Project On

“CREDIT DELIVERY MECHANISM IN

INDIAN BANKS & SUGGESTION

FOR IMPROVEMNETS”

Project Guide

Prof. N Krishnamurthy

Submitted By

Kunal.H.Gosalia

MMS Finance

2008-2010

Certificate

This is to certify that the study presented by Kunal.H.Gosalia to

Thakur Institute of Management Studies & Research in part

completion of MMS Course under “CREDIT DELIEVRY MECHANISM

IN INDIAN BANKS – SUGGESTION FOR IMPROVEMENTS” has been

done under my guidance in the year 2008-2010.

The Project is in the nature of original work that has not so far been

submitted for any other course in this institute or any other institute.

Reference of work and relative sources of information has been given

at the end of the project.

Signature of the Candidate

Forwarded through the Research Guide

Signature of the Guide

Prof. N. Krishnamurthy

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Acknowledgement

There are several claimants on my gratitude in the task of preparation of this Project report on “Credit Delivery Mechanism in Indian Banks & Suggestions for Improvements”. I would like to extend my gratitude to my guide Prof. N.Krishnamurthy, without whose continuous guidance and encouragement this project would not have been possible.

I would also like to thank Prof. S.Ganga, our Course Coordinator, for providing the necessary guidance and support, during the preparation of the project. Also I would like to take this opportunity to thank all the staff of Thakur Institute of Management Studies and Research for providing the necessary infrastructure and facilities for helping to take the project to fruition.

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Executive Summary

In India, our environment hitherto was totally regulated and

directed with reference to our industry, banking etc. High tariff walls

due to shortage of foreign exchange and forced restriction on imports,

protected indigenous Industries and created a suppliers' market, where

the consumer had no or very limited choice. Similarly Banks operated

in an atmosphere where everything was directed and controlled

externally (albeit either by RBI or Finance Ministry), the need for

studying risk was never felt. Lack of product-quality in Industry or poor

service and lack of efficiency in service-centers were never felt

seriously, as there was no competition and no alternative choice before

the consumer.

But with dismantling of State control over every sector of

economy, with deregulation (i.e. supply, demand and prices) to shape

on the basis of market forces, Indian Industry and Indian Banking have

now come to face a new challenge. Competition results in the survival

of the fittest. In the liberalized environment, competing with the high-

tech new generation Banks, the erstwhile commercial banks have to

re-orient themselves to the changed situation.

Lending which was the primary function of banking has gained

lot of importance as it determines the profitability of the bank. A bank

can lend successfully only when a borrower’s credit worthiness is

accurately assessed. The method of analysis required varies from

borrower to borrower. It also varies in function of the type of lending

being considered. The lending can differ in the way credit given to

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retail customers or corporate customers, secured or unsecured, long

term or short term etc.

For financing a project, bank would look at the funds generated

by the future cash flows to repay the loan, for asset secured lending,

bank would look at the assets and for an overdraft facility, it would look

at the way the account has been run over the past few years. In this

project the appropriate methods of analysis for lending to companies,

known as ‘corporate credit’ is being detailed.

This project is about the credit analysis in banks. The process of

lending, the various analysis involved in giving credit to a customer are

detailed. Focus is on financial ratio analysis, non financial analysis and

different models used by banks in the lending process.

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Contents

Sr. No Topic Pg. Nos.

1 Introduction 7 – 10

2 Overview of Credit Analysis 11 – 14

3 Lending Process 15 – 19

4 Financial Statement Analysis I 20 – 24

5 Financial Statement Analysis II 25 – 30

6 Non Financial Analysis 31 – 32

7 Credit Models 33 – 49

8 Conclusion 50-51

9 Bibliography 52

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1. Introduction

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The significant transformation of the banking industry in India is

clearly evident from the changes that have occurred in the financial

markets, institutions and products. While deregulation has opened up

new vistas for banks to augment revenues, it has entailed greater

competition and consequently greater risks. Cross-border flows and

entry of new products, particularly derivative instruments, have

impacted significantly on the domestic banking sector forcing banks to

adjust the product mix, as also to effect rapid changes in their

processes and operations in order to remain competitive in the

globalize environment. These developments have facilitated greater

choice for consumers, who have become more discerning and

demanding compelling banks to offer a broader range of products

through diverse distribution channels. The traditional face of banks as

mere financial intermediaries has since altered and risk management

has emerged as their defining attribute.

