Unless you’ve been saving your credit card rewards for a specific purpose, such as paying down your existing debt or purchase airline tickets for a vacation, sifting through the credit card reward options can be overwhelming.

Some major credit card providers, however, are using artificial intelligence to make it easier to not just choose your rewards, but offer the rewards consumers want the most. In this drive for hyper-personalization of our rewards, artificial intelligence banking is benefiting all parties involved.

According to a recent study, 33 percent of customers who abandoned a business relationship did so due to lack of personalization.

But artificial intelligence in banking is changing that.

Deployed by major banks including HSBC and Bank of America, AI and predictive analytics make it easier for banks and loyalty program issuers to determine what rewards consumers will want at any given time and offer those rewards, along with incentives to use their card for future, similar rewards.

What is AI?

If you haven’t heard the term used before, artificial intelligence is when computer systems or machines are able to perform tasks that normally require human intelligence. These skills include speech recognition, as in the case of the Amazon Alexa virtual assistant or Apple’s Siri, decision-making, and visual perception, such as facial recognition.

Futurist Andrew Ng wrote in the Harvard Business Review, “If a typical person can do a mental task with less than one second of thought, we can probably automate it using AI either now or in the near future.”

These tasks primarily involve pattern recognition and comparisons of data. Sophisticated marketing software uses AI algorithms for ad retargeting on social media platforms. The software can recognize, from the thousands of users who saw an ad or visited a website, those who are most likely to click an offer.

So where do these skills AI offers come into play in banking and designing better credit card rewards programs?

How AI banking could change what’s in your wallet

Just as social media marketers use ad retargeting to deliver more relevant ads to users, banks and credit card companies can design rewards programs based not just on basic demographic information, but on your past redemption activity and buying behavior.

The banks are already collecting this information – it’s available within your online accounts and on your statements. But it would take countless hours for human beings to sift through the data, find patterns, and derive logical conclusions from those patterns.

AI makes that kind of hyper-personalization possible, so you get more relevant rewards that you’re more likely to use, delivered proactively – before you even realize you want to redeem the rewards you’ve accrued.

You could have two choices when redeeming rewards in the future:

• Sift through pages of offers to analyze how you can get the most bang for your buck with rewards you’ll use or;
• Receive relevant rewards suggestions with the best return, delivered directly to your computer screen or mobile device.

Which would you choose?

Hyper-personalization: the future (and present) 

AI needs lots of data to work effectively. Fortunately, the majority of consumers are willing to share personal information if it means improved services or products, according to a digital banking report sponsored by Personetics, a digital banking solutions provider.

HSBC has already rolled out a pilot program that uses AI to deliver more relevant rewards and seeing redemption rates of 70 percent based on the AI-generated recommendations. To determine the best rewards, the software analyzes the user’s purchasing and redemption history.

But the potential of an AI card goes far beyond the typical or expected predictions. While a human being might assume that someone who redeemed their rewards for a flight to Orlando in the past might do so again, AI can aggregate all their past spending habits and recommend other offers that could be an even better fit.

And that’s only a fraction of the data programs could use to make rewards recommendations.

Tapping into location tracking on your phone, your AI card could deliver promotions via text with location-specific offers, from tickets for tourist attractions in a city you are visiting to gift cards for stores within a shopping center.

Best credit cards and banks for AI banking

Several of the major banks are already using AI banking for hyper-personalization and improved customer service.

HSBC makes great strides with AI program

HSBC is on the cutting edge, putting together a Client Intelligence Utility with 10 petabytes of corporate and institutional data from 1.6 million clients.

For reference, there are 1 million gigabytes of data in one petabyte. A single petabyte of storage can hold 13.3 years of HD video, and the entire written works of all time would take up 50 petabytes, according to this Gizmodo infographic.

HSBC’s program, so far, is experimental, but it’s safe to assume that the company’s top tier rewards cards, like the HSBC Cash Rewards Mastercard® credit card, will soon employ AI to make the best rewards recommendations, if they aren’t already.

In fact, more targeted rewards choices and hyper-personalization could help make up for the card’s relatively modest 1.5% unlimited rewards. If HSBC impresses users with proactive, useful rewards suggestions that make redemption easy and provide more value, it could keep customers using the card long after the first-year introductory rewards have been exhausted.

American Express® embraces AI

American Express is known for its high-end rewards and its travel concierge service. The charge card and credit card provider is now incorporating AI into its travel services with the purchase of Mezi, an AI-powered virtual assistant and chatbot that provides services normally offered by personal shoppers and travel agents.

