By Anant Deshpande, COO & co-founder, FinBox
One of the most notable aspects of India’s growth in the past decade has been the digitisation of the BFSI sector. It’s been fascinating to watch our nation of a billion-plus take to digital modes of financial services year after year. Take a look at digital lending, for instance: As per a report by EY, digital lending disbursals made up a mere 1% of all disbursals in FY17; fast forward to FY22, the percentage shot up to 12%. Our numbers are also making ripples globally: Last year, India secured its position at the top of the list of countries with the largest digital payment transactions, at an impressive 8.5 million. Furthermore, 46% of global real-time payments originate from India, underlining the country’s influence and prominence in shaping the future of digital financial ecosystems.
While several factors have contributed to this digital transformation—from
robust public digital infrastructure to high smartphone penetration—one cannot discount the role of big data.
Big data and its use in FinTech
Anyone with a digital footprint leaves a trail of data for businesses to use—it can range from
personal identifiable information (PII) to search engine and social media activity. This large
volume of information, structured and unstructured, is collected and analysed to make
accurate business decisions. As of 2021, 2.5 quintillion bytes of data were created every day.
The utilisation of this data extends beyond just marketing efforts. Companies employ data-
driven strategies in customer relationship management, supply chain optimisation, and risk
assessment. Like several other industries, financial institutions use big data to identify trends, patterns, and correlations to make predictions, build policies, and provide more personalised services. Take lending for instance. Big data analytics has been used across various credit products throughout the loan cycle. It is used to segment users, up-sell and cross-sell products, and assess borrowers’ creditworthiness by analysing their financial health (using consent-based alternate data), pre-approved loans, and more. Similarly, businesses across the finance sector leverage big data across each stage of their customer experience and beyond.
Here are a few ways that big data has contributed to the evolution of financial services in
recent years:
● Hyper-customer segmentation: The high volume of information can help
segment customers into very specific cohorts. The segmentation allows FinTechs to provide super-targeted products based on their behavior, needs, demographics, and other criteria. Data-driven segmentation ensures that the product caters to the right customers, enabling better distribution and reduction in customer acquisition and retention costs.
● Better customer experience: Big data can help financial service providers provide hyper-personalised products and better customer experience. It can also help remove redundancies in customer journeys, enabling better onboarding and a shorter application-to-conversion cycle. Here’s a simple example: lenders can use basic scrub data to attain details like name and mobile numbers which can later be used to auto-fill digital applications.
● Accurate decision making: Customer data can be used by FinTech to map their financial behavior too. This gives them a holistic view of their customers’ financial health and can be leveraged for several functionalities like cross-selling, up-selling, and even assessing risk. A common use-case of big data in lending, for example, is to assess the creditworthiness of a customer and determine their credit affordability to arrive at the loan amount and interest.
The synergy between FinTech and big data has already propelled significant transformations, streamlining internal processes within financial institutions and improving the customer experience. By analysing vast data sets, financial institutions have gained profound insights into customer behaviours, preferences, and patterns, allowing them to tailor their offerings with a previously unattainable precision.
Collecting more user data helps in making meaningful connections. As you collect more data, it’s important to talk to your customers about how you handle and store their information, and strengthen privacy and data protection rules. Establishing trust between a financial institution and its customers poses a complex challenge. The latter is expected to grant data access, while organisations face heightened scrutiny regarding their data practices and ethics.
Although data ethics alone isn’t the ultimate solution, adopting an ethical approach to personal data processing is vital to instilling confidence and encouraging customer engagement, increasing the uptake of modern credit products, and ultimately fostering financial inclusion.