Disclaimer: The views expressed in this article are those of the author and do not necessarily reflect the views of the Economic Times – ET Edge Insights, its management, or its members


The financial services industry has arguably seen little innovation in recent decades — if you look at the top 100 R&D companies globally, not a single financial services company makes that list. Meanwhile, other regulated sectors like healthcare and education continue to break barriers. Even for the most established incumbents, a lack of innovation can quickly lead to a new, digital-native company surpassing customer acquisition or growth.

To accelerate innovation and transformation, organisations should be looking at the wider potential of data. Analytical approaches that are under utilised by organisations can actually yield tremendous value, such as data exploration (why did something happen), predictive modelling (what will happen), and prescriptive analytics (how can we make something happen).

Enabling this with a robust modern data architecture will become even more important as open banking increases options for consumers. Currently, there are multiple architecture options for efficiently storing, cleaning and analysing data. There are the data warehouse, the data lake and the data Lakehouse. Both data warehouse and data lake have their own strengths and weaknesses when it comes to what data can be stored and how the data can be analyzed. However, data Lakehouse marries the best of both architectures, emerging as the necessary data structure for organisations to draw out crucial insights.

Data and AI making an impact

Getting the right data architecture in place, such as a lakehouse, is the crucial first step for any organisation looking to reap the rewards of using data and AI. Following the initial set-up, here are key areas where data and AI can transform financial organisations for the better:

  1. Personalisation

Data and AI have a crucial role to play in helping to create a more personalised customer experience and help financial firms move away from product centricity towards customer centricity. Continuous intelligence – the marriage of event-driven decision making and historical context – ensures completely personalised interactions with customers based on the analysis of millions of unique data points every second from multiple sources. For example, real-time payment information is analysed in real-time against contextual data points to drive the customer experience. This is not about forgetting the products altogether, but innovating looking at customer insight first, so that products align with real-time behaviours and needs.

  1. Fraud detection

Fraud detection at scale is no easy feat, particularly as data volumes increase and online fraudsters switch-up deviant tactics to avoid detection. In fact, the impact of fraud is keenly felt close to home. Within the past year up till March 2021, the Reserve Bank of India revealed that 45,613 loan fraud cases were reported, which amounts to approximately Rs. 4.92 trillion.

Having data in one place will help with scale and visibility and will also provide an easy framework for sourcing out fraud at its roots. Organisations can build a fraud detection data pipeline to visualize the data in real time. This allows more flexibility than setting rules on how fraudsters behave and mapping this against a subset of data to detect possible fraud cases.

  1. Risk management

A modern, agile risk management practice is the way forward to managing and responding to market and economic volatility.  While historical data and aggregated risk models run the risk of obsoletion, data and AI enables the delivery of scalable, real-time insights allowing FSIs to address and resolve threats efficiently. Case in point, under an initiative known as Veritas, a 25-member consortium of leading banks and e-commerce giants have come together to evaluate their AI and data analytics-driven solutions against the principles of fairness, ethics, accountability, and transparency.

The future is open

An open, simple, and collaborative approach to data and AI will propel the financial services industry forward in many ways and accelerate innovation. It may be a heavily regulated industry, but the richness of customer data and its velocity brings many opportunities for positive change and disruption, all the while keeping customer convenience and security at the heart of business growth.

Authored by

Junta Nakai, Global Industry Leader, Financial Services and Sustainability at Databricks

Disclaimer: The views expressed in this article are those of the author and do not necessarily reflect the views of the Economic Times – ET Edge Insights, its management, or its members

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