AI has become a dominant technology in a number of industries, and finance is one of them.
According to estimates, North American banks could save up to $70 billion by 2025 if they implement AI and automation. This number could reach $140 billion across the entire financial service sector, including banking, insurance, and capital markets, within the same period.
Fintech has tremendous potential to disrupt the way financial services institutions operate and shake this traditional industry to its core. Furthermore, this will result in higher productivity and a gamut of new services that users will have at their disposal.
1. Fraud Detection
Preventing fraud is one of the most important goals of every business, and in the financial sector, it is of utmost priority. Given that criminals continually target FIs and insurance companies are, these businesses can’t rely on manual reviews of potentially fraudulent transactions.
Artificial intelligence is capable of both preventing and predicting fraud before it happens. New technologies improve the security of frictionless payments, whose vulnerabilities are hard to detect even with predictive models.
That is why banks and other financial institutions implement AI and machine learning to spot suspicious transactions. For example, such a system will notice if the same card is used in different countries within a short time or if there’s an attempt to withdraw an unusually high amount of money from an individual account and alert the cardholder accordingly.
2. Automated Claims Processing
The insurance claims process is long and tedious, both for customers and insurance agents.
With the help of chatbots, this process can be facilitated and streamlined. AI-powered virtual assistants can collect all applicable information through uploaded documents or images of damaged properties and offer customers further assistance by scheduling repairs.
Apart from improving customer experience and satisfaction, insurance chatbots can take over a great deal of low-value tasks from insurance reps, and allow them to focus on more complex ones. Similarly, these smart algorithms process vast volumes of data, eliminate human error, and streamline the interaction with customers through 24-hour availability.
3. Risk Assessment
As AI heavily relies on learning from previous experiences using historical data, it’s logical that a sector based on bookkeeping and records should tap into this technology. On the other hand, banks must confirm that they offer loans only to reliable customers, which is why risk assessment is of vital importance.
Thanks to artificial intelligence and machine learning, accurate client profiling is possible, as these technologies enable running Know Your Customer checks in real-time. This way, clients can be easily identified and assessed. With such a procedure, FIs can quickly analyze every potential client’s loan repayment habits; identifying risks, deciding whether to accept their credit card eligibility, and customizing interest rates.
These practices require the processing and interpretation of massive amounts of data, which would be virtually impossible without big data analytics and machine learning.
4. Managing Personal Finances
A U.S. research study found that between 2009 and 2018, there was an 8% slip in financial literacy – the number of people who could answer four basic questions about interest rates, inflation, financial risk, and mortgage rates dropped from 42 percent to 34 percent.
These numbers show that a lot of people could benefit from having a financial advisor, but the cost is often prohibitive.
Conversational AI offers an accessible solution to the problem. Digital financial advisors and coaches are the future of AI in banking, and there are different examples of how they are already disrupting this industry.
By implementing robo-advisors, banks can engage their customers and provide them with a seamless experience. The primary purpose of these intelligent algorithms is to offer financial planning services, as well as saving and spending tips.
5. Algorithmic Trading
Investing and trading rely heavily on market predictions and forecasts.
With its powerful capability to store and analyze historical trading data, as well as identify patterns in it, AI is a must-have tool in every investor’s arsenal.
The world is still reeling from the last global financial crisis, and AI paired with ML could help prevent another similar disaster. Namely, besides their ability to spot patterns in historical trading market data, machine learning algorithms can also be trained to analyze this data and detect triggers that precede these anomalies.
Investors can use ML algorithms to help them decide when to buy and sell stocks based on their approach to risk-taking.
Although AI is still in its relative infancy, it has already revolutionized the financial industry. It has boosted employees’ productivity, accelerated numerous processes, and improved customer experience. And as it grows, use cases will become even more sophisticated.
Jennifer Wilson is a writer at Qeedle.com She knows business processes and operations management inside out. As she understands all the challenges of running a small business firsthand, it’s her mission to tackle the topics that are most relevant to entrepreneurs and offer viable solutions.