fostering sme development - biia.com · 2018-06-06 · the last two rows shows the no. of times a...

Fostering SME Development

SME Access To Finance Workshop

Date: 5th November 2010,

Venue: Jakarata, Indonesia

Sharing of Experience from

“SME Rating Agency of India Ltd

(SMERA)

Sharing of Indian Experience-SMERA

SME segment overview of the country- split of Micro/Small/Medium Ent

Macro economic policies of the government regarding SMEs-priority sector lending status with quotas etc

Study on accounting standards and financial disclosures to assess their impact on rating decisions

Identification of clusters and execution of studies in clusters

Corporate linkages and its impact on SMEs ( OEMs/SEZ etc)

Access to quality of data on SMEs in public domain-ROC/Credit Bureau

Definition of SMEs followed by lending agencies

A need gap analysis-reasons for setting up a CRA for SMEs

Identification of scope of services to be offered by the CRA

SME Rating Agency-An Indian Experience

Source: Fourth All-India Census of Micro, Small & Medium Enterprises with reference year of 2004-05


2003 2004 2005

% of NBC

% of Total priority sector

advances % of NBC


advances % of NBC


advances

Public sector Banks 39.7 100 44.7 100 42.5 100

I) Agriculture 15.4 38.9 18.3 40.9 17.2 41.4

II) MSEs 7.8 19.7 11.1 24.8 11.3 26.5

III) others 16.5 41.5 15.3 34.4 14 32.1

Private sector Banks 42.9 100 42.5 100 46.8 100

I) Agriculture 12.7 36 17.1 35.7 15.9 40

II) MSEs 3.9 9.1 13.7 28.6 12 25.2

III) others 26.3 54.9 11.7 35.7 18.9 34.8

Foreign sector Banks 33.4 100 39.5 100 34.3 100

I) Agriculture 18.3 54.7 22.7 57.6 19.4 56.8

II) MSEs 10.3 30.8 12.2 30.8 11.2 32.8

III) others 4.8 14.5 4.6 11.6 3.7 10.4

Sources : 1. RBI Annual Report 2006 2. RBI Report on Trend & Progress of Banking in India 2006-07


Bank Lending to SMEs-An Overview

Registered Unregistered

No Finance/Self Finance 87.77 93.08

Finance through Institutional Sources

Finance through Non-Institutional Sources 1.02 2.12

Total 100 100

Source: Fourth All-India Census of Micro, Small & Medium Enterprises with reference year of 2006-07


Source: Basic Statistical returns, RBI, Aug 2004

Statistics on Credit Range to SMEs

0

10

20

30

40

50

60

Rs. 25,000

and Less

Above Rs.

25,000 and upto

Rs.2 Lakh

Above Rs. 2

Lakh and upto o Rs.5 Lakh

Above Rs. 5

Lakh and upto Rs.10 Lakh

Above Rs. 10


Above Rs. 25


Above Rs. 50

Lakh and upto Rs.1

Crore

Above Rs. 1

Crore and upto

Rs.4 Crore

Above Rs. 4

Crore and upto

Rs.6 Crore

Above Rs. 6

Crore and upto

Rs.10 Crore

Above Rs. 10

Crore and upto

Rs.25 Crore

Above Rs. 25

Crore

% Number of Accounts



US 57K

US 57 K to 1.14 Mill

US 1.14 Mill to 22 Mill

US 23 K

US 23K to 454K

US 454K to 1.14 mill

Estimation of the potential market size

Data on type/tenure/size of lending by various lending institutions

Identify market for ratings based on size of units-Bank Loan ratings/Instrument ratings/Debt ratings/NBFC ratings etc.

Divide the market size between regulatory and non-regulatory ratings

Detailed analysis of recruitment/training/marketing/compliance costs

Estimate revenues based on the above

Assess the Financial viability & overall feasibility of the entity with a business plan

Draw a P&L and Balance Sheet of the proposed agency for 5 years


Identification of technical partner, promoters & other shareholders

Formation of a core team

Incorporation of the company and other compliances

Identification and appointment of CEO

Identification of independent directors and formation of the board of directors

Formation of internal rating committee/criteria/external rating committee

Appointment of technical consultant for drawing rating process in compliance with IOSCO guidelines

Formulation of policies and guidelines for the company

Finalization of org structure, recruitment and training of staff

Marketing of the concept to lenders-Pre launch

Launch of operations


12

National Equity Fund

Integrated Infrastructural Development Scheme

Technology Development & Modernisation Fund Scheme

Technology Up gradation Fund Scheme for Textiles and Jute Industries

Risk Capital for Small Industries

Credit Guarantee Fund Scheme for Small Industries (through separate

Trust)

Credit Linked Capital Subsidy Scheme

Cumulative assistance USD 6 billion to the Indian SME sector since inception!

