Data Analytics

How Deep & Machine Learning in Banking Increases Customer Satisfaction

From fraud reduction to anticipating customers’ future demands, these are the key use cases.

Tom Leinberger is the business development manager for financial services at CDW.

Brick-and-mortar banks are struggling to stay open as fintech firms and banking apps evolve. User-friendly, on-demand features are now the norm as customers look for financial partners that treat them as special instead of simply a number. As a result, competition is heating up among banks, and between banks and nonbank alternatives. Recent data suggests that 5.6 million Americans plan to switch banks in the next 12 months, with more than half making the move sooner rather than later.

For financial institutions, changing market conditions speak to the need for improved customer experience: Satisfied clients are less likely to seek out banking alternatives, especially if banks can proactively react to emerging consumer demands.

Accomplishing this means leveraging the Big Data available to banks. To do that, banks need the practical advantage of machine learning solutions to help identify consumer needs, design new strategies and deploy purpose-built best practices.

The Basics of Machine Learning for Banks

According to research from Narrative Science, 32 percent of traditional financial firms have already adopted artificial intelligence and machine learning solutions. But how do these solutions differ, and what’s the potential for financial firms?

At their most basic, deep learning algorithms use massive data sets to analyze and predict outcomes. Think of it like this: Instead of writing code that tells computers how to differentiate between a square and a circle, machine learning uses millions of examples to help devices understand the fundamental difference between these two shapes.

AI is the next step: Implementing machine learning into larger systems capable of planning, analyzing and potentially problem-solving at scale. Improved machine learning algorithms are pushing this process into the mainstream as a way for banks to significantly improve customer service. Here are four use cases and ways financial firms are using machine and deep learning algorithms to their advantage.

Machine Learning Can Approve Loans Faster

Customers applying for credit or looking for loan application approval historically waited weeks. Now, many firms have reduced this timeline to days. But expectations have evolved; more than 60 percent of consumers want an immediate response (less than 10 minutes) to sales or service questions.

As noted by Tech Radar, this has paved the way for the use of ML-driven application assessment and approval. With access to financial data sets, machine learning tools can evaluate multiple credit factors and reach an unbiased decision — and do it much faster than their human counterparts. According to Forbes, JPMorgan Chase saved more than 360,000 hours of work by analyzing 12,000 documents in mere seconds.

Banks Can Use Deep and Machine Learning to Improve Authentication

The rapid adoption of digital banking technologies comes with a caveat: lost passwords and forgotten account details. ML-based virtual assistants are now being tapped to provide speedy customer service — by providing key details, customers can quickly have passwords reset or obtain key financial information without waiting on hold or visiting the branch in person. In fact, some banks are deploying voiceprint, an ML-driven biometric solution, to authenticate customers using only their voices. Solutions like HPE’s Deep Learning have emerged to help companies accelerate data analysis for reliable, real-time results.

Machine Learning Can Help Banks Root Out Fraud

Effective security is paramount for financial client satisfaction — 84 percent of consumers will take their data, and business, elsewhere if they feel defense is lacking. As noted by Emerj, banks are now leveraging machine learning tools capable of identifying common customer behavior, detecting key deviations and then notifying bank staff of potential fraud.

Tools like Splunk offer the necessary speed and scale to manage the scope of banks’ Big Data and improve information security best practices without compromising client privacy. The outcome is twofold: fewer fraudulent transactions for financial firms to remediate, and improved customer satisfaction with data security efforts.

Banks Use Machine Learning to Predict Customer Demands

The ultimate customer experience outcome for banks? Predicting what clients want — before they know they want it. Machine learning tools capable of analyzing large data sets across categories such as buying patterns, demographics, transaction volumes and service requests can help banks create targeted credit, loan or savings offers that are low-risk for financial institutions but high-value for clients.

Customer experience now underpins the competitive future of financial organizations. Machine learning can help banks stay ahead of the crowd on approval volumes, service response, fraud detection and predictive personal interactions.

This article is part of BizTech's EquITy blog series. Please join the discussion on Twitter by using the #FinanceTech hashtag.