Chart the Way Forward: Why Banks Need AI & Machine Learning Today

Prashant Shukla, Director, Oracle ASEAN

Prashant Shukla, Director, Oracle ASEAN

Everywhere you Look in Banking Change is on the Horizon

New and emerging technologies are driving digital disruption, which in turn is bringing fresh competition and global trends. At the macro level, the rise of FinTech and the call for open banking are challenging the industry to provide new services to customers to remain in the game. Internally, the need for speed and shift to mobile is pushing banks to adopt agile IT ecosystems that can be quickly updated and enhanced, so they can deliver new initiatives in weeks instead of months or years. All of this is upsetting the status quo and challenging ASEAN banks to innovate, and fast.

Customers now expect their financial services providers to offer more than transactional services; they want banks to provide new and better solutions to help manage financial matters. To meet these new customer expectations, banks have a new mission: to move from being product centric to really understanding the consumers of their services and meeting their needs. To do so, they need unprecedented insights and predictive analysis powered by the latest technologies so that C-level executives can spend more time driving growth.

But how?

How Banks can Use Analytics to Acquire, Engage and Retain Customers

Until recently, customer acquisition, engagement and retention strategies were mostly built based on historical information or gut instinct. Now, more and more companies are realising that data can be harnessed to personalize marketing efforts tailored to customers’ interests, adjust product strategy based on usage patterns and pre-emptively predict which customers are likely to leave.

The challenge for banks is that customers are generating massive amounts of data like purchase history, profile data, browsing history, product usage patterns and social media behaviour every single day.

This is where AI and machine learning come in. As these data sets stress what is possible within human capabilities, these new technologies can amplify the results achieved from the data sets–identifying when behaviours change, and detecting delicate shifts in the underlying data, and then revising algorithms accordingly, considerably improving predictability.

In particular, data analytics technologies like Big Data, Machine Learning and AI, banks can help in several key areas:

Customer Acquisition–An Analytics Goldmine. Acquiring customers is a top priority for banks; however, it is getting more and more difficult as banking has become a commodity business. Banks need to differentiate themselves not only on the offerings but also on the services side. A key way of doing this is through customer intimacy which requires knowing your target customers better than the competition so that you can deliver a superior customer experience and in turn lead to an increased wallet share.

Analytics plays a massive role in solving this problem by integrating data from both online and offline channels to provide a unified view of the customer. This allows, for example, call center agents to study the customers’ likes, dislikes, sentiments and behaviour pattern in order to define the best fit for the prospective customer, and offer personalized services.

• Customer Engagement–A Path to Profitability. By looking inward to analyze customer usage data, companies can fine-tune product strategies and quickly improve products to keep customers engaged and satisfied. This is quite a change historically, when product management teams spent much of their time managing reporting processes, rather than understanding how end-users interact with the product. By being able to quickly correlate changes in user traffic patterns and perform cohort analyses in the context of events these teams can increase value by accelerating new product models, and drive revenues and engagement from cross-sell and up-sell strategies.

• Customer Retention–Reduce Customer Churn with Behavioural Analytics. Big Data analysis can also help mitigate attrition and help retain customers who may be on the brink of leaving. By tracking specific behaviours leading up to a transfer or withdrawal, such as if a customer called in for information with an outside financial consultant on the line, a change in employment or power of attorney. Additionally, by correlating this data, they can determine the statistical relevance of each activity or combination of activities that resulted in a withdrawal or transfer.

They can then pre-emptively identify and engage with the customer to address their outstanding concerns or needs, or offer relevant promotions, based on the insights gained. As a result, companies can significantly increase the percentage of funds they retain that otherwise would have been transferred to a competitor.

Cloud is Key in the Adoption of Big Data, Machine Learning & AI

To realise these benefits, many banks are turning to integrated data in the cloud to help power advanced analytics and predictive forecasting, giving them the ability to focus on strategies that drive new product development and growth initiatives, rather than the IT.

Check out:  Top Lending Management Solution Providers - 2018

Big data mining becomes more efficient when integrated data is stored in the cloud, which enables finance to partner with line-of-business managers on other growth programmes, such as personalized marketing campaigns that serve content to customers about relevant products and financial services. And with data aggregated in the cloud, artificial intelligence and machine learning can identify trends faster and support predictive forecasting.

And while some banks feel they are being held back from adopting cloud technologies because of a perceived lack of clarity on regulations and data sovereignty requirements, there are interim solutions available as cloud adoption becomes an industry norm. For example, Cloud at Customer, a pioneering approach by Oracle, provides all the benefits of a private cloud, while at the same time being sited behind the bank’s firewall.

It is evident that Big Data, Machine Learning and AI are here to stay, and the banking industry is an early adopter. This trend is expected to propagate exponentially in the future. However, it is important for organizations to establish a clear vision and strategy, and embrace this trend to be winners over the next ten years.

Weekly Brief

Read Also

Defining Strategies for a Better Tomorrow

Defining Strategies for a Better Tomorrow

Ken Nagel, Executive Vice President & Chief Information Officer, Umpqua Bank
How Insurance Solutions Can Help Protect Tech Companies as They Grow

How Insurance Solutions Can Help Protect Tech Companies as They Grow

Andrew Zarkowsky, Head of Technology Industry Practice, The Hartford Nick Kreczko, AVP, Hartford Stag Ventures and Dan Neubelt, Senior Analyst, Hartford Stag Ventures
From Analogue To Digital

From Analogue To Digital

Soren Rode Jain Andreasen, Chief Digital Officer, Danske Bank Group
6 Lessons from the Marvel Cinematic Universe Relevant to Mobile Banking

6 Lessons from the Marvel Cinematic Universe Relevant to Mobile Banking

Thomas P. Novak, VP/Chief Digital Officer, Visions Federal Credit Union
Business-to-Business Payments at a Crossroad

Business-to-Business Payments at a Crossroad

Guy R. Berg, Vice President Payments, Standards and Outreach Group at Federal Reserve Bank of Minneapolis