Struggles of Rural Micro, Small and Medium Enterprises for Bank Finance: Role of District Industries Centres in India

Abstract

MSMEs are particularly important for emerging countries, primarily because of their potential in job creation. The MSEs are more than just GDP earners; they are instruments of inclusive growth which touch upon the lives of the most vulnerable, marginalised, women and the most skilled. Being the largest source of employment after agriculture, the MSE sector in India enables 650 lakh people. MSEs also act as ancillary industries for large scale industries. Yet, lack of access to finance is a major obstacle to their growth. In addition to limited development of industries in NER, there is limited availability of data on whatever industries exist there. Around 54% of industrial units are concentrated in Assam among NE states. There were 37,356 registered MSME units in Assam providing employment to 2.05 lakh persons till the end of March 2013. The SLBC data showing the credit disbursals towards MSME sector by commercial banks inAssam do not show a favourable picture. Formal lending sector is always preferred over informal sector by the MSMEs. The study aims to find out the characteristics of MSMEs operating under rural villages in Assam and also the various problems encountered by them in obtaining finance from banks.

1. Introduction

The definition of MSMEs differs across nations. In India, the limits for manufacturing/service enterprise, as notified by Ministry of Micro, Small and Medium Enterprises are as mentioned in Table 1.

MSMEs are particularly important for emerging countries, mainly because of their potential in job creation. The Eleventh Five Year Plan reports that MSMEs have been recognised as engines of economic growth. The MSEs are more than just GDP earners; they are instruments of inclusive growth which touch upon the lives of the most vulnerable and the most marginalised. Being the largest source of employment after agriculture, the MSE sector in India enables 650 lakh people. MSEs also act as ancillary industries for large scale industries providing them with raw materials, vital components, and backward linkages. This sector seeks to empower people to break the cycle of poverty and deprivation. In addition to limited development of industries in NER, there is limited availability of data on whatever industries exist there. Around 54% of industrial units are concentrated in Assam among NE states. There were 37,356 registered MSME units in Assam providing employment to 2.05 lakh persons till the end of March 2013.

MSMEs need special credit policy especially at the start up stages. The study aims to find out the characteristics of MSMEs operating under rural villages in Assam and also the various problems encountered by them in obtaining finance from banks.

1.1. Registering with DIC

The District Industries Centre is the institution at the district level, which provides all the services and support facilities to the entrepreneur for setting up micro, small and medium enterprises. This included identification of suitable schemes, preparation of feasibility reports, arrangements for credit facilities, machinery and equipment, provision of raw materials and development of industrial clusters etc. The various schemes that are being implemented by the DIC, Kamrup to provide financial assistance to the MSME units are as follows:

1. Sarothi: The basic objective of the scheme is to provide financial assistance in the form of loan with Interest subvention @ 5% P.A through a designated bank.

2. Biponi: The objective of the scheme is to support to the micro & small enterprises to participate in different trade fairs and events within the state, in the country and abroad for marketing of their products and also getting the exposure.

3. Boneej: This is a special scheme to assist the rural industries of the state by providing special grant for rural industrial enterprises in traditional and micro sector in Assam. It is proposed to provide Rs. 25,000.00 (Rupees twenty-five thousand) as a grant to the industries located in Rural areas only where the annual turnover is less than Rs. 5 lakh.

4. Transport Subsidy Scheme (TSS, 1971)/Freight Subsidy Scheme (FSS), 2013: The transport subsidy scheme (TSS) was introduced on July 23, 1971 to develop industrialisation in the remote, hilly and inaccessible areas and re-introduced as freight subsidy scheme from 2013. Under the scheme, transportation cost on movement of raw material/finished goods to and from the location of the unit to the designated rail head is reimbursed for a period of 5 years from the date of commencement of commercial production. However, fresh registration has been discontinued from 2016.

5. Angel Fund Scheme: The angel fund scheme is designed to give gainful employment to the first generation entrepreneurs through providing soft loan. The purpose of the scheme is to provide easy loan to skilled as well as un-skilled entrepreneurs for starting or developing micro enterprises under Manufacturing & Service sector, Agriculture & Allied activities or any other sector for gainful employment.

6. North-East Industrial and Investment Promotion Policy (NEIPP), 2007: Under this scheme, all new units as well as existing units which go in for substantial expansion and which commence commercial production within the 10 year period from the date of notification of NEIIPP, 2007 will be eligible for incentives for a period of 10 years from the date of commencement of commercial production.

