Artificial intelligence (AI) was a major trend in 2017 and its impact was felt especially in the financial services industry. Over the past year we saw AI take center stage at major events like Sibos and Money2020. Discussions ranged from how AI can increase back-office efficiencies in the financial sector and help enhance the customer experience. The financial industry is well positioned for AI disruption given the vast amounts of big data the sector collects daily. According to a recent study by Accenture Research, companies in the financial services sector that embrace AI could improve profitability by an average of 31 percent by 2035.
Banks have recognized the immense benefits of AI, as it allows the industry to access data intelligence and apply those insights to gain operational excellence. Companies that do not embrace AI and machine learning will lose out to competitors large and small.
Using machine learning technologies, banks can improve the customer experience by gaining a deeper understanding of the individual customer to create a customized offering – instead of providing cookie-cutter services that may not suit all. Reflecting on 2017, the banking sector has taken some big steps in its digital transformation journey by implementing AI and machine learning in a myriad of ways.
AI can help banks automate routine tasks to ensure greater accuracy while also enabling banking agents to focus on their main responsibilities. A notable application of this technology can be found in the ticket system at customer service counters, a high-traffic area where customers are looking for a quick resolution. Banks can automatically categorize customer tickets so that employees do not have to take on this tedious task, which in turn speeds up addressing customer complaints.
Goldman Sachs also plans to use AI to automate the initial public offering process. This initiative frees up talent and ultimately increases the bank’s profit potential. Another bank already seeing the benefits of using AI for automation is JPMorgan Chase. The organization introduced a Contract Intelligence (COIN) platform designed to analyze legal documents and extract important data points and clauses. Typically, a manual review of 12,000 annual commercial credit agreements requires as many as 360,000 man-hours. With AI, the same number of agreements can be reviewed in a matter of seconds.
In the financial sector, chatbots are deployed to act as virtual assistants and are ideal for marketing and sales purposes. Utilizing the increased capabilities of AI, machine learning, deep learning and speech recognition, chatbots such as BoodsKapper can communicate and interact in a near human-like manner to expedite customer requests. Banks can manage common customer queries using a chatbot while an agent can focus on more unique problems requiring critical human intellect. Given the amount of customer data that chatbots collect over time, they will eventually become better at understanding customer habits and needs than most humans due to its ability to recognize patterns. Many banks are using a hybrid model of chatbots and human interaction with customers, which seems to be the most successful equation.
Risk management and fraud
AI lends itself perfectly to analyzing the enormous amount of data that financial institutions amass, especially when it comes to detecting fraud and money laundering. Algorithms can analyze copious amounts of data in milliseconds to enhance the evaluation of unusual activities and patterns and flag a potential fraud case to be reviewed. AI and machine learning can help avoid or more quickly discern certain business risks, which aids financial institutions in avoiding or diminishing potentially significant losses or penalties. As criminals become more sophisticated, however, companies need to keep up with technological advancement to combat them effectively.
Banking to an audience of one
Just as Amazon, Netflix, Spotify and Google are able to recommend personalized offerings to their customers, banks can do the same using AI algorithms and predictive analytics. AI and machine learning algorithms can tailor offerings to a specific customer instead of providing a host of generic offerings. By using data based on a customer’s past experiences and transactions, banks can provide personalized recommendations on better products and services, such as new credit card plans or savings strategies. Because AI also enables transactions to be tracked in near real time, these customized offers can be served at precisely the right time, considering the customers’ changing preferences and needs as they happen. Banks can also create a set of predefined parameters for AI algorithms to analyze credit applicants. This would accelerate credit approvals based on credit score, debt, income and age of the customer as AI can make decisions in seconds.
If fintech companies and banks want to stay competitive in 2018, in a market where technology will be the ultimate differentiator, there is no way around AI adoption. Businesses need to make a concerted effort to implement these technologies fast or risk losing significant market share to direct competitors. The rapid growth of AI and the speed of innovation was accelerated in 2017. Next year, we’ll likely see increased applications of AI in the financial sector such as AI in customer self-service scenarios, any kind of fraud detection, and personalized offerings. As AI matures in 2018, organizations will be propelled to create new, unique customer experiences and forge new partnerships within the financial sector. With this technology, companies can own a seat at the winning table.