How AI in Banking Helps the Financial Area: Samples and Cases

Every year artificial intelligence-based tools cover more and more business niches. At first, it was mostly entertainment and personal aid (probably, every person at least heard about Apple’s smart assistant Siri or its competitors). Today this technology is beginning to enter the financial sphere, including banking. Below we are going to discuss the main problems solved by artificial intelligence in banking sector, and also consider seven existing solutions that are actively exploited by well-known US banks.

Artificial Intelligence in the Financial Sector: Overview

Currently, the use of AI in banking “took root” in three major branches of corporate activities:

• Customer service. The most trivial way to apply AI in banking is to extrapolate personal assistant routines to business relationships service in the form of a chatbot. All of us have probably come across online chats in banking services, in which chatbots conduct dialogues with users and answer most typical questions without the participation of a live operator. Artificial intelligence helps to make the UX many times more personalized, and the answers – more accurate and consistent with the expectations of the client.
• Big data processing. In modern banking, every minute servers receive enormous volumes of more or less unstructured data such as financial transactions, movement of funds, deal conclusions, customer requests for comments, etc. In the past years, a new field of science appeared and started to develop – data science. This discipline is directed at analyzing structured and unstructured data, extracting knowledge and insights from it, discovering trends and building accurate predictions. One of its sub-fields is Big Data – a set of methodologies, tools, principles, and algorithms, directed at processing huge volumes of unstructured information. Artificial intelligence is one of the most useful tools here. In practice, AI in banking system does not just analyze the data but selects the best way to process it, which ensures the accuracy and speed of analysis on a fundamentally different level. For instance, this helps banks to actively introduce fraud and other anomalies’ detection systems.
• Automation. And, of course, any scalable company in modern business cannot do without AI-based automation solutions that take on some of the daily responsibilities of full-time employees and thus relieve them from routine tasks (for example, scanning the incoming correspondence and extracting the necessary information).

AI in Banking – an Analysis of America’s Seven Top Banks

And now let’s take a brief look at seven solutions that have been integrated into existing business processes of top financial corporations in the United States. This review should help you better understand how to use AI in banking:

• CitiBank. We wish to begin with a great example of a product for fraud detection in banking using AI. It was implemented by CitiBank – one of the institutions that make a major stake in providing financial security first. Feedzai is a platform that allows detecting and predicting cases of fraud in real-time by analyzing data streams, including those that come from previously unknown sources. This product minimizes the criminal intent risks, thereby increasing the reliability of the bank and customer confidence.
• Wells Fargo. One of the established market leaders, Wells Fargo bank has a dedicated AI developments group – Artificial Intelligence Enterprise Solutions. Since its establishment in February 2017, this department released several products and services. Among these, we consider the most interesting the AI-based personal finance advising option connected to their mobile app. Not only does it help to conduct account operations, but also serves as an online financial assistant.
• Bank of America. Bank of America has created a voice-controlled virtual assistant for internet banking, called Erica. It aids the customers in the implementation of all those operations that were previously available only in offline branches.
• US Bank. This bank is among the leaders by the number of funds and resources spent at the integration of AI-based solutions. USB created its own research group studying AI/machine learning and puts these two concepts into practice. Currently, they test the results of the introduction of AI- and ML-based technologies into all aspects of the bank’s operation.
• PNC. PNC is another financial institution that spends a lot of effort in developing AI-based services. Their main points of focus include the introduction of chatbots to ensure an individual approach to each client and automated treasury management system.
• BNY Mellon. The Bank of New York Mellon Corporation, which exists on the market for more than 2 hundred years, pays a lot of attention to innovative technologies – AI, ML, blockchain, robotics, etc. For example, they developed and deployed across all their businesses more than 200 chatbots with the goal of continuous digitalization of everyday work processes. In particular, such solutions are programmed to independently set multiple triggers and react to them, thus reducing the burden on live employees and minimizing possible risks associated with human factors.
• JPMorgan Chase. The last but not least artificial intelligence in banking case study is JPMorgan Chase Bank. This institution is often mentioned among the largest innovation adopters. One of their most interesting products is called Contract Intelligence (COiN). This narrowly specialized solution for legal document analysis and data extraction is actively used in all the branches of the bank.

AI in Banking: Prospects

Well, what are the prospects for AI in finances? In fact, we were able to identify five main options for the implementation of artificial intelligence in banking, which will be the most promising in the next few years:

• Improvement of the client service quality and further personalization of customer experience (AI in mortgage banking is now particularly popular because, with the help of solutions based on this concept, customers can independently calculate mortgage rates and payments);
• Market forecasting (everything is clear here – assessment of risks, prospects, customer involvement, etc.);
• Fraud detection (the speeds with which modern AI can process huge amounts of unrelated information already allows detecting non-standard customer behaviors and criminal activity with high accuracy);
• Technological competitiveness (here we are talking about all sorts of service applications for banking, which add popularity to the bank and inspire customer trust);
• Personnel work coordination (in today’s market, large networked corporations do poorly without highly automated business processes).

Summary

As you may already understand from our analysis of AI situation in banking, artificial intelligence opens up the broadest prospects for the banking industry and in the near future, AI-based products are only going to grow in number. If you also wish to bring a project of similar orientation to life, you definitely cannot do without professionals. In particular, our developers are expert in developing AI-based solutions for the financial sector, and therefore it would be easy for them to implement the product of your dream within the stipulated time frame.