Building data-driven digital systems to expand lending capabilities
Jun 30, 2020
A webinar summary.
While many organizations are now shifting to digital frameworks due to COVID-19, some SME lending companies have already begun focusing their business models on building digital systems for data analytics that can dramatically expand lending capabilities. Data analytics in SME banking is a growing field that will become increasingly important for SMEs, as it allows for enhanced risk analysis and better matching between business and lenders. Efficient data analysis can also allow lenders to speed up the loan application process, enabling them to serve SMEs better as the COVID-19 crisis rages on and well into the future.
In order to better explore the importance of data analytics in SME banking, the SME Finance Forum’s monthly member webinar highlighted how banks and financial institutions are using big data and advanced analytics to conduct SME lending in India. Through presentations from members Experian and CreditEnable and influential Indian bank, Axis bank, the discussion highlighted the new methods of data analysis; these organizations are using to improve their SME services and mitigate risk. In addition to these presentations from Salil Chugh (Experian), Ashish Badaya (Experian), Nadia Sood (CreditEnable), and Markandey Upadhyay (Axis Bank), the webinar featured a short Q&A with speakers that touched on the importance of digital transitions due to COVID-19 and how the pandemic has emphasized the need to go beyond traditional data to fully analyze risk.
One central component of applying advanced data analytics to SME lending is utilizing alternative datasets to establish a full understanding of the customer and potential risk. Information asymmetry is a key deterrent to SME lending in India, in addition to the prominent misconception that small business clients often have higher rates of non-performing loans. To address the lack of SME data, Experian created its SME X product, which utilizes alternative data in its data aggregation and machine learning programs to better understand and analyze potential clients. The alternative data used in these programs can range from banking data and credit bureau statistics to industry insights and sector-specific growth trends, encompassing broad trends in addition to client-specific information. Machine learning is utilized to expand upon normal credit scoring and data analysis, giving lenders a robust insight into risk-based segmentation and enabling digital underwriting of loans. Axis Bank is also utilizing alternative data from banking and e-commerce platforms better understand SME customers and adapt products to fit a range of needs. Through this advanced data processing, Experian is able to offer innovative solutions to lenders that better equip them to undertake and expand SME lending without lengthening their timeline. Furthermore, lenders such as Axis Bank that are now utilizing these systems have seen vastly shortened loan processing timelines and have dramatically expanded digital underwriting capabilities for a broader set of products due to this incorporation of data analysis.
In addition to benefitting lenders by reducing risk, incorporating advanced data analytics into initial client interactions can better match SMEs with banks and improve efficiency in the lending process. CreditEnable applies its analysis programs and AI systems to SME loan applications in India through its digital platform, allowing it to automatically screen applications and match borrowers’ credit profiles to lenders based on specific credit risk, geographic, and sector parameters. It’s automated analysis also allows CreditEnable to work with applicants throughout the process, preparing them to successfully apply and rejecting clients before they get to banks so that it doesn’t impact their credit score. This applied data processing has decreased the typical loan application timeline from four weeks to three to four days on average and allows SMEs to better understand their financial situation before they apply, dramatically increasing approval rates. As a result, the implementation of advanced analytics can support both financial institutions and SMEs throughout the lending process, decreasing broad SME funding gaps.
The spread of COVID-19 has further illustrated the utility of data analytics in SME lending, as banks and financial institutions seek to improve their digital services and better understand risk without access to typical risk assessment data. Programs based in alternative data and machine learning are well-equipped to assess sectoral and individual vulnerability based on data beyond repayment rates and credit scores, making them exceedingly valuable as financial institutions seek to analyze the pandemic’s impact on their portfolios. Through working directly with SMEs, this technology can also offer entrepreneurs specific business analytics support to help them preserve liquidity and outlast the crisis.
As a result, data analytics can be an extremely valuable tool in SME lending, and likely will only continue to become more influential as financial institutions expand their digital platforms and analysis capabilities.