial questions anywhere, anytime, on any device.

ai in finance

Increased Usage of Chatbots

Chatbots in banking are not only a money-saving tool, they can automate simple tasks such as opening a new account or transferring money between accounts. Companies that want to use them only need to install them on their existing websites rather than create a separate chatbot app. And they're always on, so even a customer who visits your website at 3:00 AM can get answers to their questions and assistance with their problems. Programming a chatbot means starting with specific tasks it can perform, such as paying a bill or processing an account application. But as it grows, it will begin to learn the different language and terms people use to describe the same process. Similarly, as more and more financial institutions develop voice applications, the chatbots will need to recognize vocal pitches, inflections, pronunciations, and accents.

3. Fraud Detection

With the rise in eCommerce and online shopping, fraudulent activity has increased. Transaction Data Enrichment (TDE) uses machine learning and artificial intelligence to decipher unintelligible strings of characters that represent transactions and merchants and converts them to readable text that shows each merchant's name and lists their address and city. It shows the local merchant's location, rather than the central corporate office. This method of turning hard-to-understand data into easy-to-read information, helps both banks and customers to understand where they spent their money and with whom.

Artificial intelligence It reduces both customer service calls and fraud research costs, because the customers can tell what they bought and where they bought it. Fraud detection solutions reduce the number of people calling about mystery charges on their credit card bill, because, now, customers understand what those charges mean. Fewer calls means less fraud research, which reduces costs. Most importantly, these clear descriptions help developers put financial data into context so they can more easily categorize and analyze purchases. This helps with things like budgeting, analyzing spending habits, credit scoring and being able to predict future earning and spending issues.

4. Personalized Banking

When it comes to financial advice, many consumers want some help when it comes to personal finance advice. A recent study by Aite Group showed that 79% of 22 – 34 year olds, 77% of 35 – 49 year olds, and 62% of 50+ year olds were moderately-to-extremely interested in using a digital financial wellness coach. But they don't just want abstract lessons about finance. Consumers want to be warned and reminded of important information about their own financial data, not told about issues after the fact.

They want to be advised when they should and shouldn't make purchases, not be sent an alert when they've accidentally overdrawn their accounts. Companies are using AI driven financial forecasting tools to tell users when they can actually spend money, based on their income, bank balances, upcoming obligations. By using a combination of AI, machine learning, predictive analytics, and even user feedback to predict future outcomes. It helps users make smart decisions based on their financial picture at the moment. This way, consumers who ask about a purchase — "Can I buy this today?" — can get a yes or no answer that will help them avoid problems like overdrafts, late fees, and end-of-the-month shortfalls. Consumers increasingly expect how they interact with their Financial Service Providers (FSPs) to mirror the customized, digital experiences they get with technology giants like Amazon and Netflix.

Since two-thirds of Americans now claim online and mobile as their primary banking channels, where competition was once about account features, interest rates and lending, winners and losers going forward will be determined by the data-driven, hyper-personalized digital banking experiences FSPs can offer.

5. Trading & Investments

Information from big data sources is often unstructured and unreadable to humans without the help of AI FinTech focused technologies. Using natural language processing, these technologies are capable of sifting through complex datasets and extracting meaningful insights from data in a matter of seconds. This technology helps find otherwise hidden business insights and automates the reduction of risk for investment and business decisions. Big data and investments go hand in hand in improving investors’ forecasts. By leveraging big data and AI focused FinTech, asset managers can make more data driven decisions and reduce human error or bias.