Artificial intelligence (AI) has proven useful to financial services institutions in multiple ways. From detecting potentially fraudulent charges to automating complex credit and loan processes, AI-powered fintech has proven invaluable when it comes to internally engineering value for financial services institutions.

AI use cases in fintech

There is a wide range of AI-powered applications that drive both internal and external improvements within financial services institutions. 

Predictive analytics 

Within the analytics sphere there are thousands of applications and use cases. Banks rely on AI solutions to reduce investment risk, build models based on historical data and trends, and leverage those models to improve their business. Predictive analytics are valuable in many realms, from investment analysis to operational risk management software. These solutions provide insight into how certain decisions might affect the business in the future. 

Good, clean data 

Machine learning software and artificial intelligence tools it powers are dependent on one thing—good data, and lots of it. If data is poor, the predictions and the models based on that data are useless. AI is built on data, no matter what type of machine learning algorithm (supervised, unsupervised, or reinforcement learning) it uses. All modern AI uses limited memory AI; this means the “memory” or record of past interactions builds on the current data, which future interactions will be dependent on. In other words, the data you feed your application will affect its performance down the line. 

Credit decisioning 

Making lending decisions used to be an arduous process that involved multiple parties and an in-depth study of the applying individual’s credentials. Now, teams use an AI-powered loan origination software to make lending decisions based on a variety of data points that automatically run through the tool. If someone has a credit score below a certain amount, or fails to meet other criteria given to the bot, they are automatically rejected. While high dollar decisions still require a human’s input, this technology significantly reduces the amount of hours spent researching and analyzing many of these decisions. Improvements in this technology will expedite this process even more in the future. 

Fraud detection 

AI applications help power the first line of defense for fraud detection software, stringing together data from disparate sources helps create models that rate the trustworthiness of a particular transaction. These applications process massive amounts of data at scale and in real time to flag potentially fraudulent transactions. A key point to make here is that a quality fraud detection program will limit the number of false positives. Obviously, you don’t want to make the program let potential fraudulent activity go unflagged, but false positives are a huge waste of resources and limiting those is key to having an effective and efficient fraud detection program. 

Risk management 

Financial services institutions can leverage AI to carry out risk management analysis at a variety of levels. Risk management is interwoven in the fabric of many different departments within a financial services firm. As there is so much scrutiny on financial services institutions, and the cost of underperforming or failing clients is significant, risk management is necessary to run a functioning business. AI tools can help automate and shape the risk management process. They are able to extract salient points from massive amounts of data and deliver them to a human decision maker. Once the AI learns enough, it could even take action on the data that it collects and analyzes. 

Customer service chatbots

One of the most interactive and human-centric uses of AI in fintech is its use in customer-facing chatbots. Finservs have increasingly focused on providing a fantastic customer experience from prospective customer to onboarded customer. Chatbots are vital for banks to provide a great experience without sinking too many of their resources. The AI that powers these solutions can help route customers and execute complicated functions that might have required a human in the past. Most chatbots utilize emotionally intelligent design to give users a more human-like experience. Every interaction helps improve the design and the logic the bot uses to carry out its functions. Increasingly, banks are using voice assistants in fintech applications to enable customers to conduct banking activities. 

Trading

Investors use AI to fuel their investment process. This includes AI-powered tools that scour the web for relevant pieces of information based on investor input and predictive analytics tools that model scenarios based on historical data. There are a variety of tools designed to streamline the trading process and fuel better investment choices. 

The democratization of investment product access

The opportunity to invest money has historically been an opportunity limited to socio-economic elites. Broker fees and other complexities were prohibitive barriers for the lower classes. However, now many consumer-facing apps with low or no trading fees open the markets to a larger share of people. Lower minimum investment amounts and the ability to easily facilitate high-volume trading for low net worth individuals make playing the market and investing a real possibility. There are many safe investment options out there including mutual funds, index funds, and ETFs (exchange traded funds) that reduce the amount of risk involved and generally perform better than actively managed funds when measured over long periods. You’re removing the ability to bet big and win big, nevertheless, these pooled investment products give higher returns than even the highest yielding savings accounts. More access to investment products means (hopefully) more upward social mobility for those that have been barred from the investment party in the past. 

The future of AI in fintech 

Financial services’ future is AI heavy. The industry increasingly relies on AI-powered tools to perform vital functions; that trend will become firmly entrenched in the coming months and years. While we’re years from a legitimate theory of mind AI, the scope of limited memory tools will expand to the point of making creative decisions nearly indistinguishable from those that a human made, only because they’ve learned all of the prior decisions that humans in the same role made.