Currently, the most important factor shaping the world is

globalization. Integration of domestic markets with international

financial markets has been facilitated by tremendous advancement in

information and communications technology. But, such an

environment has also meant that a problem in one country can

sometimes adversely impact one or more countries instantaneously,

even if they are fundamentally strong.

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There is a growing realization that the ability of countries to

conduct business across national borders and the ability to cope with

the possible downside risks would depend, interalia, on the soundness

of the financial system. This has consequently meant the adoption of a

strong and transparent, prudential, regulatory, supervisory,

technological and institutional framework in the financial sector on par

with international best practices. All this necessitates a transformation:

a transformation in the mindset, in the business processes and finally,

in knowledge management. This process is not a one shot affair; it

needs to be appropriately phased in the least disruptive manner.

The banking and financial crises in emerging economies have

demonstrated that, when things go wrong with the financial system,

they can result in a severe economic downturn. Furthermore, banking

crises often impose substantial costs on the exchequer, the incidence

of which is ultimately borne by the taxpayer. The World Bank

Annual Report (2002) has observed that the loss of US $1

trillion in banking crisis in the 1980s and 1990s is equal to the

total flow of official development assistance to developing

countries from 1950s to the present date. As a consequence, the

focus of financial market reform in many emerging economies has

been towards increasing efficiency while at the same time ensuring

stability in financial markets.

From this perspective, financial sector reforms are essential in

order to avoid such costs. It is, therefore, not surprising that financial

market reform is at the forefront of public policy debate in recent

years. Financial sector reform, through the development of an efficient

financial system, is thus perceived as a key element in raising

countries out of their 'low level equilibrium trap'. As the World Bank

Annual Report (2005) observes, ‘a robust financial system is a

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precondition for a sound investment climate, growth and the

reduction of poverty ’.

Financial sector reforms were initiated in India two decade ago

with a view to improving efficiency in the process of financial

intermediation, enhancing the effectiveness in the conduct of

monetary policy and creating conditions for integration of the domestic

financial sector with the global system. The first phase of reforms was

guided by the recommendations of Narasimhan Committee.

The approach was to ensure that ‘the financial services industry

operates on the basis of operational flexibility and functional

autonomy with a view to enhancing efficiency, productivity and

profitability'.

The second phase, guided by Narasimham Committee II,

focused on strengthening the foundations of the banking system

and bringing about structural improvements. Further intensive

discussions are held on important issues related to corporate

governance, reform of the capital structure, (in the context of

Basel II norms), retail banking, risk management technology, and

human resources development, among others.

Since 1992, significant changes have been introduced in the

Indian financial system. These changes have infused an element of

competition in the financial system, marking the gradual end of

financial repression characterized by price and non-price controls in

the process of financial intermediation. While financial markets have

been fairly developed, there still remains a large extent of

segmentation of markets and non-level playing field among

participants, which contribute to volatility in asset prices. This volatility

is exacerbated by the lack of liquidity in the secondary markets. The

purpose of this paper is to highlight the need for the regulator and

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market participants to recognize the risks in the financial system, the

products available to hedge risks and the instruments, including

derivatives that are required to be developed in the Indian system.

The financial sector serves the economic function of

intermediation by ensuring efficient allocation of resources in the

economy. Financial intermediation is enabled through a four-pronged

transformation mechanism consisting of liability-asset transformation,

size transformation, maturity transformation and risk transformation.

