Reforming MSME Lending Rules
Demand for credit is high yet credible information to appraise borrowers financially is deficient. While the MSME business models are evolving, the lending frameworks to assess their financial needs remain traditional. The rise of diversified economies and the end of monopolies have led to a global surge of micro, small & medium enterprises (MSME). Both developing and developed nations have a sizeable strength of MSME businesses that form the backbone of their economies. Several countries have developed financial access and support programs explicitly targeted at MSMEs, recognizing the potential for job creation and economic growth. However, living through the global recessionary outlook, the changing dynamics have created a new challenge for the lending industry. The credit crunch and poor credit ratings contribute to the problem of selective supply. Demand for credit is high yet credible information to appraise borrowers financially is deficient. While the MSME business models are evolving, the lending frameworks to assess their financial needs remain traditional.
Running through select global SME facts & stats, research suggests that firms with fewer than 500 employees are the core strength of the U.S. economy. Ninety-nine percent of all firms employ over 50 percent of private-sector employees and generate 65 percent of net new private-sector jobs. SMEs account for over half of the U.S. non-farm GDP and represent 98 percent of all the U.S. exporters and 34 percent of the U.S. export revenues. In the EU, SMEs represent 99% of all businesses. It employs many people and makes up a significant portion of the nation’s GDP. Unnoticed until recently, the policymakers and ministers have started paying attention to this sector and have slowly begun to realize the true potential it holds.
SMEs tend to be more vulnerable in times of crisis because it is difficult for them to downsize, they are not economically diverse, and most importantly, they have fewer financing options. Evidence exists to show that SMEs in most countries are confronted with an apparent downturn in demand for goods and services if not a demand slump in the fourth quarter of 2008. There is little transparency regarding the financial conditions of SMEs. Therefore, banks hesitate to give loans to small-scale units. A significant proportion of loans given to small enterprises in the past have compounded the problem of non-performing assets (NPAs) for banks. Unless reasonably detailed information on small firms is available, banks hesitate to take the risk and may prefer to lend to relatively larger firms to comply with regulation, thus leaving smaller firms significantly constrained for capital. Improving the quality of financial information is an essential requirement for enhancing the flow of funds to the SME sector, as the quality of information also influences decisions on loan finance.
The Meteoric Rise Of The MSME Sector In India
Credit demand is expected to reach INR 60 trillion by 2020. However, more than half the demand is still unsatisfied. MSMEs are critical to India’s economic growth, accounting for 40% of manufacturing GDP and employing 69 percent of the working population. As the country evolves into a sophisticated economy, the contribution of Small and Medium Enterprises is likely to grow even more.
As this market expands, so will its requirement for loans. Credit to MSMEs in the formal sector amounted to INR 16 trillion in March 2017 and is predicted to grow at a pace of 12% to 14% in 2018. In fiscal 2017, the unmet loan demand in the MSME market was anticipated to be about INR 25 trillion. Banks in India, for example, are not required to set explicit objectives for lending to SMEs. Instead, bank loans are made to micro and small businesses as part of the priority sector lending effort; Indian banks must provide 40% of their adjusted net bank credit to the priority sector, while foreign banks must devote 32% of their credit to the priority sector.
According to the Centre for Civil Society research, obtaining loans is one of the most challenging hurdles for an MSME firm. Debt is the principal source of funding for the majority of MSME firms. However, most of them struggle to qualify for loans due to a lack of collateral and favourable balance sheets. According to the IFC, the informal sector continues to be the primary source of financing for the MSME business, which has substantially higher interest rates and creates a significant development obstacle for the industry.
While the lending sector is aware of rising MSME loan demand, several obstacles have hampered its efforts to capitalize on this potential. Balance sheets, profit and loss accounts, income tax certificates, and collateral are all used by lenders. This material frequently lacks openness and reliability. Furthermore, this data collection may not directly indicate future success potential. There is no systematic or automated method for conducting extra analysis on the future performance of MSME. As a result, it is difficult for lending institutions to make risk appetite-based informed credit judgments when operating within this standard credit evaluation framework.
Disruption In The Lending Framework
The growth of technology and artificial intelligence (AI) has disrupted the loan business. Lending Kart, an online lending platform for small and medium-sized enterprises in India, offers collateral-free loans and claims to employ analytics for credit evaluation. Lenddo, a credit evaluation tool, assists financial institutions in doing machine learning-based due diligence for personal loans. For precision in lending and projected payback defaults, traditional credit underwriting techniques and processes rely on shifting human temperament and little remembrance of prior underwriting decision knowledge.
Today, neural network-driven Cognitive Agents adjudicate customer character, payment capacity, collateral value, and lending conditions in real-time by intensively studying and comparing numerous factors — a Big Data Initiative — to underwrite and continue to improve intelligence for advanced risk appetite-based portfolio management and customer acquisition strategies.
Lending institutions must implement modern credit evaluation frameworks based on alternative data and information sets in current turbulent times. Compared to traditional evaluation techniques, Big Data technology allows lenders to analyze non-obvious facts. Leveraging Big Data and generating trustworthy points with algorithmic science helps predict an MSME’s future success potential. Let us investigate the prospects for the Healthcare MSME sector in India by developing ways for data gathering and analysis to analyze the potential of Big Data and its relationship to small company cash flow.
Developing A Scoring Model For The Healthcare MSME
With the expansion of the private sector, India’s Healthcare MSME business is thriving. The private sector accounts for 82% of outpatient visits and 58% inpatient spending. Small hospitals or freestanding centres play an essential part in the private sector infra. According to NABH (National Accreditation Board for Hospitals), which has developed a new category called Small Healthcare Organizations (SHCO), “50,000 health care organizations are operating in our nation, a major percentage of which fall into the SHCO category with 50 beds.” However, the Healthcare MSME sector suffers financial constraints, particularly in tier II, III, and rural locations. Services are rapidly concentrating in urban regions, leaving individuals in villages and rural areas without access to healthcare.
The essential differentiator is the development of statistically prudent Machine Learning and Neural Network-driven models based on big-data micro-patterns to assess consumer character, future revenue potential, and paying capability. Professor initiated a research program to develop a sector-focused lending framework, using expertise and experiences from our Healthcare and Lifesciences industry vertical. Our study project aimed to create a model that would supplement standard evaluation scores with big-data-based information, allowing lending institutions to make more informed judgments. Given the diverse nature of enterprises in the healthcare MSME market, we divided them into Business to Customer (B2C) and Business to Business (B2B) categories (B2B). Machine learning models were created for both the B2C and B2B markets.
