How Artificial Intelligence can revolutionise banks with predictive analytics

Ashwin Ramachandran, CEO, VuNet Systems

By Ashwin Ramachandran, CEO, VuNet Systems

The Covid-19 induced new normal has accelerated data and AI adoption manifold. Coupled with digital transformation and disruptions to traditional business models, you will see enterprises reimagining themselves, AI first. 

How banking and financial services embrace this change will determine any country’s economic success in a digital economy. Especially in India, with the fast-growing Fintech ecosystem, rise of digital payments paving way for financial inclusion and roaring success of UPI payments, customer first, digital first and AI first are going to be the key mantras for banks.

Interestingly, one of the earliest applications of AI in banking was in credit card fraud detection. Subsequently, slowly but steadily adoption of AI increased in domains like customer care chatbots, back office, lending, eKYC and more.

New era of AI-led payment transaction monitoring: 

The massive growth of online payments has provided a great opportunity for banks but resulted in its own set of challenges in ensuring a seamless customer experience for millions of transactions daily, which is no mean task. For the customer, a payment transaction is just a click away, but it spawns a complex web of interactions within the bank, across various applications and perimeters which includes acquirer, issuers and entities like NPCI/Master/Visa. In such scenarios, creating a seamless personalised, real time customer journey view and proactively monitoring transactions for failures is critical. AI powered predictive analytics help in detecting degradations in customer experience, proactively spotting unusual spikes, accelerating diagnosis and remediation. This not only helps the banks to improve operations productivity but also in improving customer experiences, thereby enhancing competitive advantage in a digitally charged banking ecosystem.

Extending to detect hidden patterns: 

There is also an increased usage of predictive analytics in sorting through vast troves of data and building 360-degree views for personalised product nudges to the customers. Additionally, flagging suspicious activity for compliance purposes is vastly enhanced with AI platforms. Penetration of AI is also deeper into cybersecurity to better understand patterns of zero-day attacks.

Further possibilities: 

Banks will also use AI powered algorithms to reimagine large parts of their business, enabling faster credit decisions and lending based on working capital, digital payment history, or even GST payments. Banks are also better served with AI-led decision-making frameworks, as they introduce newer services from API banking, partner-led banking and many more to attract and retain customers. 

Overcoming challenges: 

However, a wide implementation of a high-end technology like AI in banks is not going to be without challenges and is definitely not a plug and play with immediate returns. From the lack of credible data to varied customer preferences and fast changing ecosystems, both internal and external, there exists a number of challenges to be overcome.

For AI to unleash its magic, the primary challenge is going to be in the way banks architect their data and information architecture. The AI adoption maturity curve is accelerated, when the focus is not just the quantity of data sets but also its quality to extract useful insights. Moreover, the disparate functions of operations, fraud, BI, security, risk, customer services and more should now be seen as interconnected functions where data is shared in a ‘hub and spoke’ model. AI enables creation of such data-hubs, futuristically called ‘Systems of Intelligence’, instead of the traditional Systems of Records or Systems of Engagement silos. Banks need to invest in creating unified data sets, which are not just about petabytes of data, but meaningful, usable and contextualised data lakes. 

This will lead to AI enabled platforms breaking silos across operations, business and CXOs and bringing in more collaboration across teams. The meaningful insights generated leads to creativity and drives productivity by replacing hours of manual effort.

In summary, banks are only scratching the surface of the power of AI and predictive analytics and the years ahead will witness more powerful ways of putting AI to use and unlocking the potential of their unified data sets. The companies that reimagine their digital workflows – thinking customer first, breaking silos internally and accelerating adoption of AI platforms, coupled with unified and contextual data sets, will tide over this wave and come out much stronger.

Ashwin Ramachandran, CEO, VuNet Systems said, “VuNet’s AI-led product enables end-to-end transaction and payments monitoring, coupled with intelligent alerting systems to reduce failures, avoid outages, and improve customer experience. Monitoring at a scale of three

billion digital payments a month, VuNet is helping banks and payment gateways reimagine the future through their AI-led offering.”


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