AI is no longer something that can only be seen in sci-fi movies and books. It has become an integral part of many fast-paced businesses.

Industries from across the board are finding ways to utilize artificial intelligence (AI), and finance is no different. From increased efficiency to reduced human biases, the use of AI in financial services has many benefits.

Credit Decisions

AI can assess potential borrowers faster and more accurately than humans. And, it can do it at less cost. To make data-backed, better-informed decisions, it accounts for a wider variety of factors.

Compared to traditional credit scoring systems, AI tools that provide credit scoring operate on more sophisticated and more complex rules. Lenders that use AI have an easier time distinguishing between credit-worthy applicants that lack an extensive credit history and high default risk applicants.

Unlike humans, AI-powered tools are not likely to fall victim to biases. Loan-issuing apps and digital banks use smartphone data (or other alternative data) and machine learning algorithms to provide personalized options to customers and evaluate loan eligibility.

Risk Management

When it comes to risk management in financial services, it is hard to overestimate the impact of AI. AI can handle vast amounts of data in a short time, thanks to its enormous processing power.

It can manage both unstructured and structured data by using cognitive computing. AI can detect early signs of potential future issues by analyzing the history of risk cases.

When carried out by humans, this task takes far too much time. AI can provide detailed forecasts and accurate predictions based on multiple variables. These reports are vital to business planning.

Banking Bots

Many financial services companies have started using chatbots powered by conversational AI to enhance the customer experience and generate better results.

Onboarding bots can significantly simplify the journey from applying for a bank loan or credit card to getting approved. The steps in between are often fraught with frustration and friction, so it’s not uncommon for customers to drop the whole thing and end the web session.

Chatbots can guide customers through the process efficiently. Thanks to such bots, customers can get approved for a loan without having to step through a complex web-based process, wait for a contact center agent, or visit a bank branch.

Artificial intelligence and NLP (natural language processing) enable chatbots to guide customers through all the tasks they need to complete to get their approval. Moreover, they can accomplish this in a single conversation. By using chatbots, bankscan get customers onboard more quickly and easily.

Fraud Prevention

AI has been helping financial institutions battle fraud for a while now. As machine learning is catching up to hackers, the future is looking brighter every year.

Due to the increase of online transactions and e-commerce, credit card fraud has been growing exponentially. AI fraud detection systems are especially effective at preventing this type of criminal activity.

They can analyze credit card users’ behavior, buying habits, and location. If something seems off, they trigger a security mechanism.

Such AI systems can also help banks reveal and prevent money laundering. Machines can help financial institutions reduce the costs of investigating possible money laundering schemes by recognizing suspicious activity early on. As seen in Aysadi’s case study, one bank lowered its investigative volume by more than 20% by using its anti-money laundering AI solution. 

Trading

Over the last few years, data-driven investments have been on the rise. This is also known as quantitative or algorithmic trading, and there is a good reason why it has been expanding rapidly across global markets.

AI trading systems can monitor and process unstructured (news, blogs, social media, etc.) and structured (spreadsheets, databases, etc.) data much faster than humans. These algorithms can test trading systems based on past data and deliver more accurate predictions for stock performance. This significantly improves the validation process.

Depending on an investor’s long-term and short term goals, an artificial intelligence trading system can put together recommendations for the strongest portfolio.

Recently, Bloomberg launched an AI-powered price-forecasting app for investors called the Alpaca Forecast AI Prediction Matrix. Using Bloomberg’s Market Data Feed (B-PIPE), the app can provide short-term market price forecasts for US 10-year treasury bonds, CME Nikkei 225 Future index, AUD/JPY, EUR/USD, USD/JPY, and other major markets.

Process Automation

To boost productivity and cut operational costs, many industry leaders rely on RPA (robotic process automation). Thanks to RPA, businesses can automate many repetitive, time-consuming tasks. Financial institutions are using AI-enabled software to extract information from forms (agreements, applications, etc), review documents, and verify data.

On top of allowing employees to refocus their efforts on tasks that require human involvement, RPA eliminates room for human error. JP Morgan Chase uses RPA to enhance the cash management process, capture documents, comply with Know Your Customer regulations, and extract data.