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    How advances in data science are securing digital lending



    The rapid adoption of fintech services has wreaked havoc on many parts of the traditional financial services business, with digital lending set to be a major disruptor. The rise of online lending platforms and fintech businesses is revolutionizing loan distribution procedures by providing rapid loan options. Furthermore, this has resulted in a greater availability of data in various formats, making consumer insights easier to analyze. Moving to digital channels, on the other hand, introduces a slew of security threats, including fraud, identity theft, data breaches, erroneous risk assessment, and, most importantly, loan defaults.

    Customer Onboarding

    Given the competitive environment, digital lending firms strive to give users with an integrated omnichannel experience by hosting a variety of services. Multiple data points, such as Aadhar, PAN, Banking, Utilities, E-commerce, GST, ITR, EPFO, Electricity, Credits, Debits, Liabilities, Savings, and Assets, must be made available online via various sources in order to gather the data. Managing digital identities becomes a huge difficulty for fintech companies when data is acquired from different sources.

    The abuse of digital identities benefits cyber criminals in the digital lending or finance area. By revealing false information about income and KYC details, on the other hand, there is a greater danger of fraud. Traditional security concerns may be redesigned by embedding security in the early stages of data collection and threat modeling. For example, instead of using PINs and passwords, better authentication methods such as biometrics, one-time passwords (OTP), and code-generating applications might be used.

    Risk Analysis

    Data science may be used by digital lending firms to build stronger risk regulations. Lenders can define these based on a variety of different data sources, such as regional and demographic factors, income group, gender, work status, organization type, language, and so on. For limiting fake client information and assuring responsible financing, several new-age fintech businesses are leveraging many different data sources to analyze their consumer behavior.

    Payment Collection

    Payment collection has always been a source of frustration for lenders, whether they are banks, NBFCs, or MFIs. A digitally enabled collection system in the digital lending arena requires optimized consumer interactions. This may be accomplished by utilizing the capabilities of new-age Data Science. It now assists lenders in doing Predictive Analysis using data obtained from various sources. It also aids lenders in gaining a better understanding of their customers' repayment habits and the best channel for them to use.

    The expansion of the Fintech industry in recent years has offered up a plethora of payment options. Virtual Accounts, Wallets, UPI, Net Banking, UPI AutoPay, E-Nach, Debit Cards, and other fintech technologies have made it easier for lenders to reach out to clients and assure payment collection at a lower cost.


    As the sector evolves, there is a greater requirement for effective security measures to be developed. Redesigned security architectures must take market developments and other repercussions into account, according to digital lenders. Data science, on the other hand, should be utilized strategically to secure privacy and data security in order to accelerate the adoption of digital lending solutions from the standpoint of clients.