Set up in April 1990 under an Act of Parliament, SIDBI is the Principal Financial

Institution for Promotion, Financing and Development of Small Scale Industries and to

Coordinate the functions of Institutions Serving the Small Industries

Small Industries Development Bank of

India-Joint Promoter

13

• D&B India had data base on 146,000 entities with P&L and B/S in SME Space

Source – D&B database (2003-2006)

• D&B India has compiled detailed reports on over 50,000 Indian entities

since its inception.

• There are over 11,000 active* ratings in D&B India database.

• D&B India receives over 32,000 rating requests on cross border entities

annually.

* Ratings are valid 13 months from the date of rating carried out.

14

Board of SMERA comprises independent directors, nominees of SME centric Banks, SIDBI, D&B.

No single entity has a controlling stake-10 Banks and 1 D&B

Shareholding of Banks – commitment to the SME sector

SIDBI, 34%

D&B India, 34%

ICICI Bank, 11%

SBI, 9.50%

Citigroup, 5%

Indian Bank, 4%

Standard Chartered

Bank, 4.50%

Other PSBs, 28%

15

Request for

Interview and

site visit Rating Request SMERA Correspondents Entity

Questionnaire

Documentation,

audited results

and certified

projections

Site Visit

Assessment

Report

ROC

Information

Rating

Model

Industry

Assessment

and Cluster

data

Rating Analyst

External Committee Review

Third Party Data

– e.g. Litigation

Information

Conducts site

visit and

interviews

Management

Documents Obtained

External Data

SMERA

Database

Entity Rating Final Rating

SMERA started operations with its inauguration by

The Finance Minister of India on Sept 2005

On Sept 2010 SMERA completed 5 years with 9000 MSME

Ratings

Only Independent agency dedicated for SME Ratings

SMERA to play active role on newly created SME Exchange

SME Ratings/Green & Brown field Grading/MFI Grading/

Educational Institutions Rating/Green Rating

Building a suitable SME Rating model & processes for rating of SMEs

Mapping of SMERA model with Banks internal model-Interest rate benefit

Lenders’ resistance to share default data

Defining the process for ratings

High initial marketing and sales cost for brand buildup & awareness

Compiling a robust database of rated entities-Identification of RDBMS

Access to experienced and trained manpower & retention

Lack of adequate experienced members for external rating committee

Acceptability of external ratings by the lenders-mindset & legacy issue

Slow Regulatory approval due to lack of default data-Basel-II

Stricter compliance by regulations for rating agencies-post crisis

Lack of Information availability in the public domain

Submission of un-reliable and insufficient data by the SMEs for rating

High expectations from Ratings-SMEs/Lenders/Govt.


19

Variables are selected for modeling after considering so many variable selection/reduction process

Table below shows the different statistical and machine learning variable selection/reduction

process considered and the list of variables selected in each of the process

The Last two rows shows the no. of times a variable had been selected in the selection process

Variables which are selected more no. of times will be considered for further analysis/modeling

Variables which are considered important by the Bank will also be included in the modeling

20

Prob

Level Non- Non- Sensi- Speci- FALSE FALSE

Event Event tivity ficity POS NEG

0.05 734 1260 3208 32 38.1 95.8 28.2 81.4 2.5

0.1 671 2366 2102 95 58 87.6 53 75.8 3.9

0.146 562 3093 1375 204 69.8 73.4 69.2 71 6.2

0.2 442 3589 879 324 77 57.7 80.3 66.5 8.3

0.5 57 4401 67 709 85.2 7.4 98.5 54 13.9

Classification Table

Correct Incorrect Percentages

Event Event Correct

Classification table, also called as confusion matrix is the best way to assess the predictive power of the

model.

The above table is the output of classifying the observations according to whether the predicted

probabilities are above or below the cut-off values in the range (0,1). In our case we had chosen 5 different

cutoff points (0.05,0.1,0.146,0.2,0.5) for demonstration.

The accuracy of the model is measured by its Sensitivity (ability to predict an default event correctly) and

Specificity (ability to predict an non-default event correctly).

Sensitivity – Proportion of default companies that were predicted to be defaults.

Specificity – Proportion of non-default companies that were predicted to be non-defaults.