2. Overview of Credit

Analysis

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2.1 Need for credit analysis

Credit analysis is done in order to lessen the credit risk faced by a

bank. Credit risk is defined as the possibility of losses associated with

diminution in the credit quality of borrowers or counterparties. In a

bank's portfolio, losses stem from outright default due to inability or

unwillingness of a customer or counterparty to meet commitments in

relation to lending, trading, settlement and other financial

transactions. Alternatively, losses result from reduction in portfolio

value arising from actual or perceived deterioration in credit quality.

Credit risk emanates from a bank's dealings with an individual,

corporate, bank, financial institution or a sovereign. Credit risk may

take the following forms

In the case of direct lending: principal/and or interest amount

may not be repaid;

In the case of guarantees or letters of credit: funds may not be

forthcoming from the constituents upon crystallization of the

liability;

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In the case of treasury operations: the payment or series of

payments due from the counter parties under the respective

contracts may not be forthcoming or ceases;

In the case of securities trading businesses: funds/ securities

settlement may not be effected;

In the case of cross-border exposure: the availability and free

transfer of foreign currency funds may either cease or the

sovereign may impose restrictions.

2.2 Role of credit analysis

The extent of the credit analysis is determined by

The size and nature of the enquiry,

The potential future business with the company,

The availability of security to support loans,

The existing relationship with the customer.

The analysis determines whether the available information is adequate

for decision making purposes, of if additional information is required.

The analysis therefore covers a wide range of issues.

For evaluating a loan proposal for a company, it is necessary to

Obtain credit and trade references,

Examine the borrower’s financial condition,

Consult with legal counsel regarding a particular aspect of the

draft loan agreement

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2.3 Framework of Credit Analysis

Credit analysis includes financial and non-financial factors, and these

factors are all interrelated. These factors include:

The environment

The industry

Competitive position of the firm

Financial risks the company has

Management/business risks

Loan structure and documentation issues.

2.4 Overview of Credit Analysis Process

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Identify purpose of loan

Historical financial analysis

Quality of management Cash

Flow forecast

Specify sources of repayment

primary/secondary

Security evaluation

Industry evaluation

Environment

evaluation

Key risks and

mitigation

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3. Lending Process

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3.1 RBI Guidelines for Credit Risk Management Credit Rating

Framework

A Credit-risk Rating Framework (CRF) is necessary to avoid the

limitations associated with a simplistic and broad classification of

loans/exposures into a "good" or a "bad" category. The CRF deploys a

number/ alphabet/ symbol as a primary summary indicator of risks

associated with a credit exposure. Such a rating framework is the basic

module for developing a credit risk management system and all

advanced models/approaches are based on this structure. These

frameworks have been primarily driven by a need to standardize and

uniformly communicate the "judgment" in credit selection procedures

and are not a substitute to the vast lending experience accumulated

by the banks' professional staff.

Broadly, CRF can be used for the following purposes:

1. Individual credit selection, wherein either a borrower or a

particular exposure/ facility is rated on the CRF

2. Pricing (credit spread) and specific features of the loan facility.

This would largely constitute transaction-level analysis.

3. Portfolio-level analysis.

4. Surveillance, monitoring and internal MIS

These would be relevant for portfolio-level analysis. For instance, the

spread of credit exposures across various CRF categories, the mean

and the standard deviation of losses occurring in each CRF category

and the overall migration of exposures would highlight the aggregated

credit-risk for the entire portfolio of the bank.

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3.2 Types of Credit Rating

Credit rating can be classified as:

3.2.1 External Credit Rating.

3.3.2 Internal Credit Rating.

3.2.1 External Credit Rating:

A credit rating is not, in general, an investment recommendation

concerning a given security. In the words of S&P,” A credit rating is

S&P's opinion of the general creditworthiness of an obligor, or the

creditworthiness of an obligor with respect to a particular debt security

or other financial obligation, based on relevant risk factors.” In Moody's

words, a rating is, “an opinion on the future ability and legal obligation

of an issuer to make timely payments of principal and interest on a

specific fixed-income security.”

