Enhancing customer experience and improving operations is more crucial than ever amid the winds of change in the banking sector. But what are some of the challenges that banks face, and what role can data and AI play to help them tackle these barriers and pull ahead of both digital banks and traditional competitors?
Challenges for banks in APAC
Speaking as a guest speaker at the “Cresting the Digital Innovation Wave in Banking with Data and AI” virtual workshop by IBM and CDOTrends, Elvie Lahournere, the Asia Pacific (APAC) digital and innovation director for Natixis Bank pointed to the rising expectations of customers as one challenge banks here face.
In swathes of Asia and metropolitan cities such as Hong Kong and Singapore, she noted that the customer experience is now “mostly mobile” and heavily personalized. As the digital experience bar gets ratcheted upwards, rising expectations mean that users are now getting impatient with even the smallest delay, culminating with customer loyalty that is now “very difficult” to capture and retain, she said.
Competition is increasing in the digital realm, too. Drawing from her five years living and working in Beijing, Lahournere pointed to WeChat’s meteorite rise from an instant messenger at the very beginning to the super app that it is now. She noted that by 2018 when she left for Hong Kong to take up her current role, WeChat was servicing as many as one million payment transactions per minute.
“There is increasing pressure from startups in FinTech, and they can eventually affect not just retail banking, but also commercial banking,” she said. “They might be small players at the start, but they have the potential to grow into ‘big tech’ giants that we cannot ignore. And they are putting extreme pressure on us – as they are often digital-native, cloud-first, and heavily data-driven.”
Pace of change speeding up
In his keynote, Kitman Cheung, chief technology officer for Data, AI, and Automation at IBM APAC, noted that the pace of change is speeding up. Using the Standard and Poor’s 500 (S&P 500) stock market index as a gauge, Cheung observed that the average lifespan of a company on the index had fallen from 61 years as measured in 1958, to an average of just 18 years now. “That is the kind of disruption and the kind of things that we need to be looking at,” he said.
“What it is showing us is that the lifespans [of companies] tend to expand and contract because there were major disturbances in the marketplace that drove changes in the industry – across all industries. And as it drives those changes, you see the average lifespan fluctuating in tandem. But overall, company lifespans getting shorter because the disruptive forces are coming fast and furious,” said Cheung.
“At IBM, we believe that we are all data companies now. The hypothesis we are putting forward is that every company is becoming data-centric, and it is no longer an option to do anything else. I would say that a bank is no longer solely about keeping money. To be a successful bank is to be a successful data processing company.”
Harnessing data and AI
This data-centric reality puts data and AI at the forefront of innovation and transformation, says Cheung. To crest the innovation wave, the onus is on companies to leverage data and AI to deliver the outcomes that they want.
“CEOs all over the world are saying the same thing today: we need to go after better decision making, we need to leverage AI to help our company make smarter decisions, be it risk or growth or any kind of analysis that they would like to do.”
“More importantly, successful companies tend to engage their customer by sharing insight about their customers as well, offering wealth advice, offering insight as to what they can do differently to achieve their financial goals. Just as important as providing, you know, investment risk analyses to your investment analysts. Sharing that data in a broader sense and leveraging it to engage customers on a just-in-time basis is becoming the cornerstone of banking success.”
The solution resides in a collaborative data environment to bring multiple disciplines together, allowing data scientists and business analysts to work hand in hand with the rest of the company, says Cheung. Moreover, it entails leveraging the cloud to overcome scalability limitations and bringing everything together with the requisite AI capabilities and strong data governance.
Getting everyone on board
But while the way forward lies in harnessing technologies such as scalable AI and data solutions, both Lahournere and Cheung acknowledged that getting people and processes right is just as important. Drawing from her experiences from when she worked as a consultant, Lahournere stressed that people, culture, and processes must evolve – and that everyone must be involved.
“That means having everybody on board. When I say everybody, I mean everybody from top management to entry-level employees. They must understand the power of data, understand the difference between data visualization and analysis, machine learning, and so on. To understand why the cloud is now a new way of working,” she summed up.
Read more about how you can intelligently automate your data and AI in IBM Cloud Pak for Data eBook, or catch up with materials on AI and data on our event page under the “Related Content” section. A full event recording can also be found here.
Paul Mah is the editor of DSAITrends. A former system administrator, programmer, and IT lecturer, he enjoys writing both code and prose. You can reach him at [email protected].
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