21

A Receiver Operating Characteristic (ROC)

curve is a graphical representation of the trade

off between the false negative and false positive

rates for every possible cut off.

The accuracy of the model (i.e. the ability of

the model to correctly classify default and non-

default cases) is measured by the area under the

ROC curve.

An area of 1 represents a perfect model, while

an area of .5 represents a worthless test.

The closer the curve follows the left-hand

border and then the top border of the ROC

space, the more accurate the model; the true

positive rate is high and the false positive rate is

low.

Area under the Curve (AUC) for the model is

78.2

22

Prediction Success for Learn Data

Actual Total Percent 0 1

Class Cases Correct N=3004 N=2230

0 4,468 64.167 2,867 1,601

1 766 82.115 137 629

Actual Total Percent 0 1

Class Cases Correct N=3131 N=2103

0 4,468 65.958 2,947 1,521

1 766 75.979 184 582

Prediction Success for Test Data

0

20

40

60

80

100

0 20 40 60 80 100

% C

lass

% Population

0

20

40

60

80

100

Gains Chart showing the Predictive Power

23

0

20

40

60

80

100

0 20 40 60 80 100

Pct.

Cla

ss 1

Pct. Population

Actual Predicted Predicted Total Percent

Class 0 1 Cases Correct

0 771 3,697 4468 17.26

1 19 747 766 97.52

Actual Predicted Predicted Total Percent

Class 0 1 Cases Correct

0 2,702 1,766 4468 60.47

1 161 605 766 78.98

Gains Chart showing the Predictive Power

Prediction Success @ 0.5 Cut-off Prediction Success @ 0.6 cut-off

24

Classification @ 0.05 Cut-off Classification @ 0.10 Cut-off

Classification @ 0.146 Cut-off Classification @ 0.50 Cut-off


SMEs are vulnerable

Information on SMEs is scarce and un-reliable

SMEs are geographically/sectorally dispersed-

difficult to assess the risk

SMEs are transaction intensive

Limited 7%

Partnership 23%

Private Limited 43%

Proprietorship 27%


0

200

400

600

800

1000

1200

1400

1600


0

500

1000

1500

2000

2500

3000

3500

1 2 3 4 5 6 7 8


Provided SME ratings which are: • Centralized

• Comprehensive

• Transparent

• Reliable

• Unbiased and independent - acceptable across the board

Facilitated Sound Credit Decisions: • Whom not to give credit, and more importantly,

• Whom to give credit

• Comfort to lenders and auditors

Provided Tools For Effective Risk Management • Arriving at risk weighted credit pricing • The evaluation of risks when changing the terms of credit agreements

Helped SMEs

• Gain Access to Bank Finance in an easy and cost-effective manner

• Better rated units were able to market their unit • Self improvement tool


Statistical

Modeling

Expert

Rules

Skilled

Business

Analysts

Industry

Trends

HYBRID RATING METHODOLOGY

Financial factors

based on

statistical

modeling

Qualitative factors

based on due

diligence and site

visit


Rating Factor Schema

Financial Parameters Non-Financial Parameters

Solvency Ratios

Liquidity Ratios

Activity Ratios

Profitability Ratios

Management Quality

Location Advantage

Marketing Network

Legal Issues

Industry and Macro-Economic Assessment

Trend Analysis

An exhaustive list of qualitative and quantitative factors considered for rating.

Each financial parameter is benchmarked within its industry-size peer group


Employee Count

Marketing Network

Management Shareholding

Age

Legal

Competitive forces

Industry Trade & Credit Terms

Industry growth

Each non-financial parameter is characterized by objective options which is scored

Management Risk

Industry Risk

Business Risk


Customized Rating Scale

Size Indicator reflects the

tangible net worth of a

company.

Composite Appraisal Indicator

reflects a company’s health,

stability and overall condition.

Ensures peer-to-peer comparison of an SME within its own size category (Size Indicator).

Banks’ Internal Risk Rating Models Mostly financial parameters Industry benchmarking – nil or limited Mostly generalization of MSMEs across geographic boundaries

Banks are involved as financiers Backward Looking

Enterprise Rating by SMERA

Rating beyond financials Robust industry benchmarking and also linked to size Consideration to parameters specific to a geographic location Ratings are neutral; credit enhancement measures are not reckoned Looking beyond



Parag Patki

Chief Executive Officer

SME Rating Agency of India Ltd

Tel. : +91 22 6714 1101

Fax : +91 22 6714 1142

Cell : +91 99303 95736

Email : [email protected]

mailto:[email protected]