Right now, Mezi’s services are offered as a perk to American Express cardholders through an downloadable smartphone app, AskAmex. Many Amex offers, such as rewards redemption and 2X rewards offered through AmexTravel, are not available through AskAmex, yet. But it’s not a stretch to think that integration between the programs could be the next step.

For now, cardmembers can “AskAmex” for the convenience of travel suggestions based on voice queries, and then book through AmexTravel using their American Express® Gold Card to maximize their points.

Let Bank of America’s erica guide you

First, there was Siri. Then, Alexa. Bank of America’s counterpart to the American Express AI chatbot is named erica, and “she” already has more than 1 million users.

Right now, the capabilities include searching for transactions, transferring money, or checking account balances. For instance, let’s say you make a purchase at Walmart using your Bank of America debit card and you want to return the item without a receipt. To expedite the return process, erica can find the transaction for you.

This virtual banking assistant was introduced shortly before Bank of America revamped its top-tier Bank of America Cash Rewards credit card. While erica currently doesn’t integrate with Bank of America rewards programs, it would be a logical next step.

The newly revamped Bank of America Cash Rewards® credit card enables users to choose their own bonus categories to earn 3X cash back on up to $2,500 in combined choice categories. While this benefits card users, giving them more flexibility to maximize their rewards, it also enables Bank of America to collect data on customer preferences and create more tailored redemption programs using AI software.

In the future, it’s possible that Bank of America’s cash rewards programs will also integrate with erica, enabling the AI to suggest relevant rewards.

Voice and AI will learn and improve

While voice recognition isn’t necessary for a successful AI card, it helps. Bank of America reports that more people are using erica through tap and gesture functions, with voice and text being used equally after that.

Voice recognition is still not perfect, and there will be learning on both sides, by people and the machines, before we can have seamless spoken conversations. But the more voice is used by today’s AI-powered virtual assistants, the better erica, Mezi, Siri, Alexa, and all the rest will get at understanding us.

Artificial intelligence banking in the future

In general, the more data AI-powered software can gather, the more effective it will be. Credit card issuers, with a world of consumer data at their fingertips, stand in a strong position to provide their customers with the best options for their lifestyles.

Banks are already using AI to make banking easier and help consumers make the best choices to get out of debt.

Hyper-personalization, more relevant rewards and even better choices for bonus points are the future, and the present, of AI-powered credit cards.

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A great deal of discussion has surrounded the importance of building a seamless digital account opening process. As found in the Digital Banking Report, Digital Account Opening and Onboarding, progress is beginning to be made, as more organizations are allowing consumers to open new checking accounts online and on a mobile device. With the exception of a very few progressive banks and forward-thinking fintech firms, however, not nearly as much progress has been made in digital lending.

To date, the focus of most digital investments in lending have been to improve the customer-facing digital output, with significantly less attention placed on digitizing the back office. While some of the resulting changes may improve the customer experience in the short-term, these mostly cosmetic changes are easily replicated, and don’t necessarily improve the overall customer experience.

Having a digital application is not enough. According to PwC, a financial organization must initially define what is desired from both a customer experience and operational efficiency basis around consumer lending. Next, banks and credit unions must build a digital lending strategy around the following organizational competencies. The path to becoming a true digital lending organization involves five steps.

1. User-Centric Design: Applying design thinking principles, oriented to both internal and external users. Tools that are used to support user-centric design include journey maps, borrower personas, ideations of multiple concepts, listening labs and prototypes.
2. Data-Driven Decision Making: Making the right information available at the right time, to the right stakeholders, in the right format. Artificial intelligence (AI) and machine learning can match customer needs with the right product … proactively.
3. Flexible Infrastructure: Systems and architectures must be configurable, aligning to real-time requirements that may extend beyond traditional ecosystems to accommodate external APIs.
4. Effective Development Approach: Development capabilities must align with the need for a much more intense speed-to-market functionality. Delivering ‘high velocity’ IT and an agile framework that is iterative is no longer optional.
5. Organizational Agility: Beyond systems and back office processes, there needs to be a tearing down of the silos that can stall development and implementation. This requires a change in culture that supports innovation and promotes change.

Digital Borrower Expectations

The expectations of the digital borrower have increased over the past several years, mostly based on marketplace offerings and digital experiences in other industries. While the interest rate and closing costs on loans are still primary considerations, the speed, simplicity, transparency and customer service of the entire process is important.

Certain segments prefer human interaction for certain parts of the process, but digital is a requirement for lenders hoping to compete across demographic segments. For the digital-first consumer, there are very few end-to-end mobile applications. There is a great opportunity for lenders hoping to differentiate with a mobile option that can go beyond the application to compare products, see status, lock in rates and calculate affordability.

