Driven to compete with nimble fintech startups, banks are turning to artificial intelligence (AI) to cut costs and deliver a smooth customer experience. Solutions like customer service chatbot and trading algorithms are among the high-profile ways banks have implemented AI into their environments.
Artificial Intelligence (AI) is disrupting industries across the board. AI has become so widespread that 47% of companies that have advanced digital practices say they have a well-defined AI strategy.
Yet, there are a few industries that are leading when it comes to AI-driven growth. Banking and finance is one such industry that is making rapid advancements in AI. In fact, AI is expected to save the banking industry a whopping $1 trillion by 2030.
4 Current Uses of AI in Banking
- Trading Algorithms
- Process Automation
Why is Banking Leading the Way?
Banks underwent a wave of digitization over the past two decades to keep up with changing technology. Accounts went online, internet banking became ubiquitous, and most services were made available to customers through a mobile app.
With digital transformation came increased security threats, however, causing governments across the world to tighten regulations. This has further compromised banks' abilities to keep pace with technology growth.
Already, banks weren't able to make heavy-duty investments in technology because they had to maintain a capital adequacy ratio as per international regulations. Fintech companies that aren’t subject to the same regulations now have a distinct competitive advantage.
Therefore, banks have to meet increasing customer expectations regarding a personalized experience while also reducing costs and improving margins. Increasingly, they’ve turned to artificial intelligence (AI) to solve this problem.
AI-Enhanced Customer Service
When we talk about AI in banking, one of the first things that comes to mind is improved customer service. AI uses customer history to develop a better perspective on customer patterns and behavior. This enables them to customize their financial products and customer interactions, strengthening customer relationships.
Say a customer is looking to buy a new car. An AI-based banking app will guide the customer through the process, giving them an idea of the total outlay and loan approval limits by analyzing their credit history, current expenditure, and income.
At the forefront of this AI-powered experience are chatbots. These chatbots are deployed by banks to act as customer service agents and provide flawless service 24/7. At a very basic level, chatbots understand customers’ questions using Natural Language Processing (NLP) technology. Then, they direct customers to the relevant resource.
Chatbots are also able to guide customers through basic banking operations like opening and closing an account and transferring money.
Over time, chatbots are going to be expected answer less-common queries. Unlike human customer service agents, who typically have to consult their supervisors for unusual queries, chatbots will provide customers faster, more comprehensive service.
The Best Examples of AI-Enabled Chatbots
Bank of America developed an AI-enabled chatbot called Erica who provides financial guidance for customers through both text-based messages and a voice interface.
Unlike actual customer service agents, Erica is available 24/7 and can guide customers through a number of daily transactions. This results in both better customer service, as well as a massive reduction in frontline personnel costs.
American bank Wells Fargo has gone one step further by creating a chatbot that leverages customer data to provide personalized solutions to clients. Their new banking app Greenhouse is also powered by AI. It’s designed to attract millennials by making regular transactions completely seamless.
AI-Powered Trading Algorithms
AI-based bots are now being used on trading floors to help boost performance. These AI algorithms are used to create extremely complex and highly refined investment strategies.
Michael Harte, Barclays' head of innovation, believes that the future of AI in banking is in large-scale algorithmic trading. He says high-velocity data can be used to build cutting-edge investment strategies and get ahead of competitors.
AI is particularly useful in high-frequency trading where order rates are high and trades are executed quickly. Given the small window of time in this type of trading, AI solutions are likely to leverage opportunities more efficiently than people.
Protecting against fraud and money laundering is one of the biggest challenges the banking industry faces across the globe. By processing and compressing data in a fraction of time, AI allows banks to detect these issues and respond to them rapidly.
Even larger institutions that are saddled with legacy systems are now looking to update their technology to manage these issues. Citibank is one of these institutions. Citi uses Big Data and Machine Learning to monitor potential cyber threats and fraudulent activities.
AI-Based Process Automation
Robotic Process Automation (RPA) is already driving automation in banks and financial institutions. However, it's now evolving into what's known as cognitive process automation, where AI algorithms are being used to perform complex automation.
To this end, JPMorgan Chase has invested in COiN, a technology that is able to review documents and extract relevant information in a fraction of the time that it would take a human to do it. COiN can review 12,000 documents in a matter of seconds, something that would take years for a human being to do.
As these tools become more sophisticated, human resources can be diverted from these tasks to more strategic, revenue-generating roles.
AI is Not a Magical Cure-All
Although AI’s role in banking is continuously increasing, it’s already impacted the sector in a big way, with a whopping $199 billion in savings realized just from chatbots and conversational banking.
That isn’t to say AI can’t have troubling side-effects. With its focus on efficiency and cost optimization, opportunities for certain types human employment can drop drastically. In 2018, Citi announced that AI solutions they’d implemented would eliminate 10,000 jobs by 2023.
Further, researches have raised concerns that AI-powered investment strategies may contain hidden biases and discriminatory practices. These so-called “black box” algorithms are opaque, and regulators are attempting to ensure human validation of their processes to avoid unintended consequences.
That being said, the responsible implementation of AI by banks can deliver powerful benefits regarding customer experience and cost reduction.