In today’s rapidly evolving business landscape, staying ahead of the curve is not just an aspiration; it’s a necessity. One of the most effective ways to achieve this is through data-driven decision-making. Data analytics helps in identifying revenue opportunities, optimising pricing, and making informed decisions for overall business growth.
Over the last few decades, the data systems around the MSMEs are evolving at a very fast pace. India stack, GST, Account Aggregator, data availability on supply chain, payments, e-commerce, etc. have increased data and digital footprints for MSMEs. Data systems have begun to play a pivotal role and have changed the way lenders view MSME customers.
Data can be effectively used for multiple business decision-making – geography identification, customer profiling, credit assessment, portfolio remedial measures, and risk management.
Geographical data – like catchment area data, lending potentials, portfolio, and number of borrowers in an area – available today across different data sets have been key decision-making factors in opening a new location or branch. In other words, if you want to open up a new branch, the analysis of data points from the bureau and the potential of the new location is studied and accordingly, a new geographical location is decided.
Customer identification has become more precise, not only on the back of the plethora of data sets available, but also on the evolved technology stacks and various channels like WhatsApp, IVRs, InApp, and the ability to precisely target these customers through multiple social media channels. Geographical data combined with specific targeting has ensured that lenders are able to reach the right customers, thereby reducing their cost of acquisition and subsequent wastage. Moreover, with these data sets, multiple propensity models can be built to derive the off-take of loans of the targeted customers. These can be used to further zero-down on customers who will have a high probability of picking up loans based on past loan behaviours, thereby ensuring sharp targeting and reduced cost of acquisition.
By harnessing the power of data analytics, we can scrutinise a myriad of factors such as credit history, payment patterns, and economic trends to assess the creditworthiness of applicants.
Data has also been playing a very important role in credit-assessing customers. Multiple data footprints are available through the bureau, banking, AA, GST, payments and e-commerce ecosystems, and other supply chain systems, which have enabled lenders to walk away from collateral-backed traditional methods towards cashflow-based lending. This ensures that not only is the right customer selected, but the right credit is delivered.
The ability to filter and process the right customers coming through the door and to assess them instantly and digitally based on their data footprints has also enabled lenders to make faster go/no-go decisions, processing the most probable creditworthy set in a focused manner. This has brought in operational efficiencies across multiple functions, thereby optimising OPEX. Operational expenditure (OPEX) is a significant concern for any financial institution. However, data-driven decision-making can help mitigate this challenge. By leveraging data analytics to streamline various operational processes, we can significantly reduce OPEX.
For instance, automated underwriting systems can efficiently assess loan applications, thereby reducing the need for manual intervention. Additionally, predictive maintenance models can optimise the maintenance of assets. The result is a leaner and more cost-effective operation, directly contributing to higher profitability. Moreover, not only does it reduce the margin for human error, but it also frees up human resources to focus on more value-added tasks.
Another significant aspect is that different models built on early-warning signals help lenders identify possible delinquent customers early and allow them to keep a close watch on them. This has also allowed lenders to identify their prime customers, and subsequently build retention and reward strategies for different cohorts of customers.
By reducing manual subjectivity in decision-making, we enhance consistency and fairness in our operations.
Also Read | Tech-Enabled NBFCs Empowering MSMEs with Supply Chain Financing Solutions
Therefore, the power of data-driven decision-making cannot be overstated. It is not merely a tool; it is a transformational force that can propel organisations toward revenue maximisation and sustained profitability. As we navigate an increasingly complex and competitive landscape, we must continue to invest in data analytics, stay agile in our decision-making processes, and remain committed to innovation. By doing so, we ensure that we not only seize revenue opportunities but also set the benchmark for excellence in our industry.
Views expressed by Amit Mande, Chief Revenue Officer, U GRO Capital
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