Artificial intelligence (AI) is all the rage now. It’s impacting numerous industries globally and changing the way we do things. One of the critical industries AI is making strides in is the financial technology “fintech” industry.
AI now plays a significant role in facilitating financial services, replacing what required manual work a few years ago. For example, banks now apply AI to assess credit risks with high accuracy. They also use it to detect fraud in massive financial networks with success.
AI in fintech is here to stay. It has already made a big dent and is simultaneously proliferating. According to P&S Intelligence, AI in the fintech market is expected to grow to $47 billion in 2030 from $7.7 billion in 2020.
What is artificial intelligence?
Artificial intelligence refers to intelligence demonstrated by machines instead of the natural intelligence displayed by humans. In the modern sense, it’s a broad subfield of computer science concerned with building machines exhibiting intelligence similar to that of humans.
Artificial intelligence has four common goals for machines;
- Systems that think humanly;
- Systems that act humanely;
- Systems that think rationally;
- Systems that act rationally.
Humanity hasn’t yet achieved the ultimate goals of artificial intelligence, even though we’ve seen remarkable progress in the area.
How do fintech companies apply artificial intelligence?
Modern fintech and established financial services companies apply artificial intelligence in many ways in their businesses. They include;
- Credit risk assessment
Credit risk assessment entails estimating the probability of a prospective borrower failing to repay a loan. It’s critical to financial institutions such as banks and credit unions that earn revenue from lending money with interest. Every time a financial institution lends money, it bears the risk of the borrower being unable to pay it back. Hence, banks go through the pain of assessing every prospective borrower’s creditworthiness. They consider numerous factors such as outstanding debt, payment history, credit history length, and credit card utilization.
Conventionally, creditworthiness is approved manually by humans. But, no matter how clever a human is, they’re bound to make mistakes when assessing hundreds of thousands of customers daily. On the other hand, artificial intelligence can perform the task without getting tired.
Computer programs can automatically analyze multiple factors affecting a customer’s credit and generate a score immediately. Then, a human makes the final decision. The AI takes a lot off the human’s plate with its analysis. The human operator has to double-check only when the AI detects red flags in a borrower’s credit history.
Many new-age startups have built big businesses selling AI-powered credit checking software to financial institutions, e.g., Upstart, which handles consumer loans.
- Fraud Detection
Fraud is a severe problem for every financial services company. Banks, payment and trading apps, middleman lenders, and the likes deal with unprecedented fraud daily. According to fraud detection firm Feedzai, banking fraud attempts soared 159% from the final three months of 2020 to the first quarter of 2021, with the majority performed online. The US Federal Trade Commission said consumers lost $5.8 billion to bank fraud in 2021, up 70% from 2020.
With bank fraud already substantial and still on the rise, financial institutions always seek new ways to tackle it. Artificial intelligence presents a sophisticated way to detect and prevent fraud in financial networks. You can use AI to analyze vast numbers of transactions to identify fraud trends. If the AI model detects any, it can flag them for further investigation or automatically halt them.
Computer programmers can apply machine learning (ML) techniques to detect unusual transactions in a bank’s network. You can train a machine learning model for fraud detection by feeding it with many transactions and assigning them to “fraud” or “non-fraud” categories. The model learns from this approach to flag transactions that resemble “fraud.” This method isn’t perfect because criminals can be very clever. Hence, you must constantly train the machine learning model to detect new types of fraud.
- Customer Support
Customer support is essential to every financial firm. Unhappy customers tend to take their money to other firms, so most financial institutions work to offer excellent customer support. Customer support typically requires an army of staff to handle and is often hectic and expensive. You should notice that many companies outsource their customer support to other countries with lower wages to cut costs. AI can help with customer support alongside human operators.
The most common use of AI in customer service is chatbots. Chatbots are software apps used to conduct online chat conversations automatically. It’s programmed to send automated responses to specific customer inquiries instead of direct contact with a customer support agent.
Chatbots simulate human-like conversations, so enterprises use them for customer support. Financial institutions can program their chatbots from scratch, but this isn’t easy. The easier way is to adopt a ready-made chatbot platform and customize it to their specific needs.
