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While the digitisation in India – especially after the demonetisation in November 2016 – has allowed digital footprints of humans accessible from anywhere, the outbreak of the COVID-19 pandemic has accelerated its adoption across industries that were at a nascent stage in the country. With nationwide lockdown and social distancing norms, business sectors are increasingly leveraging the technological innovation to transform traditional sectors and the financial service sector is no longer an exception.
“The accelerated adoption of fintech services has disrupted all aspects of traditional industry of which digital lending is anticipated to a high-level disruption. The emergence of online lending platforms and fintech startups facilitating instant loan solutions and are evolving the loan disbursement processes. Furthermore, this has led to increased availability of data in different formats which makes it easier to analyse consumer insights. At the same time, moving to digital channels also poses multiple security risks such as frauds, ID-Theft, data hacks, incorrect risk evaluation and most important loan defaults,” says Ajay Chaurasia, Head of Product at RupeeRedee.
“Considering the pace at which digital lending solutions are growing, data security has become one of the biggest challenges for fintech companies,” he added.
Chaurasia explains how The fintech companies are leveraging the capabilities of data science to combat security issues through a multi-pronged approach.
Customer Onboarding
Given the competitive landscape, digital lending companies aim to provide an integrated omnichannel experience to users by hosting a myriad of services. To collect the data, it requires multiple data points like Aadhar, PAN, Banking, Utilities, E-commerce, GST, ITR, EPFO, Electricity, Credits, Debits, Liabilities, Savings and Assets that need to be made available online via different sources. When data is collected through multiple sources, managing digital identities becomes a major challenge for fintech players.
Cyber criminals in digital lending or fintech space get benefited from the misuse of digital identities. On the other hand, there is an amplified risk of fraud activities by sharing incorrect information regarding income and KYC details. Embedding security in the initial phase of gathering data and developing threat models can help in redesigning conventional security challenges. For instance, introducing stronger authentication ways such as biometrics, one-time passwords (OTP) and code-generating apps instead of conventional ways such as PINs and passwords.
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Risk Analysis
Digital lending companies can leverage data science to create stricter risk policies. These can be defined by lenders based on multiple other data sources as well including geographical and demographic characteristics, income group, gender, employment status, organisation type, language and many more. Many new-age fintech startups are using multiple alternate data points to understand their consumer behaviour for controlling false customer information and ensuring responsible lending.
Payment Collection
Payment collection has always been one of the biggest pain points for the lenders whether it is the Bank, NBFC’s or MFI’s. In the digital lending space, a digitally enabled collection system needs optimised customer interactions. This can be enabled by capitalising on the capabilities served by new-age Data Science. Today, it is helping lenders to do Predictive Analysis based on data available to them through different sources. Furthermore, it is helping lenders to understand the consumers’ repayment behaviour and the mode of the channel that would work for them.
In the last few years, the advancement in the Fintech sector has opened a multitude of avenues for payments. Fintech products such as Virtual Accounts, Wallets, UPI, Net Banking, UPI AutoPay, E-Nach, Debit Cards and many others have allowed lenders to reach out to customers and ensure payment collection at a reduced cost.
Takeaway
As the industry is continuing to evolve, there is an increased need for creating robust security systems. From digital lenders’ point of view, the redesigned security architectures must take market trends and other implications into the account. On the contrary, from customers’ perspective, data science should be used strategically to ensure privacy and data security to catalyse the adoption of digital lending solutions.
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