While digitization in India – especially after demonetization in November 2016 – has made digital fingerprints of humans accessible from anywhere, the outbreak of the COVID-19 pandemic has accelerated its adoption in industries that were at an incipient stage in the country. With nationwide lockdown and social distancing standards, industries are increasingly leveraging technological innovation to transform traditional industries, and the financial services industry is no longer an exception.

“The accelerated adoption of fintech services has disrupted all aspects of the traditional industry whose digital lending is expected to cause high level disruption. The emergence of online lending platforms and fintech startups are facilitating instant lending solutions and changing loan disbursement processes. In addition, this has led to increased availability of data in different formats, which makes it easier to analyze consumer information. At the same time, the switch to digital channels also poses multiple security risks such as fraud, identity theft, data hacking, incorrect risk assessment and the most important payment defaults, â€says Ajay Chaurasia, product manager at RupeeRedee.

“With 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 fintech companies are leveraging the capabilities of data science to tackle security challenges through a multi-pronged approach.

Customer onboarding

Given the competitive landscape, digital lending companies aim to deliver an integrated omnichannel experience to users by hosting a myriad of services. To collect the data, several data points are needed like Aadhar, PAN, Banking, Utilities, E-commerce, GST, ITR, EPFO, Electricity, Credits, Debits, Liabilities, Savings and Assets which must be made available online via different sources. When data is collected from multiple sources, digital identity management becomes a major issue for fintech players.

Cybercriminals in the digital lending or fintech arenas profit 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. Integrating security into the initial phase of data collection and threat model development can help redesign conventional security challenges. For example, the introduction of stronger authentication means such as biometrics, one-time passwords (OTP) and code-generating applications instead of conventional means such as PIN codes and passwords.

Risk analysis

Digital lending companies can leverage data science to create tighter risk policies. These can be defined by lenders on the basis of several other data sources, including geographic and demographic characteristics, income group, gender, employment status, type of organization, language and many others. Many new-age fintech startups are using multiple alternative data points to understand their consumer behavior in order to control fake customer information and secure responsible lending.

Collection of payments

Collecting payments has always been one of the biggest issues for lenders, be it the bank, the NBFC or the MFI. In the digital lending space, a digitally activated collection system needs optimized customer interactions. This can be made possible by capitalizing on the capabilities served by the data science of the new era. Today, it helps lenders perform predictive analytics based on the data they have available through different sources. Plus, it helps lenders understand consumer repayment behavior and what channel mode would work for them.

In recent years, the advancement of the FinTech sector has opened up a multitude of payment avenues. Fintech products such as Virtual Accounts, Wallets, UPI, Net Banking, UPI AutoPay, E-Nach, Debit Cards and many more have enabled lenders to reach customers and secure payment collection at a reduced cost.

To take with

As the industry continues to evolve, there is an increased need to create robust security systems. From the perspective of digital lenders, redesigned security architectures must take into account market trends and other implications. On the contrary, from the customers’ perspective, data science must be used strategically to ensure data privacy and security in order to catalyze the adoption of digital lending solutions.