Artificial Intelligence is so much more than ‘Hey Siri, what’s the weather today?’, and financial institutions are perfectly aware of that. The application of artificial intelligence in finance simplifies the often convoluted processes, improving our interactions with money and financial transactions.
In this article, we’ll examine the role of artificial intelligence in banking and finance and how it can reshape the way we make financial decisions.
Traditional finance relied heavily on brokers and analysts. But humans actually aren’t our best bet when it comes to making financial decisions. The Great Recession demonstrated that the whole world could be thrown into an economic meltdown because of questionable financial decisions made by major stakeholders.
That’s where AI comes in. Machine learning and automation technology reduce human error, saving time and costs. Data from Finastra predicts that AI applications will save banks an estimated $447 billion by 2023. That’s enough to inspire financial institutions to take AI integration very seriously.
Another factor encouraging the adoption of AI in financial services is the rapidly changing customer demographic. Millennials and Gen Zers are quickly becoming the largest consumer group in the US, and they prefer online transactions and digital banking channels. Statista reported on the role of artificial intelligence in banking that 92.8% of millennials used mobile banking in 2018, compared to 49.8% of baby boomers.
Now, let’s take an in-depth look at the application of artificial intelligence in finance.
Successful financial decisions involve managing risk, but the first step is accurately identifying those risks.
Machine learning, a subset of artificial intelligence, excels at analyzing massive data sets more quickly than any human possibly could. The enhanced speed and efficiency of data analysis allow financial risk managers to quickly identify and address risks, minimizing losses.
AI also brings predictive analytics that uses existing data to forecast future outcomes. This allows businesses to plan more effectively to avoid risk and identify potential opportunities in time. Take the US leasing company Crest Financial. They employed artificial intelligence on the Amazon Web Services platform and immediately saw a considerable improvement in risk analysis.
Another way to use artificial intelligence in financial markets is to prevent fraud and cyberattacks. AI can quickly examine and identify inconsistencies in patterns that humans may overlook or misunderstand.
Digital financial transactions are increasing year over year as more users are sending and receiving money, paying bills, trading stocks, etc., using the web and mobile apps. Humans can’t monitor all of this activity and provide security effectively. But AI can.
Machine learning can examine how a user interacts with a banking app and flag any activity that isn’t consistent with the studied pattern. If there are suddenly more withdrawal requests from an account than normal, AI can request additional verification before approving transactions, which reduces the possibility of fraud. Even better, machine learning models improve as they acquire more data. So, if the system erroneously raises a red flag, it can learn from the experience and make better judgments moving forward.
Falling behind on the application of AI to cybersecurity can cost the finance industry dearly. According to Accenture, cyberattacks in the banking industry cost $18.3 million annually per company. IBM reports that the average total cost of a data breach is $3.92 million.
Increasing investment in AI could prevent these losses and save the time and cost of recovering from them. AI-enabled bots, for example, can spot suspicious login attempts and instantly alert both the customer and the company to possible security breaches.
Companies like ComplyAdvantage and Shift Technology attested to the benefits of using AI-based algorithms to monitor transactions. A Deloitte report cited ComplyAdvantage’s claims of an 84% reduction in false-positive alerts for anti-money laundering risk data, while Shift Technology’s use of AI to help insurers fight claims fraud.
Financial literacy is gaining popularity as more customers seek a better relationship with money. The assistance of professional financial advisors is expensive, but AI can deliver tailored suggestions for customers to improve their financial habits at a significantly lower cost.
A study by Accenture of about 33,000 banking customers found 54% want tools to help them monitor their budget and make real-time spending adjustments; 41% said they would be willing to use virtual banking advice.
Instead of human advisors, consumers can now interact with AI chatbots programmed to communicate like humans. Users can assess and manage their financial health using personalized reports of their purchasing history, income brackets, demographics, investment decisions, and more.
Using the wealth of available data, the AI technology can provide suggestions to improve financial planning and monitor customers’ progress toward their goals. And if some information is missing, AI can ask for it. For example, a chatbot can ask a customer questions about their five-year savings goal and deliver an automated budgeting and saving plan focused on achieving it. AI is even capable of sentiment analysis, in which a chatbot detects tension in a user’s tone of voice and suggests an appointment with a human consultant.
Firms like MoneyLion and USA’s largest banks like Wells Fargo and Citibank are already using AI to automate processes from customer identification to providing personalized financial reports.
Because of its ability to analyze large volumes of data faster and more efficiently than humans, artificial intelligence can be very useful for trading. Since AI-powered enterprise technology doesn’t have emotions, it’s also better suited for making accurate decisions based on market evaluations.
AI can resort to natural language processing to analyze keyword searches and track the movement of the market. This creates an algorithmic trading process that can automate trades, saving time and money.
AI can also identify patterns and trends in market price changes and use that data to predict potentially profitable stock options.
Alpaca, an AI offering from a Japanese tech company, combines deep learning and high-speed data storage to provide short and long-term trading forecasting options.
Alpaca’s technology tracks patterns in market price-changes and sorts its findings into dashboards for several markets.
Financial service companies can use the AI technology to create algorithms and machine learning processes to assess customer account activity and launch automation for repetitive transactions with the users’ approval.
For example, AI can determine how much a user can afford to save each month and automate that activity. It can also detect the amount required for paying rent and other household bills and automatically send payments to verified recipients.
The system can send periodic reports with spending charts and recommendations for improving spending habits to meet goals.
Startups like Tally use an AI-based system that aggregates all customer’s credit cards into one account and pays bills automatically through a consolidated credit line.
AI provides intuitive self-service so customers can access the information they need whenever and wherever they want it. Bank statements, beneficiaries, all account information can be instantly accessible without a human operator.
As virtual agents, AI chatbots can interact 24/7, addressing customer questions and issues as needed. They can also educate consumers, providing information via interactive videos.
Citibank’s mobile app is just one example of an app that provides full self-service and activity reports of all transactions.
77% of consumers prefer paying with debit or credit cards, while only 12% favor cash. Good credit is its own type of currency, determining the quality of jobs, houses, and loans available to customers. This makes the process of credit approval all the more important. Lending companies need an accurate method of assessing a customer’s suitability for borrowing so that they can maintain and expand their customer base.
Artificial intelligence technologies help banks and credit lenders make better underwriting decisions by using factors that more reliably evaluate borrowers in the decision-making process.
A Chinese insurance company Ping An uses AI to administer car insurance to their customers. Its smart auto claim solution allows users to assess and monitor their credit status, which is related to their driving behavior and history. A user’s credit can increase if they drive safely and adhere to traffic regulations, and reduce if they don’t stop for a red light, for example.
The application of artificial intelligence in finance industry is an efficient way to meet the demands of customers who want convenient, virtual ways to access their money. But the future of finance is a collaboration between humans and machines, combining efficient machine calculations with human perceptions to optimize solutions by allowing each participant to do what it does best.
If you decide to develop an AI-powered fintech application, Postindustria can help. Being a renowned developer of AI solutions and fintech software, we have experience creating innovative business solutions that keep you ahead of the curve, streamline your processes, and increase your revenue. Contact us today!
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