Since S&P and Moody's are considered to have expertise in credit

rating and are regarded as unbiased evaluators, there ratings are

widely accepted by market participants and regulatory agencies.

Financial institutions, when required to hold investment grade bonds

by their regulators use the rating of credit agencies such as S&P and

Moody's to determine which bonds are of investment grade.

The subject of credit rating might be a company issuing debt

obligations. In the case of such “issuer credit ratings” the rating is an

opinion on the obligor’s overall capacity to meet its financial

obligations. The opinion is not specific to any particular liability of the

company, nor does it consider merits of having guarantors for some of

the obligations. In the issuer credit rating categories are

a) Counter party ratings

b) Corporate credit ratings

c) Sovereign credit ratings

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The rating process includes quantitative, qualitative, and legal

analyses. The quantitative analysis is mainly, financial analysis and is

based on the firm’s financial reports. The qualitative analysis is

concerned with the quality of management, and includes a through

review of the firm’s competitiveness within its industry as well as the

expected growth of the industry and its vulnerability to technological

changes, regulatory changes, and labor relations.

3.2.2 Internal Credit Rating:

A typical risk rating system (RRS) will assign both an obligor

rating to each borrower (or group of borrowers), and a facility rating to

each available facility. A risk rating (RR) is designed to depict the risk

of loss in a credit facility. A robust RRS should offer a carefully

designed, structured, and documented series of steps for the

assessment of each rating.

The following are the steps for assessment of rating:

Objectivity and Methodology: The goal is to generate accurate and

consistent risk rating, yet also to allow professional judgment to

significantly influence a rating where it is appropriate. The expected

loss is the product of an exposure (say, Rs. 100) and the probability of

default (say, 2%) of an obligor (or borrower) and the loss rate given

default (say, 50%) in any specific credit facility. In this example,

The expected loss = 100*.02*.50 = Rs. 1

A typical risk rating methodology (RRM)

o Initial assign an obligor rating that identifies the expected

probability of default by that borrower (or group) in repaying its

obligations in normal course of business.

o The RRS then identifies the risk loss (principle/interest) by

assigning an RR to each individual credit facility granted to an

obligor.

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The obligor rating represents the probability of default by a borrower in

repaying its obligation in the normal course of business. The facility

rating represents the expected loss of principal and/ or interest on any

business credit facility. It combines the likelihood of default by a

borrower and conditional severity of loss, should default occur, from

the credit facilities available to the borrower.

3.3 Documentation - Term Loan Agreement

The loan agreement is the legal document that defines the

relationship between the borrower and the lending bank or banks. The

loan agreement gives the bank the right to terminate the loan

agreement if any of the following events occur:

o Non payment of principal

o Non payment of interest

o Acceleration of other indebtedness (cross default)

o Voluntary or involuntary bankruptcy.

These events are known as ‘events of default’ and the mechanism in

the loan agreement used to control them are known as loan agreement

covenants.

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4. Financial Statement

Analysis I

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4.1 Ratio Analysis

Interpreting and analyzing financial statements enables to

discover what a company’s financial position is. Ratio analysis is a

device which is used to

Compare the performance of a company this year with last year

Compare the performance of a company with its competitors.

Detect specific weaknesses

Determine a company’s liquidity (ability to meet debts)

Determine a company’s profitability

Provide an indicator of trends.

Financial ratios can be divided into five categories

4.1.1. Liquidity ratios

4.1.2 Turnover ratios

4.1.3 Leverage ratios

4.1.4 Profitability ratios

4.1.5 Valuation ratios

4.1.1 Liquidity Ratios: Liquidity refers to the ability of a firm to meet

its obligations in the short-run, usually one year. Liquidity ratios are

generally based on the relationship between current assets (the

sources for meeting short-term obligations) and current liabilities. The

important liquidity ratios are:

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The Current Ratio: A simple measure that estimates whether

the business can pay debts due within one year from assets that

it expects to turn into cash within that year.

