Demand for credit is high yet credible information to appraise borrower’s 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 end of monopolies has led to the ascent of micro, small & medium enterprises (MSME) across the world. Both developing and developed nations have a sizeable strength of MSME businesses that form the backbone of their economies. Recognizing the potential for job creation and economic growth, several countries have developed financial access and support programs targeted specifically at MSMEs. However, living through the global recessionary outlook the changing dynamics have created a new challenge for the lending industry. Credit crunch and poor credit ratings are contributing to the problem of selective supply. Demand for credit is high yet credible information to appraise borrower’s 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. They make up 99 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 provides employment to a large number of people and also makes up a significant portion of the nation's GDP. Unnoticed until recently, the policy makers and ministers have started paying attention to this sector and have slowly started 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 a clear 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. Significant proportion of loans given to small enterprises in the past have compounded the problem of non-performing assets (NPAs) for banks. Unless fairly 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 important requirement for enhancing the flow of funds to the SME sector, as the quality of information also influences decisions on loan finance.
Borrower? or Lender? . . Challenges remain on both sides . .
“We have a huge work-force dedicated to this segment, however loan sanction rates are still very low. Many applications are rejected as the borrowers lack credible documents, collaterals, cash-flows and relevant credit history. For small borrowers it is hard to take credit risks without proper information.”
Meanwhile, a few hundred kms away, Dr. Anuradha is running a Maternity clinic at her ancestral town named Hasanpur in U.P (India). She wants to expand the facility to have in-patient and critical care services. Dr. Anuradha says – “Most serious cases are referred to bigger multi-speciality centres in Delhi NCR. There is no other obstetrics centre with critical care facility in the area.” She believes her business currently has a huge lost sales component. For the planned expansion she needs INR 2 Crore (INR 20 million). Though she would like to borrow money for expansion, banks have historically declined her application based on the current size of balance sheet.
Meteoric rise of MSME Sector in India
As this segment grows, so does its need for credit. As on March 2017, credit to MSMEs in the formal sector stood at INR 16 trillion and is expected to grow at a rate of 12% to 14%. The unmet credit demand in the MSME segment was estimated to be nearly INR 25 trillion in the FY 2017. Noticeably banks in India are not provisioned with specific targets for lending to MSMEs. Bank loans given to the micro and small enterprises is part of the priority sector lending initiative - Indian banks are required to achieve a target of 40% of adjusted net bank credit to the priority sector, while foreign banks have a target of 32% exposure to the priority sector.
Centre for Civil Society study suggests that access to credit is one of the top challenges of a MSME business. Debt is the primary source of finance for most MSME businesses. However, most of them struggle to qualify for loans due to lack of collateral and positive balance sheets. According to IFC, the dominant source of debt for MSME industry is still the informal sector which has much higher interest rates and poses a huge growth barrier for the industry.
While the lending industry is aware of growing MSME credit demand, multiple challenges have hindered their aspirations to tap into this opportunity. Lenders rely on balance sheets, P&L statements, income tax certificates and collateral. Many a times, this information lacks transparency and credibility. Moreover, this information set is not a direct reflection of future success potential. There is no structured or automated approach to conduct additional analysis on MSME’s future performance. Operating within this traditional credit appraisal framework it is hard for lending institutions to make risk appetite based intelligent credit decisions.
Disruption In The Lending Framework
Today, neural network driven Cognitive Agents adjudicate customer character, payment capacity, collateral value and lending conditions by intensively studying and comparing numerous factors - a Big Data Initiative - in real-time to underwrite and continue to enhance intelligence for advanced strategies on Risk Appetite Based Portfolio Management and Customer Acquisition.
In these disrupting times, it is pertinent that lending institutions adopt newer credit appraisal frameworks using alternate data and information sets. Big Data technology can enable lenders to consider non-obvious information which is vast in comparison to the traditional assessment methods. Leveraging Big Data and deriving credible information with algorithmic science can indicate future success potential of an MSME business.
Developing approaches for data aggregation and analysis to study the potential of Big Data and its relation to the small business cash flow, let's explore the possibilities for the Healthcare MSME sector in India.
Developing A Scoring Model For The Healthcare MSME
Alternate Data Sources | Augmenting Credit Decision Making With The Help Of Big Data
Healthcare MSME industry in India is burgeoning with the rise of private sector. Private sector accounts for 82% of outpatient visits and 58% of inpatient expenditure. Within the private sector infra, small hospitals or standalone centres play a pivotal role. As per NABH (National Accreditation Board for Hospital) – which has defined a new category called Small Healthcare Organizations (SHCO) – “50,000 Health care organizations are functioning in our country out of which significant number falls under the SHCO category with < 50 beds”. Healthcare MSME sector is also facing a credit challenge, especially in tier II, III cities and rural towns. Services are fast aggregating to metropolitan areas depriving people in towns and rural areas from healthcare services.
Developing Machine Learning and Neural Network driven statistically prudent models that are based on big-data micro-patterns to adjudicate customer character, future revenue potential and paying capacity is the key differentiation. Harnessing knowledge and experiences from our Healthcare and Lifesciences industry vertical, we at Profisor, undertook a research initiative to device a sector focused lending framework. The objective of our research engagement was to devise a model, to augment traditional assessment scores with big-data based intelligence and to enable lending institutions make more informed decisions. Given the variegated nature of businesses across healthcare MSME industry we segmented the businesses into Business to Customer (B2C) and Business to Business categories (B2B). Machine learning models were developed for both B2C and B2B segments.
Healthcare MSME Segmentation | Assessment Levers For Identifying Alternate Information Sources
Numerous factors impacting the success potential of a MSME business – including financial, social, political, demographic, micro and macro-economic variables were analysed and compared in real time to strengthen the agility of the lending model.
NABH Accredited Healthcare Providers
Demographic Parameters : Demand Fueled By Changing Per Capita GDP
Designing The Next-gen Credit Appraisal Framework
Concluding our research hypothesis with astounding and insightful results, we arrived at a ratings mechanism. Not all data and data sources proved relevant. Macroeconomic factors, along with select social and demographic factors, turned out to be a better indicator of the MSMEs business success.
Devising a ratings engine is living the art and a science of the lending business. Select set of challenges one faces in this journey include:
Lending models are bound to evolve and with Big Data on our side the possibilities are amplifying every day. Not one hypothesis will stay the longer course. Methods and or approaches for model design and validation must be of higher importance. With time the impact of changing market conditions and borrower’s repayment behaviours must be analysed to study the maturity of new-age assessment models.
Although the math will continue to advance, lending machinery should distinguish on methods beyond application of big data, for instance the Cash Flow Analysis of borrower by their business type (Listed Company, Private Company, Partnership, Proprietorship and others); Fraud Analytics to eliminate “Cook Book” scenarios; and Social Media Reputation scores for consumer facing businesses.
Innovation necessitates higher business risk appetite . . be the first or await a new benchmark scoring mechanism from a startup, choice is yours to make . .
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