Artificial intelligence in finance is quickly becoming one of the most innovative—and indispensable—ways major industry players are enhancing their service offerings and reaching new customers.
Business leaders and entrepreneurs alike are already looking for ways to use AI and machine learning in financial services, quickly adopting this still-new technology. And they're not expected to slow their innovations anytime soon. In fact, an Intelligence Unit study found that 86% of financial services executives plan to increase their AI-related investments through 2025.
The adoption of AI in the financial industry
Forbes reports that approximately 54% of larger financial service organizations, those with over 5,000 employees, are integrating AI for finance to refine their operational processes. Here, we explore five key ways artificial intelligence finance applications are revolutionizing the sector:
1. Amplifying predictive analytics
By leveraging AI for financial services, companies can now predict the behavior of millions of customers in seconds. These AI solutions for finance companies mean faster data processing, better predictive models, and invaluable insights in a fraction of the time.
2. Slashing operational costs
Another way companies are using artificial intelligence in financial services is by integrating AI into the processes that govern key financial products, like insurance, banking, and asset management. Tedious tasks that used to be done by humans or slower system processes can now be completed almost instantaneously—eliminating human error and slashing operational costs associated with data entry and similar tasks by up to 70%.
3. Boosting revenue growth
The implementation of AI in the financial industry is integral to maintaining competitive edges. A significant 34% of companies acknowledge AI’s potential to elevate annual revenue by at least 20%, suggesting that AI in banking and financial services is a growth game-changer.
4. Elevating customer engagement and satisfaction
FinTech firms are already employing AI in banking and finance services to improve customer satisfaction, retention, and engagement. One way we're seeing this use of AI in finance is through AI-driven chatbots, designed for frictionless, 24/7 customer interactions. AI-based virtual assistants can further help these companies to better understand their customers’ needs, and in turn, increase customer engagement.
5. Strengthening cybersecurity
AI in financial services is instrumental in fortifying online banking fraud detection and prevention, meeting stringent regulatory requirements and safeguarding transaction security.
Diverse applications of artificial intelligence in banking and finance
The broad spectrum of business advantages offered by artificial intelligence financial services has accelerated its widespread adoption. Below, we spotlight the most innovative applications of AI in banking and finance:
Data-driven investments — aka algorithmic, quantitative, or high-frequency trading — have grown rapidly across the world’s stock markets. Intelligent trading systems use artificial intelligence for financial services to make precise predictions based on historical and real-time data. AI-powered trading systems can analyze huge, complex data sets, enabling quick decision-making and transactions, thus increasing opportunities for profit.
Example: Infinite Alpha uses AI for financial services to facilitate a cryptocurrency trading platform specifically designed for professional investors. It secures traders' data and transactions via advanced authentication and encryption. The trading platform provides users with an intuitive dashboard interface to easily access their account details, balances, and transaction histories.
Fraud detection and prevention
Artificial intelligence in financial services is adept at real-time anomaly detection in customer behaviors and transactions, fortifying fraud defense mechanisms.
AI enables financial services firms to analyze and detect irregularities in customers’ behaviors, locations, and spending habits in real time. It can recognize suspicious or anomalous activity and trigger a security mechanism to reveal and prevent fraud.
Examples: BNY Mellon, one of the largest payments service providers, has designed an AI-based fraud detection framework to defend its third-party data. The bank deployed artificial intelligence and finance-based machine learning models trained on over 100 million data samples to identify patterns of fraudulent transactions that improved fraud detection accuracy by 20%. Another example is American Express, which uses AI-based algorithms to monitor every transaction on its platform in real time to detect fraud and render decisions in milliseconds.
Another beneficial use of AI in financial services is leveraging artificial intelligence to trim operational costs, increase productivity, and boost operational efficiency by setting up process automations. AI can help such organizations automate repetitive, time-consuming tasks and eliminate human biases and errors. AI-enabled applications can also help firms verify data, generate reports, and review lengthy documents.
For instance, AI-enabled intelligent character recognition software can automate distinct mundane tasks such as entering customer data from contracts, forms, and other sources and extracting information from documents like loan applications and agreements.
Examples: Royal Bank of Canada has designed a private AI-based cloud to run simulations, train AI models, and analyze large databases to deliver smarter and more secure services. The cloud is helping the bank offer a smoother and faster customer journey, improving retention and customer experiences.
Another use of AI in the finance industry is occuring at JP Morgan Chase, where the company leverages AI-based process automation to perform otherwise time-consuming tasks such as data extraction, know your customer updates, and capturing documents.
Financial services firms leverage AI-enabled solutions to offer personalized products and services to customers, such as banking, lending, and payments. They also use AI-based chatbots powered by natural language processing to offer 24/7 financial guidance to customers.
Some also use voice-controlled virtual assistants to provide better customer service. AI-based applications employ algorithms that can track customers' regular expenses, income, and purchasing habits to provide personalized financial advice based on the user’s financial goals.
Examples: Bank of America uses an AI-based mobile banking app to provide clients with reminders to pay bills, enables customers to plan expenses, and lets them interact with the bank in an easier and more streamlined way.
Fintech Square uses conversational AI to empower its virtual assistant, which the company says can understand and answer 75% of customers’ queries.
Insurance claim processing
AI-enabled applications are transforming the insurance industry by improving the accuracy and efficiency of claims. The insurance claim management process frequently now employs a fully data-driven approach wherein AI analyzes all required documents to process and automate insurance claims. Using AI to automate claims processing also helps insurers to identify fraudulent claims and offer digital services to improve customer experience.
Examples: Cape Analytics uses AI to convert geospatial data into actionable insights to enable insurers and other property stakeholders to access valuable property characteristics at the time of underwriting. It also provides data about vegetation density and roof material that homeowners can use to protect their property, along with a calculated risk that insurers can use to write better policies.
NerdWallet is a finance company that uses AI to recommend the best financial products to meet the needs of their customers, such as credit cards, mortgages, and insurance.
Credit risk evaluation
An AI-based platform provides lending companies with a quick and accurate assessment of a potential borrower by considering several factors, such as their credit score and history. In other words, lenders can make better-informed, data-backed decisions to identify credit-worthy applicants and accelerate the overall process of credit risk evaluation.
Example: Fintech document automation software firm Ocrolus helps lenders automate credit decisions. The company enables financial services organizations to make the right lending decisions by providing accurate data and analyses.
Artificial intelligence and finance go hand in hand
The impact of AI in finance and banking can't be understated. Not only are artificial intelligence financial services faster, cheaper, and more accurate, the more AI is used in the financial services sector, the harder it is to get away with fraud. In this way, artificial intelligence for financial services is one of the most innovative—and disruptive—market shifts the industry has ever seen.