A ratio of less than one is often a cause for concern, particularly

if it persists for any length of time.

Current Ratio = Current Assets

Current Liabilities

The Quick Ratio: Not all assets can be turned into cash quickly

or easily. Some - notably raw materials and other stocks - must

first be turned into final product, then sold and the cash collected

from debtors. The Quick Ratio therefore adjusts the Current Ratio

to eliminate all assets that are not already

Quick Ratio = Current Assets – Stock

Current Liabilities

4.1.2 Turnover Ratios: Turnover ratios, also referred to as activity

ratios or asset management ratios, measure how efficiently the assets

are employed by a firm. These ratios are based on the relationship

between the level of activity, represented by sales or cost of goods

sold, and levels of various assets. The important turnover ratios are:

inventory turnover, average collection period, receivable turnover,

fixed assets turnover, and total assets turnover.

Stock Turnover Ratio: Stock turnover measures how fast the

inventory is moving through the firm and generating sales.

Inventory turnover reflects the efficiency of inventory

management.

Stock Turnover = Cost of Goods Sold

Average Inventory

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Debtor’s Turnover Ratio: This ratio shows how many times

sundry debtors (accounts receivable) turnover during the year.

Debtor’s Turnover = Net Credit Sales

Average Sundry Debtors

Average Collection Period: The average collection period

represents the number of days’ worth of credit sales that is

locked in sundry debtors.

Average Collection Period = Average Sundry Debtors

Average Daily Credit Sales

The average collection period and debtors’ are related as follows:

Average Collection Period = 365

Debtors’ Turnover

Fixed Assets Turnover: this ratio measures sales per rupee of

investment in fixed assets.

Fixed Assets Turnover = Net Sales

Average Net Fixed Assets

Total Assets Turnover: Akin to the output-capital ratio in

economic analysis, the total assets turnover is defined as:

Total Assets Turnover = Net Sales

Average Total Assets

4.1.3 Leverage Ratios: Leverage ratios help in assessing the risk

arising from the use of debt capital. Two types of ratios are commonly

used to analyze financial leverage: structural ratios and coverage

ratios. Structural ratios are based on the proportions of debt and equity

in the financial structure of the firm. The important structural ratios are

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debt-equity ratio and debt-assets ratio. Coverage ratios show the

relationship between debt servicing commitments and the sources of

meeting these burdens. The important coverage ratios are interest

coverage ratio, fixed charges coverage ratio, and debt service

coverage ratio.

Debt – Equity Ratio: the debt equity ratio shows the relative

contributions of creditors and owners.

Debt Equity Ratio = Debt

Equity

Debt – Assets Ratio: the debt-asset ratio measures the extent

to which borrowed funds support the firm’s assets.

Debt – Assets Ratio = Debt

Assets

Interest Coverage Ratio: Also called the times interest earned,

this ratio enables to know whether a firm can easily meet its

interest burden even if profit before interest and taxes suffer a

considerable decline or not.

Interest Coverage Ratio = Profit before Interest and Taxes

Interest

Fixed Charges Coverage Ratio: This ratio shows how many

times the cash flow before interest and taxes covers all fixed

financing charges.

Fixed Charges Coverage Ratio =

Profit before Interest & Taxes + Depreciation

Debt Interest

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Debt Service Coverage Ratio: Used by banks in India, the

debt service coverage ratio is defined as:

Debt Service Coverage Ratio =

Profit after Tax + Dep. + Non Cash Charges + Interest + Lease

Rental

Interest + Lease Rental +Repayment of Loan

4.1.4 Profitability Ratios: Profitability reflects the final result of

business operations. There are tow types of profitability ratios: profit

margins ratios and rate of return ratios. Profit margin ratios show the

relationship between profit and sales. The most popular profit margin

ratios are: gross profit margin ratio and net profit margin ratio. Rate of

return ratios reflect the relationship between profit and investment.

