How many times have you asked a chatbot to open a savings account? How often has your bank called you to check your credit card activity? The world of AI is heating up, and no industry or sector hasn’t been affected by its impact and ubiquity. The financial and banking industry is a key player in harnessing this disruptive technology’s potential.

AI has made programs and processes more efficient, automated mundane tasks, enhanced customer service, and improved businesses’ bottom line.

While most banks are aware of the advantages of AI. It has become even more critical in the wake of the pandemic. Which has impacted the financial sector and encouraged more people to adopt digital experiences.

Forbes reports that 70% of financial companies use machine learning. To forecast cash flow events, improve credit scores, and identify fraud.

Continue reading to find out what some of the most common examples of AI in finance are. How financial institutions are embracing AI, ethical considerations, and the future of this fast-changing sector.

Common Examples of AI in Finance

Risk assessment

Is it possible to use AI to find out if someone is a borrower? Absolutely. According to Towards Data Science. “banks and apps use machine learning algorithms not only to determine a person’s loan eligibility but also to provide personalized options.” The silver lining is that AI is non-discriminatory. And can quickly and accurately determine the eligibility for a loan.

Risk management

Risk reduction is still one of the most important — and yet far-standing — issues in banking. Now, with machine learning, experts can use data to identify trends, mitigate risks. Save time and money, and provide better planning insights, according to Built In.

Fraud detection, management, and prevention

Have you ever had a call from your bank or credit card company after making multiple purchases? AI allows fraud detection systems to look at a user’s purchase history and send a warning. If something doesn’t seem normal or doesn’t match your typical spending habits, says Towards Data Science.

Credit decisions

According to Towards Data Science. AI can quickly and accurately evaluate a prospect based on many factors, including smartphone information.

Financial advisory services

Want to stay up to date on the latest financial news? Want to take a look at your portfolio? AI algorithms can analyze your portfolio. So you can get the data you need as fast as possible, Forbes reports.

Trading

Because AI is used to identify patterns within large datasets. It is not surprising that it is frequently used in trading. As In points out, AI-driven computers can process data much more quickly than humans can. Which speeds up the process and saves a lot of time.

Managing finances

Chatbots and VAs have eliminated the need to wait on the phone to speak to a human agent. Thanks to advances in technology and AI. Customers can now balance their accounts, schedule payments, view account activity, ask questions via a virtual assistant. And receive personalized banking recommendations at the convenience of their customers. As reported by Towards Data Science.

Preventing cyberattacks

Banks and financial institutions want to make sure their customers’ money and personal data are safe and secure. And AI can help with that. Human error is responsible for as much as 95% of cloud data breaches. 

AI can enhance your company’s security by analyzing and identifying common patterns. And trends in data and reporting anomalies or suspicious activity to your organization.

Better predict and assess loan risks

AI can, according to Forbes, look at a customer’s spending habits and behaviors. Which can be used to predict how they will borrow money. This is also relevant in parts of the world. Where people have access to mobile devices and other communication tools. But don’t have access to traditional credit.

Reducing the need for repetitive work

AI can take on mundane, repetitive, and time-consuming tasks. Like document review or extracting data from applications, freeing up employees to focus on other tasks.

Making smart underwriting decisions

AI technologies are enabling banks and lenders to. “make more informed lending and credit card underwriting decisions,” as per Built In. This is achieved by taking into account a range of elements. That provide a more precise representation of those who might otherwise be considered to be underrepresented.

Save money

Each of the above-mentioned items on this list can increase your bottom line. By automating your processes. You free up your employees to take on more tasks instead of hiring more staff. 

Vocational assistants and 24-hour chatbots create a better customer experience. And the use of AI to determine whether someone is eligible for a loan usually means looking for people. With good credit who are unlikely to default.

Ethics in AI in the Finance Sector

AI doesn’t come without ethical issues. Particularly when it comes to safeguarding your personal and financial data. Fintech Times identifies three major challenges facing. AI in the financial industry:

Bias: AI errors can occur, and in most cases. The issue is with the underlying algorithm. Here’s a case study from The Fintech Times: If an AI system is tasked with determining a customer’s creditworthiness to maximize revenue. It could soon engage in predatory lending. By targeting low-scoring borrowers for subprime lending. This practice may be frowned upon by society. and considered immoral. But the AI doesn’t understand these nuances.”

Accountability: Who should be held accountable if AI makes the wrong choice? For instance, who should be held accountable in the event of a self-driving collision?

Transparency: Algorithms come to specific conclusions. But how and why they do it isn’t always clear.

There is also the widely held belief in AI. That robots will one day replace human employees. According to Forbes, research suggests that. While AI will eliminate some types of jobs, businesses and companies will have more time to focus on other high-value roles.

Another ethical issue, as reported by Investopedia, concerns the concept of ‘weaponized machinery’. Which refers to the use of AI and machine learning tools for nefarious purposes. Such as the theft of personal data.

The Future of AI in Finance

With the increasing adoption of AI across all sectors. It is no wonder that it is gaining traction in the financial sector. Especially since the pandemic has altered human interactions. Thanks to AI’s ability to automate and standardize processes.

As well as its ability to analyze data and information much faster than humans can. AI has revolutionized the banking industry and is expected to save the sector. Around $1 trillion in revenue by 2030.

AI is part of the future, and banks must embrace it at scale to stay competitive.  McKinsey & Company said. “Achieving success requires a multi-faceted organizational transformation.”