2018-2020: How AI and Machine Learning will Transform the Banking Industry

I remember the first time I lost my ATM card at a rickety, worn-out local machine. Smudged by the not-so-gentle pokes of a thousand fingers, it swallowed my card slowly, and all I could do was feel the back of my neck go warm with panic.

What followed is anyone’s guess. Incessant calls to customer care, getting redirected to the automatic IVR with its many unintelligent responses, followed by endless call-waiting. After a long forty-five minutes, I successfully blocked my card.

To the uninitiated, the term Artificial intelligence brings to mind the image of some Terminator-esque entity out to take over our lives and our jobs. But to those that are aware of its potential, AI and machine learning in banking offer a better and more productive way of doing things that were previously time-consuming and far from customer-friendly.

Something as simple as blocking a card when the ATM swallows it up.

46% of large FinTech companies consider AI to be one of the most relevant emerging technologies to invest in within the next 12 months, as per the PwC Global FinTech Report 2017.

AI and Machine Learning have emerged as one of the hottest topics of discussion in industries across the globe. But what does it do for banks and financial institutions in particular? Here is a quick look at how AI and machine learning are set to transform the banking industry in the future.

Evolution of banks as a partner, not just a provider

In the decades that lead up to the technological revolution, financial institutions were seen as providers or enablers who offered a service that made managing finances an easier affair. At best, bigger accounts had portfolio managers who reached out to their assignees with an e-card around Diwali or Christmas. Technology changed this and put the control back in the hands of the customer.

The biggest contribution of AI and machine learning in banking, just like in other service industries, lies in the universal democratisation of services. It shifted the focus to the customer and rewrote the role played by banks in their current form. According to a study from EY, banks and credit unions are focussing on using technology to move from being transactional and regulatory to becoming innovation-lead firms that act as partners in their customers’ daily lives. A huge contributor to this change in priorities is the increase in competition from existing players as well as new entrants in the industry.

Research shows that adoption of Fintech firms as providers of money transfer and payment services rose from 18% in 2015 to 50% in 2017. Banks today need to find a way to differentiate their offering from other competitors and present a more personalized and customer-centric experience to their customers.

Hyper-Personalization: Going from transaction to prediction

According to the Capgemini/RBS World Payments Report 2015, total non-cash payments globally, including all wholesale and retail electronic payments, amount to 389 billion per year, equivalent to 1.06 billion movements per day. These countless data points contain a wealth of information that was up until now either too vast to comprehend or not in a format that could be combed for insights. Increased investment and subsequent advancement of AI and machine learning in banking help translate this data into pointed insights that help future decisions.

If step one in the adoption of AI and machine learning was to simplify existing processes with intelligent automation, the future lies in being able to offer bespoke solutions that are tailored to each customer based on their previous interactions. The evolution of chatbots in banking is a definite example. Banks such as American Express and Wells Fargo have already introduced Facebook Messenger bots to help their customers with details about their financial transactions over chat. Since the bot is tuned to learn based on each transaction and chat conversation, the interactions become a lot less transactional and human-like with time.

Amex’s chatbot, launched in August 2016, started off as a simple chatbot alerting users of recent transactions and relevant benefits on the card. However, it has evolved into a predictive assistant that analyses past data and offers helpful suggestions such as which new card to apply for.

Quoting American Express’ Mathew Sueoka,

We’re investing in artificial intelligence and other technologies so that we can make these services smarter, more adaptive, to continue to learn over time.

Chatbots present a dual benefit to banks. Increased number of interactions and continuous learning over each of those interactions helps the bot transform from a transactional entity offering textbook responses to basic questions into a personal assistant that offers intelligent suggestions and personalized recommendations. Simultaneously, the transactions allow banks to gather a lot more information about each customer and target them with relevant products and services that would make sense to them.

Utilising insights for acquisition and retention

One of the biggest benefits of AI and machine learning in banking is the way it has lent its voice to the role played by big data and predictive analytics in the industry. Machine learning not only helps in improving engagement levels with existing customers, but it also helps in predicting the behaviour of customers based on certain set patterns or occurrences.

An interesting example is that of a European bank that altered its retention strategy because of an interesting insight it noticed. Their initial strategy ” which focused on targeting inactive customers ” failed to translate into anything significant. The bank then turned to machine learning algorithms to predict currently active customers that were likely to reduce business with the bank based on an analysis of their current transaction patterns. What came out of it was a targeted campaign that focused on high-risk but currently active customers, reducing future churn by 15 percent. 

One key to rule them all: Integrated services

Modern banking has evolved to cater to the needs of the digitally-aware and demanding customer. AI and machine learning act as the glue that allows banks to continually gather relevant data, glean insights, and offer meaningful, highly personalized experiences to their customers. The ability of the algorithm to continually learn and adapt based on every new interaction, additional data source, and analysis of past performance helps in more accurate and improved predictive models.

This clever integration of multiple data sources to curate a seamless customer experience is the future of AI and machine learning in banking. In fact, Bank of America’s soon to be launched bot ” Erica ” does just that. For instance, once the bot detects a drop in a customer’s credit rating, it recommends ways to bring the rating back up. This is done because of a partnership with Khan Academy, a provider of free online courses, which contains tomes of data on how to improve a falling credit rating. The algorithm sieves through the available information and offers only relevant parts as recommendations to the customer. Why go through the entire course when your friendly neighbourhood banking assistant bot can bring you exactly what you need?

The benefit of AI and machine learning in banking lies in the way it contributes to both parties. Predictions get smarter with each iteration. Overall costs go down as a result of better predictability, improving marketing efficiency and allow better targeting of products and services. From the customer’s perspective, this translates to a more personalized and seamless customer experience.

To say that AI and machine learning will disrupt the banking industry is passé because their role in the changing dynamics of the financial services industry is already evident. What remains to be seen is the extent and pace of adoption across public and private sector banks.

Quoting David Gerbino, Principal of @dmgconsulting,

Financial institutions that effectively leverage data and advanced analytics across the enterprise will be in a position to capitalize on newer technologies such as machine learning and automation. Those firms who fall behind will need to quickly overcome barriers that are preventing them from enjoying the benefits of advanced analytics or they will find themselves too far behind to catch up.