Blog By: Arjun Ray Chaudhuri
I am live at Oracle Industry Connect and I just had the pleasure of sharing a demo with the attendees during Sonny Singh’s Keynote address around Machine Learning and applying it to the context of Next Best Offer. For those of you that were at Oracle OpenWorld back in September, you may recall we introduced Jenni to you. Jenny was applying for a car loan with her bank; she was able to apply and receive an offer to buy the car right on her mobile device.
Well now at Oracle Industry Connect, Jenny is back, her life fast forwarded 3 years and she is ready to pay off her car. Jenny is now posed with a situation – does she take the monthly amount she was paying into her car and buy a new car, or does she take that money and put it into something else. At the same time, her bank is evaluating the relationship with Jenny and realizes she’s about to pay off her loan.
Mobile banking remains Jenny’s preferred banking channel and the bank has been tracking Jenny’s activities over the mobile website and app and comparing them with her dynamic peer segments for many months. For the bank, it’s the right moment to pitch a marketing offer and deepen the relationship with Jenny given that one of the products is about to get closed.
After Jenny has made the last car payment on her bank’s app on her phone, a marketing communication is displayed asking Jenny if she’d like to open 529 plans for her children. But how did the bank know that 529 accounts were the right offer for Jenny? Why didn’t they make the offer earlier?
Thanks to Machine Learning, financial institutions are better armed to analyze vast amounts of data, be it every transaction level data or online activity data of the customer in the form of weblogs and applogs. With this capability, banks are able to service their customers through data driven marketing offers, before they’ve even had a chance to think about taking that car payment to another institution or to make some other purchase. The future is now, and organizations that sit back and wait to decide how to integrate these technologies into their operations will fall behind and start losing customers like Jenny. Just as the McKinsey article stated: “Now is the time to grapple with these issues, because the competitive significance of business models turbocharged by machine learning is poised to surge.”
We all know banks need to have strong and quality intelligence and this is needed for a variety of reasons: customer retention, cross sell/upsell, regulatory requirements, risk management and the list goes on and on. But how can machine learning take financial institutions to the next level?
Here is a list of benefits of applying machine learning on big data when compared to traditional statistical models:
According to the “Innovation in Retail Banking” report from Efma and Infosys Finacle, financial institutions understand the potential impact and benefits of AI, but that they are still hesitant to act. The hesitation comes from a number of reasons, with legacy technology environments coming in as the biggest hurdle to jump, and a lack of unified vision for digital across the enterprise coming in a close second.
The priorities are there – they know how to best leverage the technology if they had it. 78% of organizations say the Creating a customer-centric organization is a priority, while 74% say enhancing channels to give an omnichannel digital experience is key; and even 68% say maximizing usage of digital technologies such as mobile and social are important. And our example with Jenny leverages each of these.
So how can banks bridge the gap between priorities and barriers?? The trick is to recognizing the strategic business opportunities that exist here. The use of Machine Learning helps optimize customer experience for marketing personalization and engaging them with relevant offer recommendations by processing vast amounts of information more accurately. As with any organizational change or regulatory requirement, there are cost implications and change management to monitor, but the extended benefits outweigh the concerns.
You don’t want to be an organization that is behind in leveraging advanced technologies and lose customers along the way. I hope you join me next time as I continue with you on this journey of capitalizing on artificial intelligence.
Arjun Ray Chaudhuri is a Product Manager with Oracle Financial Services Analytical Applications and can be reached at arjun.ray.chaudhuri AT oracle.com