Mobile banking has transformed the face of financial services, Kamal Brar writes in his article for finews.first. According to S&P market intelligence, by 2030 Asia-Pacific's cashless payments will make up 45 percent of the total global payments volume, valued at US$60.1 trillion.
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A big part of such accelerated change is attributed in part to the rise of artificial intelligence (AI) and machine learning in financial services, with digital-only banks offering innovative, personalized services like real-time budgeting tools and investment advice through their apps. Today, financial organizations are racing to bring newfound capabilities to online customers.
Large language models (LLMs) are now evolving to develop specialized models tailored for specific tasks, especially for business enterprise applications. In Singapore, MAS has recently announced support of up to S$100 million in investment to boost financial institutions’ AI capabilities, intending to deploy AI models for high-impact use cases, governance and risk management. Mobile banking is set to play a larger role in helping people manage their money, becoming a key part of our daily lives.
«This New Approach Allows Banking to Go Beyond Transactions»
The implementation of AI combines both personal data and functionality to deliver automated, secure, personalized banking experiences. It can take this convenience a step further too, by analyzing consumer behavior to shape their services. For example, transactions can be automatically categorized into spending «pots», or savings goals can be suggested based on a user’s spending habits.
This new approach allows banking to go beyond transactions. It also provides a seamless, bespoke banking experience that gives customers control and confidence in their digital wallets.
How is this possible? Typically, user data would be gathered in batches. Large amounts of data are collected and stored for analysis to inform banks to plan the next phase of engagement. While this approach still works for less time-sensitive data, crucial spending information will become outdated as soon as it is stored.
Real-time data streaming allows for near-instant analysis of every transaction within milliseconds, giving users timely insights to make smarter financial decisions and helping banks create more relevant and accurate recommendations.
«This Lateral Exchange of Insights Helped Drive Innovation»
Trust Bank, which sought to build a secure digital bank to power real-time experiences for customers, employed Confluent’s data streaming platform for its event-driven architecture. Teams were enabled to produce, share, and consume self-service data products across functions. For instance, Trust Bank’s marketing team could subscribe to existing customer data generated by their fellow teams to build customisable notifications.
This lateral exchange of insights helped drive innovation, reduce cost of ownership and optimised security and quality – all which bolsters smarter growth for a digital-first organisation.
«Customers Are Now More Protective»
The next step is to further enhance personalization efforts for customer experience and services. Where this aims to build trust and elevate convenience, it is easier said than done.
With personal data highly protected by laws like Singapore’s Personal Data Protection Act (PDPA), this presents a challenge. Customers are now more protective when it comes to managing access to their personal data, recognizing potential security risks and the intrinsic value their data holds for businesses.
To address this, banks should rely on first-party data, where users give a company permission to use their data. This «opt-in» approach means that consumers make a conscious decision to share their personal data at their own discretion.
Giving users control over the amount of data they are comfortable sharing, will increase trust in mobile banking. Data settings will adapt to each unique user without compromising security or experience.
«Workers Should Be Well-Equipped With The Right Tools»
While mobile banking handles everyday transactions with ease, in-person services still offer valuable, interactive touchpoints between banks and their users.
When it comes to complex financial decisions like loans or investments, a human element is often essential, especially for those with limited digital skills or internet access.
Using these opportunities to build trust through face-to-face sessions, workers should be well-equipped with the right tools to provide better, more personalized customer support. With AI, this can mean summarising financial documents, outlining personalized suggestions for financial management and more – cumulative efforts that are invaluable to long-term relationship building.
«Banks Can Offer Tailored Services That Truly Meet Individual Needs»
As mobile banking evolves from a tool for managing transactions to offering valuable financial insights, it will play a bigger role in helping people navigate their finances. By leveraging AI and machine learning to maximize the value of shared data, banks can offer tailored products and services that truly meet individual needs.
The power of flexible data use, combined with AI models that facilitate real-time data processing, empowers banks to level up customer experiences that builds confidence and reliability – an essential value asset that ultimately stands the test of time.
As Senior Vice President, Asia Pacific and Japan, Kamal Brar is responsible for growing the Confluent business and expanding the company’s customer momentum across the Asia Pacific and Japan region. He holds a Bachelor of Computing & Information Systems from Macquarie University, Sydney and a Master’s in Technology Management from the University of New South Wales. He has held various leadership positions at Oracle, IBM, Hewlett-Packard, MySQL, MongoDB, Talend, Hortonworks and most recently Rubrik. He is an entrepreneurial leader having successfully led some of the most successful disruptive technology companies across Asia Pacific & Middle East.
