If there is one thing microfinance is synonymous with is, its group lending. In fact, most of non-farm credit in India goes either through Joint Liability Group (JLG) lending or Self Help Group (SHG) lending. Till couple of years back, out of total Rs. 55,390 crore non-farm credit which went to rural households, 72.7% went through SHGs while the rest went through JLGs. Therefore, group lending methodology has been an indispensable element of non-farm rural credit in India. The success can primarily be attributable to peer cognizance. Group peer pressure avoids member default while social cognizance avoids field officers’ fraud, ghost lending and collusion with deceitful borrowers.
However, traditional group lending approach, rightly credited as a primary prerequisite for non-farm credit, has its own limitations when it comes to swift expansion. Because of long gestation period and slow organic growth process – characteristics which are inherently built into the design – a speedy expansion and inorganic scaling up has not been possible. This is one of many reasons why Indian MFIs are looking at ‘individual lending’ as the next frontier for expansion. Individual lending is the way forward for Indian microfinance institutions to reach quickly to more poor in new geographies.
Having said that, not one MFI practitioner has been able to crack the unsecured individual lending model. Individual lending comes with its own set of challenges namely – high instances of misrepresentations by deceitful individuals, field officer-borrower collusion, ghost lending, – leading to high incidences of defaults, in other words, high NPA. The average NPA of 5+ % in individual lending is almost ten times that of group lending (which averages around 0.5% as per claims of MFIs).
Inability to crack unsecured individual lending has to do with dearth of disruptive innovations in microfinance sector in last five years. Let’s first look at the reasons –
Firstly, MFIs still do not have mechanism to counter misrepresentations by deceitful individuals. This can be attributed to MFIs’ inability to assess the exact need, risk appetite and credit worthiness of individual borrowers in unrecognized sector. Due to multiplicity of livelihoods, lack of books of accounts and movement of cash across multiple livelihoods, creation of profit-loss and balance sheet statement of individual rural household becomes a daunting and unviable task for MFIs. Further, in absence of books of accounts, availability heuristic and mood congruence bias makes it difficult to extract real income & expense figures from individuals.
MFIs also do not have mechanism to identify potential ‘entrepreneurs’. While graduating to individual lending, MFIs find a significant percentage of borrowers seeking loan for micro-enterprises. The enterprises of poor are more of a way to buy a job (necessity entrepreneurship or forceful self-employment) when a conventional employment opportunity is not available. Many of the businesses are run because someone in the family can work in part-time. This person is often a woman, who takes up micro-enterprise in addition to household work. Apart from necessity-entrepreneurs, very few ‘born entrepreneurs’ are able to set up new enterprises due to lack of need-based finance and market intelligence.
Secondly, MFIs still do not have mechanism to counter employee frauds like field officer-borrower collusion and ghost lending in individual lending. Lack of fraud detection systems and robust organizational processes make MFIs vulnerable to employee frauds.
Absence of these mechanisms lead to high incidence of defaults over time, thereby discouraging MFIs to graduate to individual lending. This is precisely the reason why MFIs, for individual lending, have to resort to collateral based lending (like LAP – Loan against Property), which again pushes deserving poor back into financial exclusion. Traditional lending methods have, anyway, been favouring well off among the poor.
Need of Disruptive Innovation
MFIs face a strong need of individual credit models which can reduce the NPAs to acceptable levels and reduce dependence on field officer to curtail frauds. This can be possible through parametric decision making (or even non-parametric machine learning techniques) to predict repayment behaviour of individual borrowers, which means MFIs can use past data to predict future behaviour. There are plethora of statistical techniques, both parametric and non-parametric, to accomplish this.
Nevertheless, statistical techniques have severe limitations. One is never sure about predictive accuracy till one see’s the tail end. In microfinance individual lending, tail end can be attributable to variety of reasons which can be livelihood specific, geography specific, event-specific or even caste-specific! Owing to large number of possibilities for tail end, statistical modelling does not merit to be the way forward for microfinance. Many practitioners and vendors, in last few years, have created credit models using past data but with unsatisfactory results. The only approach which can work is the fundamental risk assessment of borrower i.e. lending decision based on individual P/L assessment, DSCR and cash flow based lending. And therefore, in depth knowledge of borrower psychographics in rural, semi-urban and urban slums becomes a large barrier to entry to crack a good quality predictive model.
MFIs also face a need of disruptive products like psychometric tests and market-analytics tools to reduce incidences of borrower misrepresentations and business failures, respectively. Psychometric tests can filter out necessity-entrepreneurs from born-entrepreneurs and suggest appropriate credit products accordingly. Market-analytics tools can fill the vacuum created by missing knowledge ecosystem and incubation ecosystem in unorganized sector. As most businesses in unorganized sector are started in a me-too manner or on basis of herd mentality – copying any enterprise that is moderately successful – these market-analytics tools can caution the new entrepreneurs about the feasibility of the enterprise in the given market.
Need of such disruptive innovations in unorganized sector can be traced back to recommendations of Nayak Committee (1991) which laid down banking norms for SMEs in India!
~ Pranay Bhargava