The ability to cross-sell current customers on loan products drives a significant portion of new loans. The difference for a digital-first customer is that they do their shopping online and may select an alternative provider based on the right combination of cost and ease of process. An underused opportunity exists for financial organizations who can provide pre-approval notifications before the customer realizes they need a loan.

According to the PwC report, Consumer Lending: Understanding Today’s Empowered Borrower, three out of four demographic segments prefer to be online for each phase of the lending process as opposed to traditional methods, such as in person or on the phone. This is far different from the expectations only a few years ago.

The preference for digital lending also extends for all types of consumer loans, including student loans, personal loans, home loans and auto loans. Compared to a previous mortgage study done by PwC in 2013, there was a significant jump in digital expectations for each step in the mortgage loan journey.

The chart below shows the mobile app features that consumers indicated they would like to see, broken out by Millennials versus non-Millennials. While some lender apps offer the higher-ranking features – such as the ability to calculate the loan amount that the borrower can afford and the ability to lock in an interest rate on a loan, most of the other features are still not offered by most organizations.

The biggest opportunity in the marketplace is the ability to continuously evaluate a customer’s credit worthiness and need based on artificial intelligence (AI), including a pre-approval loan amount on the front screen of the online and mobile banking app and in alerts. Letting a customer know they can get a personal loan at any time with an organization in a matter of seconds can avoid competitive shopping and increase loyalty.

Being a Digital Lender is More Than Just Fewer Clicks

To become a digital bank, organizations need to think beyond ‘minimizing the number of clicks’, reducing manual data entry, and improving the speed of decisions. In the PwC report, Getting a Bang For Your Digital Buck, it is emphasized that digital investments need to be aligned to the overall company strategy and desired omni-channel experience. Equally importantly, the report emphasizes that lenders need to develop the right internal capabilities to be able to deliver innovation in a timely and effective manner.

The process of becoming a digital lender for the long-term moves investments from ‘digital features’ to a ‘digital mentality’ and process that can support changing digital lending options. It is a major move from investing in just digital output to investing in the digital input that works behind the scenes. It is a strategic framework for the future of digital lending.

As with any digital banking transformation, the adjustment is far more than turning paper into clicks. The transformation must look at all current processes, refining what was done to better accommodate a digital consumer, using data, advanced analytics and artificial intelligence to eliminate steps and improve the process. The key is to avoid recreating bad processes online, instead fixing underlying inefficiencies by letting design lead technology decisions.

Build, Buy or Partner?

Change in the banking industry is happening quickly and outside of the control of most financial organizations. It is not just happening in the lending area, but across all product lines and segments. The consumer is asking for more than most organizations can deliver alone. The question becomes – how do we deliver what is being asked for?

Digital lenders need to determine if they build their own solution, buy a solution or partner with a fintech firm or another organization to deliver what is required. In most cases, the solution will be a blend of in-house capabilities and vendor or partner solutions. And there is no need to fulfill all of the needs with the same partner/vendor, making compatibility of solutions a major determinant of success.

According to PwC, “Many lenders are partnering with start-up technology firms or vendors offering solutions that are built to be integrated with their digital suite to enhance their offerings or gain a competitive edge. Marketplace lenders have excelled at the partnership angle and financial technology firms are looking for lender partners to strategically combine forces with a unique go to-market solution.”

Key considerations include:

• Strategic Positioning: Do you want to be a leader or a ‘fast follower’. (Be careful, since the amount of time between the two options has shortened immensely in the past couple years)
• Budget: How much can be allocated to the lending transformation process?
• Customer Experience: How do you want your organization to interact with your customer or members in the long-term.

The Future of AI, Machine Learning and Predictive Solutions

The future of digital lending will reduce the friction associated with the borrowing process, eliminating paper and moving all of the steps of the customer journey to an online and mobile capability. Going beyond customer-facing digitization, the internal process will be evaluated, eliminating steps that are not in alignment with being a ‘digital bank.’

The most important change in the future will be the application of customer insights and advanced digital analytics (including AI and machine learning) to better understand and respond to customers’ personal needs … in real time. Perfectly executed, the customer will no longer need to apply for a loan in the traditional sense, since the amount of credit available will be continuously processed and communicated to the customer with alerts, notifications and mobile messaging.

When the customer accepts a predetermined offer, all supporting documentation will be pre-filled and ready for customer acceptance. The acceptance and any additional interaction may change as well, being supported by chatbots, wearables, voice-first interaction and digitally-augmented human support.

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