Digital Banking

With AI-Banking Growing, Ally Seeks To Humanize Conversational Tech

Banks want to get smarter even — or perhaps especially — if that includes a dose of artificial intelligence (AI).

As of 2017, 32 percent of financial executives reported they would focus their investments on AI technologies like predictive analytics, voice recognition and recommendation services. While AI allows customers to communicate with their banks using voice recognition or chatbot services, research indicates customers still consider human-to-human interactions a primary part of the banking process.

That appears to stand true for older generations and digital natives alike. Today, face-to-face interactions are reportedly preferred by more than half of millennials, and 80 percent of them still want the option to visit a bank branch in person.

In that spirit, Ally Bank is working to find the balance between introducing virtual assistant technology into its banking operations and maintaining a personal experience for customers, according to Arvy Rajasekaran, the company’s chief information officer of digital channel technologies. In a recent interview with PYMNTS, Rajasekaran discussed how Ally is working to offer more human-like engagement while simultaneously introducing AI and machine learning solutions.

Making AI interactions more human

AI-based solutions are familiar territory for Ally Bank. The FI first introduced its virtual assistant, Ally Assist, through its mobile app in 2015. The service enables users to interact with the bank using speech and text message commands to perform bill payment, transfers and account information requests, among other tasks.

The company followed that up late last year by releasing an Ally Skill for Amazon’s Alexa, an offering intended to grant customers access to account information on Amazon Alexa-enabled devices like the Echo or Echo Dot. Especially helpful to a shopaholic, the service also offers insights into how many hours a user would need to work to earn the money for a specific purchase based on his or her income.

According to Rajasekaran, Ally sees these developments as ways to reintroduce human-like experiences as customers interact with their financial service organization. The goal is to improve communication with customers, regardless of whether those customers choose to connect with their banks using voice commands or text messages.

The bank views AI and machine learning-based tools as opportunities to develop a “common brain” for these type of interactions, ensuring the FI offers customers a conversational experience that both feels natural and aligns with Ally’s brand identity and voice.

“Overall, the interest in AI and machine learning is to make conversations more human and find ways to engage with our customers better,” he said.

Intelligent engagement and smarter security solutions

Giving customers an opportunity to engage with the bank using everyday language can be more than just a “novelty,” Rajasekaran explained.

Ally recently began working on a new solution that aims to use core data science to build profiles of their individual financial habits. The solution is designed to learn and build profiles about a customer’s financial habits as he or she engages more frequently with AI and machine learning systems.

This type of knowledge opens the door to new opportunities to engage with customers, Rajasekaran said, and all based on the insights the system produces. It can also help the bank detect fraud using a portfolio of available APIs, allowing it to work with the customer to understand if, and how, their financial habits are shifting.

“As the [solution] learns customer user behavior patterns specific to each customer, it identifies anomalies,” he said. “It gives us the opportunity to reach out to customers and have that conversation and say, ‘Is this something that you are doing? This seems out of pattern for the type of transactions you do.’”

As the bank’s AI and machine learning capabilities continue to advance, and the FI develops a deeper understanding of customer financial habits, Rajasekaran believes customers will come to appreciate having these systems available.

“That, to me, is making machine learning come to life in a very human way,” he said, adding that investing in the tools not only helps FIs like Ally improve their engagements with customers, but also serves as a valuable asset to ward off potential fraudsters.

“Security, fraud and risk management is an arms race,” Rajasekaran said. “We’re going to need tools like machine learning and AI to keep winning.”

Banking’s virtual and augmented reality

Beyond investing in voice and text-based solutions to engage with customers, Ally Bank is also exploring virtual reality (VR) and augmented reality (AR) solutions — even for transactions.

In fact, some of the company’s AR-based solutions are already being put to use. Its mobile app has an ATM locator feature that enables an augmented view of nearby ATMs. Before this year’s Super Bowl, Ally launched an AR-based mobile savings game designed to encourage viewers to interact with their home environment and earn points to be deposited into a virtual piggy bank.

Rajasekaran said FIs will have new opportunities to engage with customers as technology continues to be integrated into financial operations. Investing in them to improve customer engagement has prompted Ally to re-evaluate its own identity.

“At every stage, we really think of ourselves more as a technology company in the guise of a finance company,” he said.

As customers become more comfortable engaging with the bank using voice-based commands and text messages, Rajasekaran believes it will be important to remember they are using everyday language and that responses should match Ally’s brand identity.

“The number one goal is to make sure we don’t put a capability out there that doesn’t fit the brand of Ally, that doesn’t feel human,” he said.

It seems customers should continue to expect human-like interactions from their financial institutions for the foreseeable future, even as the role of AI and machine learning continues to grow and evolve.

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