7. Prime Minister’s Employment Generation Programme (PMEGP): Government of India has approved the introduction of a new credit linked subsidy programme popularly known as DIC loan, by merging the two schemes that were in operation till March 31, 2008 namely Prime Minister’s Rojgar Yojana (PMRY) and Rural Employment Generation Programme (REGP).

The limits of funding under PMEGP are as shown in Table 2.

Manufacturing sector
Enterprises	Investment in plant and machinery
Micro	Does not exceed 25 lakh rupees
Small	More than 25 lakh rupees but does not exceed 5 crore rupees
Medium	More than 5 crore rupees but does not exceed 10 crore rupees
Service sector
Enterprises	Investment in equipment
Micro	Does not exceed 10 lakh rupees
Small	More than 10 lakh rupees but does not exceed 2 crore rupees
Medium	More than 2 crore rupees but does not exceed 5 crore rupees

Table 1.

Limits of investments for micro, small and medium enterprises.

Categories of beneficiaries under PMEGP	Beneficiary’s contribution (of project cost)	Rate of Subsidy (of project cost)
		Urban	Rural
General category	10%	15%	25%
Special (including SC/ST/OBC/Minorities/Women, Ex-servicemen, physically handicapped, NER, hill and border areas, etc.	5%	25%	35%

Table 2.

Funding under PMEGP according to categories.

The balance amount of the total project cost will be provided by Banks as term loan as well as working capital. As per RBI guidelines the project costing up to Rs. 5 lakhs under PMEGP loans are free from collateral security.

Though registering with DIC is not compulsory for the MSME units, doing so is beneficial for the economy and the MSMEs. It helps them avail some benefits for such units such as:

• Credit direction (Priority sector lending)

• Differential interest rates

• Excise exemption schemes

• Exemption under direct tax laws

• Statutory support such as reservation and the Interest on Delayed Payments Act.

Gurjar and Sudindra [1] found through trend analysis that the registration of MSMEs in India has a fluctuating trend and declined in growth rate over the last few years. It has declined by 22% in 2007–2008, 21% in 2008–2009, 23% in 2009–2010, 31% in 2010–2011, 41% in 2011–2012, 33% in 2012–2013 and 14% in 2013–2014. Average growth rate of registered MSMEs has declined by 23%. Yadav [2] reported from annual MSME census that only 1.5 million MSMEs are in registered segment while the remaining 24.5 million that constitute 94% of the units are in unregistered segment.

2. Review of literature

Venkatesh and Kumari [3] states that the significance of MSMEs is attributable to their calibre for employment generation, low capital and technology requirement, promotion of industrial development in rural areas, use of traditional or inherited skill, use of local resources, mobilisation of resources and exportability of products. The sector generates around 100 million jobs through over 46 million units situated throughout the geographical expanse of the country. With 38% contribution to the nation’s GDP and 40 and 45% share of the overall exports and manufacturing output, respectively, it is easy to comprehend the salience of the role they play in social and economic restructuring of India. They further stated that only 4% of micro, small and medium enterprises (MSMEs) fall within the purview of the Indian banking system and therefore the small scale industries need to be strengthened and supported. The numerous initiatives introduced in the past few years are a step in the right direction as they contribute to the well-being of the individuals engaged in small scale industries which positively affects the progress of the economy as a whole. Shilpi [4] found that the contribution of the micro, small and medium non-farm activities is substantial both in terms of employment and value added in Bangladesh. There are about 4.25 million MSM enterprises in Bangladesh and nearly 70% of them are located in rural areas. The rural financial markets in Bangladesh include formal and micro-finance institutions and informal sources and continue to be inadequate to meet the demand of the rural population. The author added that the transaction costs of providing and receiving financial services in rural areas are high, because of smaller loan sizes, more dispersed geographical coverage, lack of information about potential borrowers, high risk of default, and difficulty of enforcing contracts in the case of default.

