Over the years, we have become increasingly dependent on technological advancements. Almost all aspects of our lives - the way we communicate, the way we shop, and the way we bank are all impacted by technological advances. Innovations are underway to improve standards and build consumer friendly products. Take for example the mobile phone industry which has evolved significantly over last 10 years and has come up with innovative ways to make communication simpler, by creating products like WhatsApp, real time video calls, games etc.
Similarly, Artificial Intelligence (AI) is a technological advancement that is making an impact on the banking industry. Many organizations are already exploring ways to remain ahead of the competition and increase their standing as innovative enterprises. If banks are to remain competitive they need to take into account consumer lifestyle and behavioral changes that are defining today’s value propositions and design around artificial intelligence to deliver relevant services.
According to Efma, “AI presents a huge number of opportunities for the banking industry, who, when able to exploit their growing data repositories, can better meet regulations, increase their bottom line, improve customer experience and more.”
In broader terms, artificial intelligence is the ability of a machine to act like a human being by gathering facts about a situation through sensors and comparing them with stored data and making decisions based on what it signifies. Voice and visual recognition followed by machine learning are the most commonly used AI solutions across industries. Movie recommendations by Netflix, product recommendations by e-commerce retailers like Amazon, Alibaba and Facebook's ability to spot our friends faces - are all early examples of machine learning. And Google's self-driving car is becoming a classic case study. A self-driven car is not programmed to drive, it learns by driving millions of miles on its own and observing how people drive.
Machine learning can be useful especially in cases involving large dynamic data sets, such as those which track consumer behavior. When behaviors change, it can detect delicate shifts in the underlying data, and then revise algorithms accordingly. Machine learning can even identify data variances and treat them as directed, considerably improving predictability. These exclusive capabilities make it significant for a broad range of banking applications. Banks can use machine learning across the front, middle and back office, in functions ranging from customer service to sales and marketing to fraud detection to securities settlement. They can identify or even prevent fraud by deploying machine learning to look into patterns in payment transaction data like for credit cards to spot anomalies or inconsistencies, in middle office functions.
There are many other areas in the banking ecosystem where machine learning can add substantial value:
Creating New Sales Opportunities – It opens doors for cross selling and up selling to by developing deeper insights from evaluation of customers’ needs and usage patterns. Organizations using machine learning can increase existing customer revenue by 10-15%.
Not Letting Customer Go - As machine learning monitors customer behavior deeply, it can forecast if there are any risks of losing a customer and can enable banks to quickly act on retaining them. Media sentiments, demographics and site behaviors play a very important in predicting such activities. Organizations that use machine learning can reduce customer attrition by around 25%.
Automating Customer Services – Cognitive machine learning helps organizations automate their customer service centre and lower servicing cost, enhance system performance, improve customer experience, enable faster responses and reduce risks. Today there are multiple virtual assistants available that use deep learning and natural language processing to understand and interact the way humans do.
Reducing Bad Debt – Machine learning can build dynamic models which can segment delinquent borrowers and identify self-cure customers enabling organizations have better collection practices. Organizations using machine learning have reduced their bad debts by 35 – 40 % already.
Reducing Price Leakage – Machine learning can certainly help in eliminating price leakage and billing errors by applying advanced analytics. Improved customer segmentation and appropriate pricing models can help organizations achieve 10 – 15% more revenue.
Technology is transforming the way consumers behave and as seen above it is most evident in the banking industry. Machine Learning can revolutionize payment operations by creating insights from large datasets and forming various patterns, correlations and provide informed decisions in real - time. Reduced operational cost, improved compliance and better productivity which in turn leads to higher revenue are few benefits that banks can derive out of machine learning.
Despite these benefits, organizations do not exhibit complete trust in machine learning. It must be noted that 60 years ago when calculators where introduced they too were not trusted. And in some cases when organizations do take up machine learning, they expect instant ROI. This however, is not realistic, you need the right talent, right tools to develop the right approach and harness the potential of machine learning.
It is evident that machine learning is here to stay, and is impacting a large number of industries, and the banking industry 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.
My colleague Parul Jain and I co-authored this blog. We would love to hear your views. We are reachable at tushar dot chitra at Oracle dot com and parul dot jain at Oracle dot com