Financial institutions can also use AI to assist human support agents. They can apply artificial intelligence to customer inquiries to automatically interpret what they’re asking and display the relevant materials. Doing so saves time for the agent and customer and reduces cost.
This use case is related to fraud detection but not the same thing. It involves securing the bank’s back-end platforms rather than the user-facing apps.
Artificial intelligence is bolstering cybersecurity for financial institutions and other enterprises in many ways. A unique way is automating many cybersecurity tasks that a human analyst would usually perform. These include scanning code repositories, servers, workstations, and other hardware for known vulnerabilities.
AI can consume billions of billions of data artifacts within a computer’s network. It then analyzes these artifacts to detect atypical behavior. AI also helps battle bots within a network, differentiating the good bots (like web crawlers) from bad bots and stopping the latter. Cyberattacks are growing in complexity and volume, so enterprises can’t use the cybersecurity tools of old to tackle new threats. Hence, the application of AI in the cybersecurity field has been welcomed with open arms.
Many new startups specializing in applying artificial intelligence to cybersecurity processes have propped up and built solid businesses.
According to MarketsandMarkets, AI in the cybersecurity market is projected to grow from $8.8 billion in 2019 to $38 billion in 2026.
Artificial intelligence has made its way into trading and investments in the financial sector. Fintech firms now use AI to help identify good investments and trading opportunities. A typical example of this use case is automated investment apps, also known as “Robo advisors.” Robo-advisors use AI to analyze millions of data points concerning stocks, bonds, commodities, or other investment assets. The analysis helps them execute trades at the most optimal prices.
Likewise, AI trading bots have become ubiquitous. Self-directed trading is hard (the majority of day traders lose money), so people often opt for algorithmic trading bots powered by artificial intelligence. Trading bots are computer programs that trade based on specified conditions, e.g., when a stock price falls below a certain level. According to Mordor Intelligence, the algorithmic trading sector is expected to grow at a compound annual growth rate (CAGR) of 10.5% from 2022 to 2027. There won’t be such significant growth if investors weren’t seeing profits from the sector.
There are a few dedicated marketplaces for buying automated trading strategies, e.g., the MQL5 Marketplace. These marketplaces enable traders to buy and use trading bots developed by experienced programmers of MQL5.community. The community responds to traders’ requests for custom development. If you have no programming skills, you can submit a task to the MQL5 Freelance Exchange and get a response from one of the 1,200 professional developers familiar with algorithmic trading.
Developers also use AI to backtest their trading strategies. Backtesting refers to testing trading models based on historical data. MetaTrader 5 Trading Strategy Tester is an excellent example of a platform that allows investors to backtest AI-based investment strategies.
Hedge funds, private equity funds, mutual funds, or other investment firms need research to make sound decisions. A lot of time and effort is dedicated to research in investment firms. But, manual analysis is difficult and stressful. Hence, such firms adopt artificial intelligence to help them analyze data to identify promising investments. For example, AI can derive the frequency of shopping at retail stores by analyzing traffic footage. With this information, funds can know which retail chain had good quarters and invest in them ahead of their official earnings statements.
Insurance entails protection against risks no matter how low the probability of them occurring. Individuals and enterprises buy insurance policies, and the regulated firms selling these policies consider risk profiles to determine their prices. Now, insurance providers are using artificial intelligence to assess risk levels.
The auto insurance sector is the most typical use case for AI in insurance. Auto insurers can collate and process real-time data from in-car sensors to assess a vehicle’s condition and accident risk, thanks to artificial intelligence. They can also monitor a driver’s behavior and performance to determine accident risk. They can price their services better with this information at their fingertips, charging “good” drivers lesser premiums than “bad” ones.
Insurance providers also use AI to automate claims collection. AI bots can walk a customer through the process of filing and collecting claims in a conversational tone.
We barely touched the tip of the iceberg. Artificial intelligence is applied in many more ways in the fintech sector. Undoubtedly, AI and machine learning are here to stay, and any financial services firms not adopting them are doing themselves a disservice.