Chauhan [5] wrote that the majority of India’s population resides in villages and agriculture and allied activities constitute their major occupation, but the traditional occupational structure of India got destroyed by replacement of agriculture and craftsman production by the British superimposed colonial mode of production. A SWOT analysis conducted by the author on micro and small enterprises in rural areas revealed one of their major weaknesses as high rate of interest on available modes of finance. Das [6] conducted a study in the District of Ganjam in Orissa, which found that the largest weakness for small business owners is to raise finance. The author stated that many small business owners were found to be forced to invest their own funds into their business because institutional lenders like banks and government financial corporations are unwilling to advance money to these small units. According to Barslund and Tarp [7], rural households strongly rely on informal credit arrangements with neighbours, friends and relatives to start a micro enterprise rather than the availability of formal credit institutes. They found that local access to formal finance does not seem to be an important factor when it comes to the individual decision to start a micro enterprise. Priscilla et al. [8] have found that most small business owners do not keep records, have limited access to financial services and are mainly dependent on unregulated financial institutions. Low levels of financial literacy of the small business owners make them more prone to business shocks and more difficult to sustain or stir a business to grow. Kumar and Sharma [9] stated that finance is the lifeblood of business but majority of Indian MSMEs are falling in unorganised sector and hence struggling for regular credit flow. MSMEs require timely and adequate capital infusion which is not feasible through informal sources but through term loans and working capital loans by formal sources. The author has noted that over the years there has been a significant increase in credit extended to this sector by the banks but there still exists a huge gap between credit supply and demand by the sector. MSMEs face problem with access to adequate and timely credit at a reasonable cost. The statistics compiled in the fourth census of MSME sector revealed that only 5.18% of the units had availed finance through institutional sources, 2.05% through non-institutional sources and 92.77% of MSME units dependent on self-finance of informal sources. Sambrani et al. [10] noted that the Indian rural entrepreneurs are been face some major challenges such as low scope for external funds mainly due to lack of ability to produce tangible security. Loan sanctioning is a tedious process and is time consuming. Rural entrepreneurs face lack proper financial knowledge and have very low access to financial training. Rural entrepreneurs are seen to prefer borrowings from local Zamindars (Landlords) or from regional rural banks who sometimes charge unreasonable interest rates.

Sohns and Diez [11] said that the mere existence of a bank in the village is not sufficient for starting a micro enterprise. They said that it is more important that the bank provides affordable micro loans. Kumar et al. (2014) in his study in Orissa observed that public sector banks are playing the dominant role in catering to the MSME sector. One reason behind this could be that PSU bank branch network has been growing at a much faster rate than their private sector counterparts especially in rural areas. Arsjah and Djamaris [12] inform that the Indonesian government encourages the banks to have at least 20% of their portfolio in small medium enterprise (SMEs). This policy requires a lot of commitment from banks as lending to this sector requires specific treatment. Developing microenterprises must be a priority in order to prepare the facing challenges. The clients’ limitation and the banks’ interest must be formulated such that the loans delivery mechanism can provide access for microenterprises without eliminating principles and prudential banking. Patnaik et al. [13] has tried to focus on how sometimes stringent banking requirements makes it difficult for MSME borrowers to obtain desired credit. Author has observes from MSME reports that 92% of the MSMEs have no finance, 5% are getting loan from institutional sources and 3% are receiving credit from non-institutional sources. Therefore he feels that simplification of documentation process can help to ease credit procurement and will certainly provide a big push for the sector. Mandalaa et al. [14] conducted a case study in a rural bank in Bali, which followed the procedure of submitting application, data verification and approval or disapproval decision for the credit assessment process. The data used by the bank for assessment are gender, age, credit amount, monthly income, expenditure of each month, current payment per month, savings, collateral types, collateral values, loan period, type of business activities, sources of funding and previous credit status/rating. Based on the results, the study found that collateral value is the most important criterion in credit assessment. Ikasari et al. [15] have subdivided the dimensions of access to finance as accessibility, eligibility and affordability. They have found that small business owners and banks in Indonesia and Thailand do not have issues with access to finance but are seen to have mutual trust issues. In Indonesian banks, it was found that collateral quality remained an issue whereas Thai banks did not express significant concern related to collateral. Paramasivam and Mari Selvam [16] feel that attitude of bank officials need to be improved while sanctioning loans to the MSME sector. Arora et al. [17] from their study in Punjab reveals that though all the nationalised banks have delved into microfinance, a lot of effort is still required to pave the way for microfinance in the commercial banking sector. The problem is how many target beneficiaries are aware of various schemes available and how many actually avail these schemes presents a dismal picture. Most bankers have reported that microfinance clients make up less than 5% of their total number of clients.

