— September 3, 2019
Machine learning technology is making rapid gains in the financial services industry, and a growing number of companies are now using it to enhance the customer experience. Gartner even predicts that by 2020, 85% of customer relationships with an enterprise won’t involve interacting with another human.
Instead, machine learning will be leveraged to create a more streamlined and rewarding customer experience. Here are some of the primary ways machine learning can be used in financial services, and the key benefits this technology offers.
Machine learning and banking
Determining fair pricing on complex banking products like corporate credit cards and merchant acquisitions has never been easy. It’s something banks have struggled with for years. Leveraging advanced analytics, however, has been a game-changer, and puts financial institutions in a more favorable position.
According to McKinsey & Company, “Using new data sources, technologies, and modeling techniques, these early adopters are providing front-end staff with in-depth views of customers and prospects, including such information as to their product-acceptance probabilities, price sensitivities, propensities to churn and lifetime values.” In turn, bank managers can provide more personalized customer experience and offer extremely targeted pricing for each micro-segment.
Needless to say, this can create a major competitive advantage. By gaining in-depth insights through machine learning, banks can fine-tune their products and services, keep customers happier, and reduce customer attrition rates.
A key part of the financial services industry is providing customers with financial guidance. While customers have historically used portfolio management services – something that’s quite labor-intensive – machine learning has changed that. Now customers can receive excellent advice from robo advisors based on incredibly detailed analyses to make strategic decisions and improve their financial health.
For example, Wealthfront, an automated investment service, provides financial advice through a smartphone app. They use advanced software that’s programmed to follow smart investment strategies and identify the best opportunities. And the machine learning capabilities ensure their software grows smarter and smarter as it accumulates more data.
The best part is that it can use a wide variety of strategies so the experience is fully customized for each individual customer. Not only that, it’s far more cost-effective than hiring a traditional financial advisor – something most people will appreciate.
Trading is a notoriously complex process that requires access to vast volumes of data for effective decision-making. That’s something machine learning can help with as well.
“Algorithmic traders often make use of high-frequency trading technology, which can enable a firm to make tens of thousands of trades per second,” explains James Chen, Director of Trading & Investing Content at Investopedia. “Algorithmic trading can be used in a wide variety of situations including order execution, arbitrage, and trend trading strategies.”
By utilizing this advanced technology, traders are able to swiftly buy and sell assets, which gives them an edge that would otherwise be impossible. No matter how skilled and knowledgeable a human trader maybe, they simply can’t compute massive amounts of data in seconds as machine learning software can. As a result, they can be far more strategic with their trades, greatly increasing the odds of big profits.
Having great customer service is vital to the longevity of nearly every business. It’s what boosts customer loyalty, opens the door for cross-selling opportunities, improves brand reputation, and makes a company more profitable. So of course, organizations are always looking for ways to better their customer service.
Machine learning has several applications here. One is helping chatbots interact more fluidly with customers. By leveraging vast quantities of data, chatbots can better determine when someone is feeling frustrated and come up with an appropriate response to resolve the situation.
Another is identifying customer intent. Machine learning can figure out why a customer is contacting a business based on previous data, and promptly direct them to the appropriate party. For instance, a customer may be routed to a particular sales agent who’s best suited to handle their inquiry.
In addition, it can be used to identify the optimal channel for contacting customers, such as email, text, Facebook Messenger, and so on. This too is important because each person has their preferred means of contact, which eliminates a lot of friction.
Finally, financial institutions can significantly lower their risk level by applying various machine learning techniques. For example, say a bank is looking to assess a person’s risk level when applying for a mortgage. Now they can efficiently manage this risk by analyzing a large number of data sources with more layers of information than was ever possible in the past. Rather than being limited to basic information like someone’s credit score, they can analyze large volumes of personal information to reduce their risk.
And by using an advanced process like web data integration, they can greatly improve the quality of their data and make it easily digestible. “IBM estimates that poor data quality costs businesses in the U.S. more than $ 3 trillion annually,” says Gary Read, CEO of Import.io. “An end-to-end web data integration strategy is game-changing for those serving the financial sector, e-commerce or other data-driven businesses.”
The use of AI has increased by 270% over the past four years. Of the various forms of AI, machine learning was one of the most widely-utilized, with 58% of companies adopting it – a number that was up 5% year-over-year. This goes to show how pervasive it’s becoming, and that more and more organizations are recognizing the impact it can have.
While machine learning can serve many different industries, it’s especially advantageous in the financial services industry. As you can see, it has several key applications and can aid in everything from providing legal advice and streamlining trading to optimizing customer service and minimizing risk.