Since public use of the internet first began in the early 1990s, the banking industry has quietly gone through a technological revolution with the advent of artificial technology (AI). Before the rise of digital technology, banking was primarily done in physical locations that involved traditional methods of manual approval of loans and paper forms for every banking transaction and application. 

Advanced metrics and data analysis were also difficult to obtain because data required a team of humans that was slow and inefficient which is why AI has become so important to the global financial industry. Without AI and automation, many of today's most advanced banking services would simply be impossible to imagine, so let’s take a dive into how AI is impacting banks across the globe. 

Traditional Banking Systems

Before AI and the internet, banking was largely done on paper. Rows of clerks and tellers would meticulously write down every transaction from the day, recording it for future use and reference. This meant wait times for withdrawals and deposits were much longer than today as many waited in line with the same payday schedules. Not only was this time-consuming for bankers, but it was also subject to human error which could cause serious turmoil for customers. 

Things slowly improved as computers became commercially available, letting tellers keep more extensive records of banking activity in small storage devices. ATMs also created a major impact as they allowed customers to skip interacting with tellers entirely and gave them more direct access to their funds. 

However, large-scale data analysis and processing was still unheard of as it required advanced teams with specific training to produce insights into client activity. Fortunately, at the turn of the 21st century, AI models built with Deep Learning were able to begin sifting through large amounts of consumer data and set the stage for the online banking systems we see today. 

Major Applications of AI in Banking

There are many ways that AI is being used to assist the banking industry, providing more robust services and protections to customers. They include:

• Customer Service: Chatbots and personal assistants can help customers acquire important information quickly. They can help clients with serious questions when a bank is closed or if they are out of the country. 

• Fraud Detection and Prevention: Machine Learning algorithms can detect strange occurrences in a customer’s banking activity and flag them as potential fraud, protecting a customer’s funds from being stolen. 

• Risk Management and Credit Scoring: Traditional credit scoring relies on strict parameters that can be automated by AI, giving customers more detailed options for loans and other financial services like credit cards. 

• Trading and Investment: AI can help manage portfolios and optimize them for specific investment purposes compared to traditional methods that require clients to call brokers over the telephone to make certain trades. 

• Operational Efficiency and Automation: Predictive analytics can help prevent banking stoppages and outages that affect large swaths of clients by monitoring server status and other operational metrics on a large scale.

AI's Role in Financial Forecasting

Financial forecasting is the act of predicting certain financial trends on a small or large scale. This is important for businesses that must prepare for higher or lower customer activity based on seasonal influences like summer travel and winter holidays. Predictive marketing trends like data synthesis and pattern recognition help businesses recognize specific trends in customer activity and can be enhanced with continuous learning. 

Neural networks can also provide banks with macro-economic analysis too, providing them with a glimpse into entire economies that can influence investment and strategic bank planning. This can be especially ideal if global economies begin to trend downwards, helping banks mitigate losses before recessions and prevent bank runs. 

Challenges and Concerns

Like many other industries, AI poses a major threat to many in banking as it possesses the ability to replace a large number of different roles and jobs. Automation is a huge concern to many, and with AI potentially taking away the more simple jobs like tellers, a new skill gap is beginning to emerge. 

However, it’s not all doom and gloom. The ATM, when it was first developed, posed many similar fears of replacing tellers because of its ability to automate their jobs almost entirely while also giving customers more direct control over their funds. Yet, decades later, tellers still exist and are able to help customers with a different set of matters instead of basic withdrawals and deposits. 

The Future of AI in Banking and Finance

As many already know, money is a driving force for many across the world and is easily one of the most powerful factors in human life, so it comes as no surprise that the future of banking and AI holds exciting new potential for automated financial management. AI has the power to analyze vast amounts of data faster than any human and could soon hold some of the most valuable insights into how to best store assets across separate accounts, giving customers more flexibility in how to handle their finances. 

Furthermore, with technology like blockchain becoming increasingly popular in developing countries where banking is less reliable, AI can create even more valuable insights as it scans through the massive network of public information stored on-chain.