AI in banking still has room for growth in Asia Pacific
The evolving banking landscape has pushed traditional banks to digitalize in order to cater to consumer demand for personalized and value-added services. This is especially prominent given the effects of the Covid-19 pandemic, which has driven a majority of the customer base towards digital services.
Whilst many emerging technologies exist, there have been variations in adoption rates by traditional banks around the world, with trends showing that they have been generally slower to adopt these technologies.
Typically, traditional banks have been rolling out their digitalization efforts conservatively, usually with a multi-channel approach. These include improving existing digital channels or ramping up efforts to launch independent digital banking businesses.
AI in banking around the world
A McKinsey report, AI in banking: Can banks meet the challenge?, identified four key outcomes that banks can achieve with the use of Artificial Intelligence (AI). They include higher profits, at-scale personalization, distinctive omnichannel experiences, and rapid innovation cycles.
McKinsey warns that banks that do not strategize their operations around AI will risk being overtaken by competition as they lose their customer base. Leading financial institutions are steadily incorporating a comprehensive approach to deploying AI across the front to the back office.
According to McKinsey, consumer preferences have shifted towards better personalization. As such, case studies of digital banking leaders have shown that they leveraged highly accurate predictive AI technologies to proposition customers with services they are very likely to take up. Additionally, AI allows all these to happen in a timely manner within an appropriate channel.
Nearly 60% of these leaders have implemented at least one AI capability, with the most common (36%) being robot process automation (RPA) for structured operational tasks. Interestingly, 32% of them have implemented virtual assistants or conversational interfaces for customer service, and 25% use machine learning (ML) for fraud detection, underwriting, and risk management operations.
According to the CEO of MovoCash and Forbes Finance Council Member Eric Solis, financial institutions are also using AI to make better investment decisions and manage customers’ wealth portfolios. Robo-advisors have been gaining popularity, and it is estimated that by 2022, these automated wealth advisors will be managing over US$ 4 trillion in consumer assets around the globe.
The digital banking landscape in APAC
Over in the Asia Pacific, the digital banking landscape is still in its early stages of growth, although with extremely promising prospects, especially in the Southeast Asian (SEA) region. Countries such as the Philippines, Indonesia, Malaysia and Singapore have all seen encouraging growth in the digital banking and fintech sectors.
The laggardly pace with which traditional banks have embraced digitalization and eschewed personalized services has led to a vast expense of banking real estate up for grabs by digital challenger banks (DCB). DCBs leverage digital innovation, easily entering emerging banking markets and disrupting mature ones.
An example would be KakaoBank, which managed to capture a large millennial customer base and report profits of over US$ 101 million in 2020, despite launching in a mature banking market.
They demonstrated success by leveraging their existing (massive) customer base from their popular KakaoTalk messaging app. KakaoBank captured their attention with attractive and well-designed mobile apps based on their UI and UX expertise with KakaoTalk and targeted this segment with highly personalized services marketing.
AI in banking within the APAC
According to the Boston Consulting Group, consumer preferences in SEA still largely hover around a need for personalized financial advice, lower banking fees, and more attractive imagery and aesthetics, most of which do not require front-facing AI services. Trends here also tend to point towards heavier use of AI in backend operations.
For example, China’s WeBank uses the ABCD (AI, blockchain, cloud computing, and big data) of technology, mostly in the back of office to optimize efficiencies and scale-up. Some applications of their AI are in a machine-learning-powered AI ecosystem, as well as marketing and asset and risk management. As the world’s largest digital bank, it generated US$ 570 million in profit in 2019 alone.
On the traditional bank side, Singapore’s UOB pushed out TMRW, an AI-powered mobile-only bank offering a full suite of solutions for the millennial market.
With positive and progressive regulator sentiments, a massive untapped market of the under and unbanked, conducive startup and fintech ecosystems, and the rapid digitalization of large swathes of populations, the SEA region is ripe for DCBs to step in and fill the massive financial services gap.
And ideally, digital banking players can implement AI in various ways to further optimize service offerings for consumers, not just now, but in anticipation of changing preferences down the road.