If there's one lesson to come from the COVID-19 pandemic, it is the importance of resilience.
With so much demanded of businesses around the world to stay afloat, it's clear that after six months of COVID-19, the companies with concrete business continuity plans in place will be the ones better off in a post-pandemic world.
Before the upheaval began early this year, the banking sector in Australia and New Zealand was comparatively strong, and a migration from legacy to digital systems, while not wholly discounted, was not high on the list of priorities of a small minority.
Then social distancing and lockdowns were mandated, and many found themselves wishing they had invested in digital transformation earlier. No one could have foreseen a worldwide pandemic, but those that relied on legacy systems in early 2020 found themselves in deep water – the result of a lack of willingness to digitally transform.
It's now clear that digital transformation is a necessity for banks that haven't already in order to ensure business adaptability and continuity. This can come in the form of migrating operations to the cloud, embracing AI-enabled business services, and various other kinds of fintech.
One of the most important ways of increasing adaptability and staying power in adverse conditions is business model innovation.
With the bar to success set so much higher amid lockdowns, innovation is almost essential, and one of the most straightforward paths to innovating a business model is through AI.
The industry is still struggling through shrinking foot traffic and closing branches; in April, ANZ bank closed 58 of its branches, Westpac had closed 30, and by May, Commonwealth Bank had closed 114 of its branches.
Given this, AI will have an even more significant role to play in maintaining the quality of experience on these channels.
Face-to-face interactions between relationship managers and customers will dwindle, but in its place, video calls and chatbots armed with useful tools and insights will promote better quality engagement.
AI will also help banks distil their unnecessarily long product lists into a smaller range of reimagined, risk-managed offerings. By making products more relevant to the post-COVID-19 scenario, and simplifying associated processes like loan applications and customer service, banks will end up doing the same amount of business, if not more, with fewer products.
It's also essential to have an AI infrastructure open to change, due both to the current volatile market environment and in anticipation of new market and regulatory needs.
This was especially evident in Australia when early this year the Big Four banks (Commonwealth Bank, Westpac, ANZ and NAB) collectively lost half a billion dollars' worth of savings deposits to neobanks, such as Xinja, Judo Bank and 86 400, which were promising interest rates that were several times that offered by incumbents.
It's part of a growing trend in which traditional banks and financial institutions are lagging when it comes to digital transformation. In fact, a recent report from Forrester revealed that this attitude could hit banks hard in the future.
According to the report: "Leading corporate banks outpace their retail banking peers in using emerging technologies and embed their products and services in the daily lives of their corporate clients, yet very few corporate banks think that they have the right applications and architecture in place to best serve their customers — and their employees.
"Application development - delivery teams need to identify state-of-the-art digital banking processing platforms that will help their bank survive in the fast-paced world of open digital banking," the report said.
Flexibility and configurability are vital ingredients to accommodate for future applications and systems, especially AI. To further support change at speed and scale, AI must also be parameterised.
This can be achieved with the implementation of SaaS platforms, like Infosys Finacle's Digital Banking Suite. It's an easy-to-deploy solution designed to service the needs of financial institutions, combining experience and comprehensive functional spread.
It leverages liquidity management and payments software, as well as guidance on trade finance, retail loan origination and customer relationship management (CRM).
It's also widely held within the industry that AI should aid banks in managing business risks. This would be especially helpful now, given the massive number of business failures and job losses during the coronavirus crisis, especially when it comes to solvency.
It's not all doom and gloom, though; AI can also launch the financial sector into the future by pioneering comprehensive banking automation.
Machine learning, deep learning, chatbots, robotic process automation and advanced insights will deliver fully digital banking, including digital onboarding, digital transacting, digital delivery, and even digital closure.
And digitisation will not be limited to financial transactions alone; even routine queries that were previously handled in a branch or call centre will henceforth be attended to by a chatbot with complete context and traceability.
Banks will witness a broader, more sophisticated application of AI in typical use cases, in the future. Here are five of the most anticipated use cases:
ML for enhanced security
As digital transactions and consequently, cyber-risks increase, AI will be used to enhance banking security to new levels. (Think voice-based customer authentication using machine learning solutions that can detect voice variations with time of day.) In corporate banking, voice may be used to authorise bank guarantees, letters of credit etc.
Deep learning to enhance fraud prevention
Deep learning will support rapid risk scoring to ensure genuine transactions are not rejected. The solution will use transaction data of the past 5 to 10 years to respond in milliseconds.
Robotic process automation (RPA)
With human resources and budgetary resources drying up in the pandemic, banks will be forced to automate to stay afloat. Soon, RPA will be deployed extensively across the front, middle and back-office, and at all touchpoints.
Hyper localisation on the IoT
By connecting with IoT devices, AI will enable banks to ramp up product sales locally; using information about preferences and location; it will present hyper-localised, hyper-personalised offers to customers.
Bots on unassisted channels
Intelligent chatbots will simulate human-to-human interactions by employing 'regular' conversation and even urban slang to ease the migration to unassisted banking channels.