The important rate of return measures are: return on assets, earning

power return on capital employed and return on equity.

Gross Profit Margin Ratio: This ratio tells us something about

the business's ability consistently to control its production costs

or to manage the margins its makes on products its buys and

sells.

Gross Profit Margin Ratio= Gross Profit

Net Sales

Net Profit Margin Ratio: This ratio shows the earnings left for

shareholders as a percentage of net sales. It measures the

overall efficiency of production, administration, selling, financing

and pricing.

Net Profit Margin Ratio = Net Profit

Net Sales

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Return on Assets: ROA is an odd measure because its

numerator measures the return to shareholders whereas its

denominator represents the contribution of all investors.

Return on Assets = Profit after Tax

Average Total Assets

Earning Power: Earning power is a measure of business

performance which is not affected by interest charges and tax

burden. It abstracts away the effect of capital structure and tax

factor and focuses on operating performance.

Earning Power = Profit before Interest and Taxes

Average Total Assets

Return on Capital Employed: ROCE is the post-tax version of

earning power. It considers the effect of taxation, but not the

capital structure. It is internally consistent.

Return on Capital Employed: Profit before Interest and Tax (1 –

Tax rate)

Average Total Assets

Return on Equity: It measures the profitability of equity funds

invested in the firm. It’s very important measure because it

reflects the productivity of ownership (or risk) capital employed

in the firm.

Return on Equity = PAT – Preference Dividend

Equity Share Cap. + Reserves & Surplus

4.1.5 Valuation Ratios: Valuation ratios indicate how the equity

stock of the company is assessed in the capital market. Since the

market value of equity reflects the combined influence of risk and

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return, valuation ratios are the most comprehensive measures of a

firm’s performance. The important valuation ratios are: price-earnings

ratio, yield, and market value to book value ratio.

Price Earnings Ratio: The price earnings ratio or the price

earnings multiple is a summary measure which primarily reflects

growth prospects, risk characteristics shareholder orientation,

corporate image and degree of liquidity

Price Earnings Ratio = Market Price per share

Earnings per share

Yield: this is a measure of the rate of return earned by

shareholders.

Yield = Dividend + Price change

Initial Price

Market Value to Book Value: This ratio reflects the

contribution of a firm to the wealth of society.

Market Value to Book Value = Market Value per share

Book Value per share

4.2 Historical and Peer Group Analysis

Useful information can be obtained by comparing ratios from the

same company over time (from historical data) or comparing the dame

ratios in similar companies (from industry studies from sources such as

ratings agencies)

The advantage of using historical ratio analysis from the same

company is that the information is easily obtained and directly

comparable. The disadvantage is results may have been influenced by

different economic conditions, different production methods, inflation,

or changes in accounting policies.

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For peer group analysis, it is often difficult to find similar

companies with which to make a comparison. Typically a bank

compares the financial ratios calculated on the spreadsheet with

published data, such as lists of industry standard ratios. Another

approach is that banks use the data published by credit rating

agencies or financial databases available in the market which enables

the analysis of the company vis-à-vis its competitors.

5. Financial Statement

Analysis II

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5.1 Cash Flows:

A firm basically generates cash and spends cash. It generates

cash when it issues securities, raises a bank loan, sells a product,

disposes an asset, etc. it spends cash when it redeems securities, pays

interest and dividends, purchases materials, acquires an asset etc. the

activities that generate cash are called sources of cash and activities

that absorb cash are called uses of cash.

Companies can be profitable with negative cash flows and loss

making with positive cash flows. A company can report a large profit

for a year in which the cash balance may have fallen, perhaps as a

result of heavy expenditure on fixed assets. Likewise, a company can

be losing money and generating cash via asset disposals. It is

important to understand that cash and profit are different.

