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Data-as-a-Service: The next transformation for India's financial institutions

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DQINDIA Online
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iPaaS

It is not uncommon for financial institutions to struggle with advanced credit models today. With antiquated systems and restricted access to data, these institutions often must settle for inefficient analytical engines. It is no surprise that the dependence on traditional data, limited access to alternative data, rigid modelling and human error continue to limit growth, resulting in deficient credit decisioning; all these before regulations are even taken into account. 

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Data is power, and the freedom to innovate. With India's analytics market expected to reach INR 11,200.42 billion by 2027, financial institutions in the country will become dependent on more data to speed up innovation, deliver unparalleled customer experiences and build the resilience for an ever erratic credit landscape. 

While the Big Data Technology and Service Market in India has grown significantly in recent years with an expected CAGR of 35.1 percent from 2021 to 2026, this growth has been driven mostly by Banking, Financial Services and Insurance Solutions (BFSI) and retail, and will result in the country becoming one of the largest Analytics Market globally. With such opportunities abound, companies lagging in data access will quickly find themselves left in the dust.

Data-as-a-Service from One-Stop Marketplaces

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As data analysis and insights continue to shape consumption, DaaS marketplaces have emerged as platforms that consolidate many types of data and provide financial institutions with easy access to   fraud, credit, identity, open banking and many other alternative data sources. Access to new data - be it local, regional or global data, unlocks newer cross-border markets that were once limited to a few global players. 

By selecting a few specific data sources through a data cloud's single API, businesses can produce rich, customised datasets that enhance their ability to act on data-driven decisions. Data marketplaces will enable users to leverage new data sources almost instantly, and test that data across decisioning models quickly; without having to go through tedious systems integrations and maintenance and lightening the load on in-house development teams to focus on more important tasks.

With additional integration, the whole data process can be further automated, enabling the business to provide relevant offerings custom-made according to every customer need in seconds. This next-level approach to credit decisioning means more business opportunities, in more localities in shorter time. 

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These tools aren't just powerful but are a catalyst for companies to widen their product offerings, and at the same time, provide a superior customer experience. Data ecosystems also mean greater accuracy, an important factor, since only 18 percent of fintechs and financial services companies believe their current risk models are spot on. 

The ability to access more data from a DaaS marketplace means businesses are no longer stuck with antiquated credit models , and can start moving into new business segments and markets boldly. By developing a seamless, frictionless and automated decisioning process, a  business can deliver higher value, eliminate risk and become the disruptor instead of the disrupted.  By enabling smarter, faster decisions across customer lifecycles through a holistic risk ecosystem, brands can gain deeper insights, automate the optimisation of decisioning, and continuously retrain models for greater decision accuracy.

The article has been written by Varun Bhalla, General Manager, Provenir India

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