Gupta [18] has tried to address the issue of urban migration. The author has stated that 38% of migration happens for employment. Uncontrolled migration adversely affects both the origin place and the labour market of destination place. Migrants also affect income, expenditure pattern and investment and change relation at household and community level. He further highlighted how in a country like India where the 73% population are from rural or semi urban area, and more than 50% are working in agricultural and allied activity, the growth in rural to urban migration for various reasons can affect aversely the uniform growth of the nation. The main reason behind rural to urban migration is the industrial development in urban areas which gives more opportunities for employment. Therefore to control migration, development of MSMEs in rural area is one of the vital solutions because it could create income and employment opportunities to local people. Lavanya et al. [19] stated that as per OECD report 2005, rural areas are affected by problems of reduced employment opportunities in primary industries and an ageing population due to migration of young population to urban areas in search of employment opportunities. There exists a wide gap between rural and urban areas in terms of infrastructure, market and financial access etc. The author feels that development of rural areas is the only solution to solve these issues.

2.1. Research gap

From the literature review, it has been found that considerable problems exist in financing rural MSMEs all over the world. But there are very limited findings on this issue, especially in the state of Assam. Also, the author has found limited studies in Assam that has evaluated the role of DICs in improving the bank financing of MSMEs.

3. Objective

The study aims to find out:

1. A comparison between demographic and financial characteristics of registered and unregistered rural MSMEs.

2. A comparison between problems being experienced by registered and unregistered rural MSMEs in obtaining bank loans.

3.1. Limitations of the study

The study is not free from limitations such as:

1. The whole state of Assam could not be covered due to time and financial constraints.

2. The viewpoint of the bankers could not be taken.

3. There is possibility of personal bias in answering the questionnaire by the MSME units as respondents.

3.2. Scope for further study

The study can be extended geographically to include other districts of Assam. Also the bankers’ side of the story has not been included which could be a basis for a further study on the issue.

4. Research methodology

4.1. Geographical area

The geographical area for the study is Kamrup (rural), Nagaon (rural) and Dibrugarh (rural) districts in the state of Assam as combined they constitute more nearly 50% of MSME units in Assam.

4.2. Population

Population of the study includes registered and unregistered MSMEs operating within Kamrup (rural), Nagaon (rural) and Dibrugarh (rural) districts in the State of Assam.

4.3. Sampling design

4.3.1. Sample size

The study has taken the responses of 100 sample units, out of which 50 were registered under DIC and 50 were not registered. Out of each 50, 25 were from Kamrup district, 15 from Dibrugarh district and 10 from Nagaon district. The sampling unit is MSME units and sampling element is owner/s, proprietor/s, manager/s or competent representative.

4.3.2. Sampling procedure

For registered MSME units, Random Sampling method was used to select samples. The website stattrek.com was used to generate random numbers. The EM-II list of MSMEs compiled by DIC, Kamrup was consulted using the random numbers generated to select the samples for the study. For unregistered MSME units, snowball and convenience sampling method was used. The respondents were selected on the basis of location, availability and willingness to respond.

4.3.3. Data collection

Primary and secondary data were used for the study. The primary data collection was carried out with the help of questionnaires presented to MSME units. Secondary data was collected from published reports and other data source from websites and personal visits to offices, such as RBI reports, SLBC reports, reports and lists by District Industries and Commerce centres, Annual Reports by MSME Development Institutes, Planning Commission Reports, journals and articles.

5. Data analysis

5.1. Sample profile

The sample characteristics of MSME units as per their demographic characteristics as a comparison between registered and unregistered units are presented in Table 3.

Profile of respondents	Frequency
	Registered units	Unregistered units
Gender of the owner
Male	23	35
Female	27	15
Age of the owner
Upto 30 years	1	7
F 30–40 years	21	29
F 41–50 years	24	11
Above 50 years	4	3
Education status of the owner
10th	2	1
10 + 2	20	19
Graduate	27	26
Post Graduate	1	4
Unit type
Micro	46	49
Small	4	1
Form of business
Proprietorship	29	38
Partnership	21	12
Nature of business
Manufacturing	28	19
Service	22	28
Both	0	3
Whether owned premise
Yes	48	44
No	2	6

Table 3.

