Indeed, there’s evidence that AI-enabled apps and tools can save financial institutions up to $447 billion by the end of 2023, and the majority of banks are well-aware of that. There are a lot of AI solutions being developed right now, and they’re poised to begin changing the way banking is done and how people communicate with banks.

And it makes perfect sense to introduce AI here because the global financial industry is not a world of humans and vaults, but mostly computers and networks. The monetary transactions are nothing more than an exchange of information over these networks, which means that technology is the answer to making them more efficient and secure.

That’s why the number of impressive products from as well as the number of FinTech companies continues to grow. The applications of their products are already impressive and include the following areas.

1. Banking Chatbots

The use of electronic personal assistants that mimic human online behavior - chatbots - is exploding in the banking industry. With the arrival of artificial intelligence, the time of uncertainty over their usefulness is finally over, and the exciting time of development has begun.

The main promise of AI-powered chatbots in banking is their 24/7 availability as well as the ability to provide clients with helpful answers without having to visit a branch. For example, Erica, Bank of America’s new chatbot, helps customers with:

• Sending and receiving money
• Credit score changes
• Keeping up with electronic bills
• Locking and unlocking a debit card
• Getting information about refunds
• Checking available credit
• Ordering foreign currencies
• Reporting a lost card.

The full list of features is available on Erica’s official page at Bank of America’s website, and it’s really impressive.

According to FIS’ 2019 Performance Against Customer Expectations (PACE) survey, about 75 percent of all bank transactions are now completed on mobile, so people are totally ready for more functionality. That’s why chatbots remain a popular choice for development for FinTech companies, so we’re definitely going to see more of them in the next few years.

2. Personalization of Banking Services

One of the most important promises of AI for any industry is the personalization of customer experience. With most customers expecting personalized experiences with businesses, banks included, increasing the relevance as well as the speed of banking services is something they would enjoy.

Let’s consider a simple example of a mortgage application. The experience of getting one is often nothing but enjoyment, so many people would love to see this process improved somehow. That’s where AI comes in. Thanks to the ability to process tons of data quickly, a machine learning algorithm can enhance some of the most time-consuming parts of this process (for example it can make affordability checks easier and faster to get).

On top of that, AI can help banks with selecting mortgage recommendations based on the needs of individual clients. The possibility of such impressive advances as real-time, hyper-personalized banking product recommendation and even creation is also viable.

Let’s not forget tools like chatbots here, too, as they also serve to improve the relevance and personalization of banking services.

3. Online Fraud Detection

Another area in which FinTech can really help to increase the security of online transactions is fraud identification with AI. Unfortunately, cybercriminals are getting smarter and develop more sophisticated ways to attempt fraud; in fact, the recent McAfee report suggested that cybercrime costs the world $600 billion, and fraud is a big part of that.

Research has defined that AI has the ability to detect fraud by analyzing vast amounts of data and detecting suspicious behavior patterns. For example, a machine learning algorithm is able to define, understand, and anticipate certain behaviors at any stage of a financial transaction. It tracks the behavior in profiles containing both monetary and non-monetary transactions, and checks the progress with each completed transaction stage.

For example, by analyzing typical behaviors of clients, the algorithm may indicate if suspicious, extraordinary activity occurs. One of the banks that has tried and improved the detection of fraudulent activity was Danske Bank. The organization struggled with a 40 percent fraud detection rate and had a high number of 1,200 false positives every day.

In this case study found on their website, they describe the following outcomes of applying AI to recognize potential fraud cases:

• A 60 percent reduction in false positives
• A 50 increase in real fraud identification
• More efficient use of fraud detection resources.

Definitely, AI can be really beneficial for banks here, as the effectiveness of manually-based approaches to fraud identification is interior to technology. Moreover, as FinTech companies continue to improve AI-enabled online fraud detection, they’re becoming closer to a type of software able to handle a continuous stream of incoming customer data in real time.

4. Faster Responses to Credit Applications

Without a doubt, risk is the deciding factor considered by a financial institution in any credit application scenario. To reduce it, a bank typically begins with a thorough credit check of the applicant, often using external agencies and organizations. While this initial step can be done relatively quickly, updating the data on which it’s based is a totally different story (in fact, the updates are typically carried out monthly).

AI can help to improve this process and ensure an instant response to credit applications. According to the report on AI and banking from Accenture, a machine learning algorithm can quickly analyze credit data from multiple sources - utility companies, electoral registers, financial service providers - and come up with an informed decision considering real-time data of an individual applicant.

This is already happening. FICO, the global credit agency, has recently partnered with a FinTech startup called Lenddo to create digital footprints - a credit applicant’s creditworthiness - by analyzing such previously unexplored factors a person’s geolocation, social media account use, and the history of online behavior.

This could be huge for countries like India, where about 350 million adults have no access to lending services due to a lack of sufficient data to determine creditworthiness.

Moreover, the algorithm could also determine the best credit conditions based on that data, thus helping banks to ensure that only genuine customers get a personalized product that meets their needs in the current circumstances. Ultimately, such an algorithm will make credit application a much easier and straightforward process for everyone involved.

AI and FinTech: A Powerful Combination

FinTech innovations are quickly and surely transforming banking of all stripes. The use cases listed in this article are nowhere near exhaustive, so stay tuned for more exciting news about AI changing the banking industry.

AUTHOR BIO

Angela Baker is an experienced writer whose biggest interests at the moment include blockchain and artificial intelligence. Working as a freelancer writer at Studicus, Grab My Essay, and other online writing services, she authored numerous reports, case studies, and eBooks on these topics.