By Umesh Revankar, Executive Vice Chairman, Shriram Finance
Lending has always been an integral part of the economy’s fundamentals. In the rapidly evolving landscape of financial services, traditional lending practices are being reshaped by the integration of data and analytics. It is evident that the ability to harness data effectively can revolutionise the way we approach lending, enhance customer experiences, and drive financial inclusion.
However, with the passage of time, traditional lending models have also experienced limitations. These models have relied heavily on credit scores, historical financial statements, and collateral. While these methods have their merits, they often fall short in assessing the true creditworthiness of individuals and businesses, particularly those in underserved segments. These models were also based on the trust and relationship of the borrower and lenders. Many potential borrowers are excluded from the formal financial system due to a lack of credit history or insufficient documentation.
The power of data and analytics
In the yesteryears, lenders went into every possible market with all available products which were invariably pre-structured. This led to a lot of hits and misses in terms of which markets were a success and which failed. Similar was the case in terms of products – some succeeded and some failed. This effectively affected the topline and bottomline of lenders since every market was a protracted learning curve for them.
The advent of Data and Analytics has changed this entire paradigm by playing two crucial roles for lenders. The first role is helping identify the right markets, the product/s that are required in that market and structuring the product/s to meet the requirements of the market. The second role is then to help decide the eligibility of the borrower and structure of the loan to be given to the borrower.
While personal loans are still largely collateral-based, loans to businesses are steadily moving from collateral-based lending to cash-flow based lending decisions. Data and analytics offer a solution to these limitations by providing a more comprehensive and nuanced understanding of borrowers. By leveraging alternative data sources such as transaction history, social media activity, utility bill payments, and even mobile phone usage patterns, lenders can gain deeper insights into an individual’s or business’s financial behavior.
Here are some ways in which data and analytics are transforming our approach:
1. Enhanced risk assessment
Traditional risk assessment models often rely on static and historical data. In contrast, data analytics allows us to create dynamic risk models that are continuously updated with real-time information. By analysing diverse data sets, we can identify patterns and trends that provide a more accurate assessment of a borrower’s risk profile. This enables us to make more informed lending decisions and mitigate potential risks.
2. Personalised lending solutions
Every borrower is unique, and a one-size-fits-all approach to lending can be limiting. Data analytics enables us to tailor lending solutions to meet the specific needs of each borrower. By understanding their financial behavior, preferences, and challenges, we can offer personalised loan products, repayment plans, and interest rates that align with their circumstances. This not only improves customer satisfaction but also increases the likelihood of successful loan repayments.
3. Streamlining operations
Data analytics also enhances operational efficiency by automating and streamlining various processes. From loan origination to underwriting and monitoring, data-driven automation reduces the time and effort required for manual tasks. This not only accelerates the lending process but also reduces costs, allowing us to offer more competitive loan products to our customers.
4. Proactive fraud detection
Fraud is a significant concern in the lending industry, and traditional methods of fraud detection often lag behind sophisticated fraud schemes. By leveraging advanced analytics and machine learning, we can proactively identify and prevent fraudulent activities. Analyzing patterns and anomalies in transaction data helps us detect potential fraud early, safeguarding both our customers and our business.
5. Managing lending risks through data analytics: In today’s diverse geographical markets, lending institutions can mitigate risks and improve efficiency by leveraging data analytics. Delinquency prediction models use historical loan and payment data to foresee erratic borrower behavior and guide credit renewal decisions, ensuring timely repayment. Advanced analytics provide deeper insights into customer behavior, enabling more effective collection strategies through micro-segmentation based on demographics, account activity, and risk ratings. Additionally, data from sources like Aadhaar, mobile usage, and social media, along with continuous monitoring via mobile apps, help prevent credit fraud by validating customer identity and detecting potential fraud scenarios post-loan approval.
6. Leveraging data for customised lending solutions in diverse geographical markets: Data analytics enables lending institutions to identify customer patterns and tailor their approach to specific geographical locations. By analyzing customer data such as transaction records, demographics, and local economic conditions, lenders can uncover unique behavioral trends and preferences in different regions. This deep understanding allows for the creation of customised lending solutions and strategies that cater to the specific needs of borrowers in each market. Advanced analytics tools facilitate the segmentation of customers into micro-groups, enabling more precise credit assessments, personalised loan offers, and targeted collection strategies. As a result, enhance decision-making processes, improve customer satisfaction, and reduce risks across varied geographical markets is witnessed.
The future of lending
As we look to the future, the role of data and analytics in lending will continue to expand. The integration of artificial intelligence, machine learning, and blockchain technology will further enhance our capabilities, enabling us to deliver even more innovative and secure lending solutions.
We are committed to staying at the forefront of this transformation. By continuously investing in data and analytics, we aim to redefine traditional lending practices, drive financial inclusion, and create value for our customers and stakeholders.
In conclusion, the shift from traditional lending to data-driven lending is not just a trend but a necessity in today’s digital age. By embracing data and analytics, we can overcome the limitations of traditional models, provide personalised and inclusive lending solutions, and build a more robust and resilient financial ecosystem. However, we should be cognizant of the fact that in India, there may not be sufficient historical data for conclusive decision-making. It is therefore imperative that we scale up gradually rather than growing exponentially which can lead to disaster.