Comparative profile of registered and unregistered MSMEs.

It can be observed from Table 3 that both registered and unregistered units have very similar demographic characteristics. The number of female MSME owners is higher in case of registered MSMEs possibly due to the fact that government has launched many schemes targeting women entrepreneurs in the past few years. Similarly there are more manufacturing units which are registered MSMEs, as government also has many schemes for the manufacturing sector.

From the financial profiling of MSME units (Table 4) it can be seen that there is a very similar pattern in case of amount of capital invested, yearly turnover and amount of loan sanctioned among registered and unregistered units. With respect to sources of finance we can see that more proportion of unregistered units have additionally obtained their funds from non-bank sources as compared to registered units. Informal sources of credit are not always healthy for a business and therefore more MSMEs need to be brought under the formal financial system as confirmed by many studies (Barslund and Tarp, 2008) [7, 8]. It is also observed that more unregistered units have utilised their loans for operational needs and repayment of previous loans. This could be because of the fact that majority of DIC’s schemes of bank finances are exclusively for new businesses only.

Profile of units	Frequency
	Registered units	Unregistered units
Capital invested
Upto 100,000	19	25
100,001–500,000	28	19
500,001–1,500,000	1	4
15,000,001–2,500,000	2	2
Yearly turnover
Upto 100,000	28	31
100,001–500,000	17	18
500,001–1,000,000	5	1
Amount of loan applied
Upto 100,000	38	41
100,001–500,000	10	8
500,001–1,000,000	2	1
Source of finance (Other than banks)
Other formal finance institutions	0	2
Own funds	28	48
Funds from relatives and friends	19	23
Funds from moneylenders	4	15
Utilisation of loan
To start business	44	38
Operational needs	6	12
Repayment of previous loans	9	14
Business expansion	4	9

Table 4.

Comparative financial profile of registered and unregistered units.

After comparing firm and financial characteristics between registered and non-registered MSME units, we have found that possibly since DICs implement a number of schemes for women, there more female entrepreneurs who are registered. Also because of schemes specifically for the manufacturing sector such as Boneej and various subsidy schemes; we find a strikingly large number of manufacturing units registered under DIC. It is also found higher numbers of registered units seem to have used the loan to start the business which could be because DIC has more schemes for new units when compared to existing ones. This could be a reason why existing MSME units are not motivated to get registered under DIC.

5.2. Statistical analysis of data

In order to further fulfil our objectives various tests using SPSS has been done and the following are the results obtained:

5.2.1. Registration and satisfaction with source of finance

It is believed that registration will help in obtaining desired amount of loan. A chi-square test was done as shown in Table 5 to check for association. The hypothesis formulated was as follows:

1. H₀ = There is no association between registration status of firms and satisfaction with source of finance.

2. H₁ = There is an association between registration status of firms and satisfaction with source of finance.

	Value	df	Asymp. Sig. (2-sided)
Pearson Chi-square	0.056a	1	0.812
Continuity correctionb	0.000	1	1.000
Likelihood ratio	0.056	1	0.812
Fisher’s exact test
Linear-by-linear association	0.056	1	0.813
No. of valid cases	100

Table 5.

Chi-square tests for registration status of firms and satisfaction with source of finance.

^a

0 cells (.0%) have expected count less than 5. The minimum expected count is 11.50.

^b

Computed only for a 2x2 table.

Since the p-value is more than 0.5, we do not have sufficient evidence to reject the null hypothesis. It can be concluded that there is no association between registration status and satisfaction with source of finance.

5.2.2. Registration and time taken to sanction loan

It is expected that registration will help in obtaining bank loans quickly. A chi-square test was done as shown in Table 6 to check for association. The hypothesis formulated was as follows:

1. H₀ = There is no association between registration status of firms and time taken to sanction loan.

2. H₁ = There is an association between registration status of firms and time taken to sanction loan.

	Value	df	Asymp. Sig. (2-sided)
Pearson Chi-square	13.724a	4	0.008
Likelihood ratio	14.445	4	0.006
Linear-by-linear association	11.886	1	0.001
No. of valid cases	100

Table 6.

Chi-square tests for registration and time taken to sanction loan.

^a

Two cells (20.0%) have expected count less than 5. The minimum expected count is 0.50.

Since the p-value is less than 0.5, we reject the null hypothesis. It can be concluded that there is an association between registration status and time taken to sanction loan.

