AI in banking still has room for growth in Asia-Pacific
The changing banking landscape has pushed traditional banks to digitize in order to meet consumer demand for personalized and value-added services. This is particularly important given the effects of the Covid-19 pandemic, which has pushed a majority of customers to digital services.
While many emerging technologies exist, there have been variations in the adoption rates by traditional banks around the world, with trends showing that they have generally been slower to adopt these technologies.
In general, traditional banks have taken their digitization efforts in a cautious manner, usually with a multi-channel approach. These include improving existing digital channels or stepping up efforts to launch independent digital banking businesses.
AI in banks around the world
A McKinsey report, AI in banking: can banks rise to the challenge?, identified four key results that banks can achieve with the use of artificial intelligence (AI). They include higher profits, large-scale personalization, distinctive omnichannel experiences, and rapid innovation cycles.
McKinsey warns that banks that don’t strategize around their AI operations risk being overwhelmed by the competition by losing their customer base. Leading financial institutions regularly integrate a comprehensive approach to deploying AI from front to back office.
According to McKinsey, consumer preferences have shifted towards better personalization. For example, case studies of digital banking leaders have shown that they are using highly accurate predictive AI technologies to deliver services to customers that they are very likely to adopt. Plus, AI allows all of this to happen in a timely manner in an appropriate channel.
Almost 60% of these leaders have implemented at least one AI capability, the most common (36%) being robotic process automation (RPA) for structured operational tasks. Interestingly, 32% of them have virtual assistants or conversational interfaces in place for customer service, and 25% use machine learning (ML) to fraud detection, underwriting and risk management operations.
According to MovoCash CEO and Forbes Financial Board member Eric Solis, financial institutions are also using AI to make better investment decisions and manage clients’ wealth portfolios. Robo-advisers are growing in popularity and it is estimated that by 2022, these automated wealth advisers will manage more than $ 4 trillion in consumer assets globally.
The digital banking landscape in APAC
In Asia-Pacific, the digital banking landscape is still in its early stages of growth, but with extremely promising prospects, particularly in the South East Asia (SEA) region. Countries like the Philippines, Indonesia, Malaysia and Singapore have all experienced encouraging growth in the digital banking and fintech sectors.
The slowness with which traditional banks have embraced digitization and avoided personalized services has resulted in a huge outlay in bank real estate for the digital challenger banks (DCBs) to seize. DCBs leverage digital innovation, easily penetrating emerging banking markets and disrupting mature markets.
An example would be KakaoBank, which has managed to capture a large, millennial customer base and record profits of over US $ 101 million in 2020, despite launching into a mature banking market.
They have demonstrated their success by leveraging their existing (massive) customer base from their popular KakaoTalk messaging app. KakaoBank grabbed 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 service marketing.
AI in banking within APAC
According to Boston Focus Group, consumer preferences in SEA still largely revolve around a need for personalized financial advice, lower bank fees, and more attractive images and aesthetics, most of which do not require front-end AI services. Trends here also tend to indicate more intensive use of AI in backend operations.
For example, the Chinese WeBank uses ABCD (AI, blockchain, cloud computing and big data) technology, mainly in the back office to optimize efficiency and scale. Some applications of their AI are found in an AI ecosystem based on machine learning, as well as in marketing and asset and risk management. As the world’s largest digital bank, it generated $ 570 million in profit in 2019 alone.
On the traditional banking side, Singapore’s UOB released TMRW, an AI-powered, mobile-only bank offering a full suite of solutions for the millennial market.
With positive and progressive sentiments from regulators, a massive untapped market of under and unbanked startup and financial tech ecosystems, and the rapid digitization of large swathes of populations, the SEA region is ripe for DCBs to step in and fill the gap. ‘huge deficit in financial services.
And ideally, digital banking players can implement AI in a number of ways to further optimize service offerings for consumers, not just now, but in anticipation of changing preferences in the future.