The purpose of cash flow statement is, therefore, to report the

net change in the cash balance and to help explain how the surplus or

deficit in cash arose.

5.2 Components of Cash Flows

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Cash Flow from Operating activities + Cash Flow from Investing

activities + Cash Flow from Financing activities = Net Cash Flow for the

period.

5.2.1 Cash Flow from Operating activities

Profit before interest and tax

+ Depreciation

+ Non cash charges

+ Changes in working capital

= Cash flow from operating activities

5.2.2 Cash flow from investing activities

Sale of assets

- Purchase of assets

= Cash flow from investing activities

5.2.3 Cash flow from Financing activities

Issue of shares

+ Issue of debentures

+ Raising of loan

- Redemption of debentures

- Repayment of loan

= Cash flow from financing activities

5.3 Summary of cash flow statement

o Reports the financial effect of all transactions during the

accounting period.

o Mixes capital and revenue transactions and is entirely backward

looking.

Cash flow statements are used to

o Assess the long term risks in a lending situation

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o Test the assumptions of a given project

o Understand the parameters in a project in order to devise

appropriate security structures and financial ratio covenants in

the loan agreement.

6. Non Financial Analysis

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6.1 Economy Analysis

It is important to do an economic analysis before lending. The

rates are which credit is offered is determined by the demand and

supply of funds in the market. Usually higher interest rates are charged

for loans when there is high inflation in the economy. It is also

important to know the growth rate of the economy because if the

economy grows at a faster rate, the companies also grow at a fast rate

and there is more need for funds.

6.2 Industry Analysis

The industry sector in which the borrower operates has a

significant impact upon the way the business is managed. It also

produces different financing and asset structures in the balance sheet

of the businesses. The terms of trade between the buyer and the seller

in the industry and the methods by which the contract of sale are

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controlled will all have and e effect upon borrower’s activity in the

industry and a financial implication in its business.

6.3 Business Analysis

The nature of the market in which the customer operates or in

which its products are sold is important to understand. In investigating

the market the analyst is be able to establish a point of view of both

macro and micro- economic elements that may affect the future of the

debtor or obligor. The potential effectiveness of plans and strategies

can be achieved when comparing the obligor’s view of market

compared to independently sourced information. In business analysis,

specific analysis is done by understanding the product, the growth of

the business in which the firm is operating, its corporate strategy and

plans, its business plans and its management.

7. Credit Models

34

7.1 Credit Evaluation

Proper assessment of credit risks is an important element of

credit management. It helps in establishing credit limits. In assessing

credit risks, two types of errors occur:

Type I error: A good customer is misclassified as a poor credit

risk.

Type II error: A bad customer is misclassified as a good credit

risk.

Both the errors are costly. Type I error leads to loss on account of

failure to serve a particular client. Type II error results in creation of

Non Performing Asset (NPA) on account of loan given to a risky

customer.

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While misclassification errors cannot be eliminated wholly, a

bank can mitigate these occurrences by doing proper credit evaluation.

Three broad approaches are used for credit evaluation, viz. traditional

credit analysis, numerical credit scoring, and discriminant analysis.

7.2. Credit Models

7.2.1 Traditional Credit Analysis

The traditional approach to credit analysis calls for assessing a

prospective customer in terms of “five C’s of credit”.

Character: The willingness of the customer to honor his

obligations. It reflects integrity, a moral attribute that is

considered very important by credit managers.

Capacity: The ability of the customer to meet credit obligations

from the operating cash flows.

Capital: The financial reserves of the customer. If the customer

has problems in meeting credit obligations from operating cash

flow, the focus shifts to its capital.

Collateral: the security offered by the customer in the form of

pledged assets.

Conditions: the general economic conditions that affect the

customer.

A bank can rely upon the following information to evaluate the credit

worthiness of a customer.