5.2.3. Registration and satisfaction with sanctioned amount

It is believed that registration will help in obtaining desired amount of loan. A chi-square test was done to check for association (Table 7). The hypothesis formulated was as follows:

1. H₀ = There is no association between registration status of firms and satisfaction with sanctioned amount.

2. H₁ = There is an association betweenregistration status of firms and satisfaction with sanctioned amount.

	Value	df	Asymp. Sig. (2-sided)
Pearson Chi-square	1.382a	1	0.240
Continuity correctionb	0.614	1	0.433
Likelihood ratio	1.425	1	0.233
Fisher’s exact test
Linear-by-linear association	1.369	1	0.242
No. of valid cases	100

Table 7.

Chi-square tests for registration and satisfaction with sanctioned amount.

^a

2 cells (50.0%) have expected count less than 5. The minimum expected count is 3.50.

^b

Computed only for a 2x2 table.

Since the p-value is more than 0.5, the null hypothesis cannot be rejected. It can be concluded that there is no association between registration status and satisfaction with sanctioned amount of loan.

5.2.4. Registration and difficulties faced

Here, 37 statements have been used in a Likert scale to identify whether MSMEs face difficulty in borrowing from banks. It has been attempted to try and find out whether those MSMEs which have registered with DIC experience same or different levels of difficulty in obtaining bank loan for MSMEs. Firstly, the aggregate difficulty scores for each respondent have been calculated by simple addition of difficulty response points assigned by the respondent. The difficulty response is a five-point Likert scale (Strongly disagree, disagree, neutral, agree and strongly agree).

Next an independent sample t-test was conducted as shown in Table 8 between difficulty scores and registration status to find out whether there is any effect of registration on difficulty scores.

		Levene’s test for Equality of Variances		t-test for equality of means
		F	Sig.	t	df	Sig. (2-tailed)	Mean difference	Std. error difference	95% confidence interval of the difference
									Lower	Upper
Agg_diff	Equal variances assumed	0.688	0.409	0.329	98	0.743	1.000	3.042	−5.038	7.038
	Equal variances not assumed			0.329	97.011	0.743	1.000	3.042	−5.038	7.038

Table 8.

Independent samples test between registration and difficulties faced.

The hypothesis formulated is as follows:

1. H₀ = There is no difference between average difficulty score for registered and non-registered borrowers.

2. H₁ = There is difference between average difficulty score for registered and non-registered borrowers.

Since the Levene’s test for equality of variance could not be rejected, it was assumed that there is equality of variance and select the p-value of t-test results accordingly. Since p-value >0.05, there is no sufficient evidence to reject null hypothesis. Therefore it implies that there is no significant difference between average difficulty scores for registered and non-registered borrowers.

6. Conclusion and suggestions

The following conclusions are arrived at from the study:

1. Registration status has no association with the satisfaction level of the borrowers with respect to their source of finance. DIC has therefore not been successful in influencing bank borrower’s experience.

2. Registration status has an influence over time taken to sanction loans. Registered units who wish to borrow through DIC have to go through a preliminary screening before being referred to a bank. The bank then performs its routine screening policy as per their individual policies. This may have influenced the total time taken by the bank is sanctioning the loans.

3. Registration with DIC also has no association with satisfaction with respect to amount of loan sanctioned by the bank for MSME borrowers. Therefore it is possible that bank’s decision of sanctioning amount of loan is not influenced by the registration status of the MSME unit.

4. Registration also was seen to have no influence over the average aggregate difficulty scores experienced in getting loans from bank. Therefore it is seen that borrowers were subjected to similar levels of difficulty whether or not they were registered under DIC.

From the study, it can be concluded that DIC has been able to influence only the time duration of the loan process. It has not been able to influence satisfaction level with source of fund, satisfaction level with amount sanctioned by the bank and difficulties with borrowing. In order to invite more registrations under DIC, there has to be benefits which can motivate the MSMEs. Therefore only with incorporation of stronger and better procedures to achieve their objectives of helping the MSME sector can be achieved. Studies on bank-MSME relationships have produces similar results in different states and countries highlighting the need for stronger customer bond with MSMEs [12, 13, 14, 15, 16]. Banks being an important channel to bring MSMEs under the formal financial system, bank services need to be specialised for MSMEs.