Financial Statements: financial statements contain a wealth of

information. A searching analysis of the customer’s financial

statements can provide useful insights into the creditworthiness

of the customer. The following ratios are particularly helpful in

this context: current ratio, acid test ratio, debt equity ratio, EBIT

to total assets ratio, and return on equity.

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Bank references: The banker of the prospective customer is

another source of authentic information. To ensure a higher

degree of candour, the customer’s banker may be approached

indirectly by the bank of the firm granting credit.

Previous experience: the previous experience of the bank with

the customer is very helpful in all further dealings of the bank.

Bank has all the details regarding the customer’s bank accounts,

his deposits, withdrawals etc.

Prices and yields on securities: for listed companies, valuable

inferences can be derived from stock market data. Higher the

price-earnings multiple and lower the yield on bonds, other

things being equal, lower will be the credit risk.

Traditional Credit Analysis

37

7.2.2 Risk Classification Scheme

38

On the basis of information and analysis in the credit

investigation process, customers are classified into various risk

categories. A simple risk classification scheme is shown below;

Risk Classification Scheme

Risk

Class

Description

1 Customers with no risk of default

2 Customers with negligible risk of default (default rate less

than 2%)

3 Customers with little risk of default (default rate between 2%

& 5%)

4 Customers with some risk of default (default rate between 5%

& 10%)

5 Customers with significant risk of default (default rate in

excess of 10%)

7.2.3 Sequential Credit Analysis

Sequential credit analysis is an efficient method. In this analysis,

investigation is carried further if the benefit of such analysis outweighs

it cost. To illustrate, consider three stages of credit analysis: review of

the past payment record, detailed internal analysis, and credit

investigation by an external agency. The credit analyst proceeds from

stage one to stage two only if there is no past payment history and

hence a detailed internal credit analysis is warranted. Likewise, the

credit analyst goes from stage two to stage three only if internal credit

analysis suggests that the customer poses a medium risks and hence

there is a need for external credit analysis.

7.2.4 Discriminant Analysis

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The technique of discriminant analysis is employed to construct a

better risk index than the one given above. The nature of this analysis

is discussed with the help of a simple example. A bank considers the

following financial ratios of its customers as the basic determinants of

creditworthiness: current ratio and return on net worth. The plot of its

customers on a graph of these two variables is shown below. X‘s

Represent customers who have paid their interest and installments on

time and O‘s represent customers who have defaulted on payment of

interest or installment or both.. the straight line seems to separate the

X’s from the O’s – while it may not be possible to completely separate

the X’s and O’s with the help of a straight line, the straight line does a

fairly good job of segregating the two groups. The equation of this

straight line is

Z = 1 Current Ratio + 0.1 Return on Equity

Since this is the line which discriminates between the good customers

(those who pay) and the bad customers (those who default), a

customer with a Z score of more than 3 is deemed creditworthy and a

customer with a Z score of less than 3 is considered not creditworthy.

The higher the Z score, the stronger the credit rating.

Discriminant Analysis

40

7.2.5 Numerical Credit Scoring

In traditional credit analysis, customers are assigned to various

risk classes somewhat judgmentally on the basis of the five C’s of

credit. Credit analysts may, however, want to use a more systematic

numerical credit scoring system. Such a system may involve the

following steps:

Identify factors relevant for credit evaluation.

Assign weights to these factors that reflect their relative

importance.

Rate the customer on various factors, using a suitable rating

scale (usually a 5-point scale or a 7-point scale is used.)

For each factor, multiply the factor rating with the factor weight

to get the factor score.

Add all the factor scores to get the overall customer rating index.

Based on the rating index, classify the customer.

Return on Equity

CurrentRatio

41

Construction of a Credit Rating Index (based on a 5-point

rating scale)

Scoring applications:

It is estimated that as much as 80% of the “measurable and

controllable” risk is decided upon at the time of underwriting. Stated

another way, once the account or loan is approved, servicing and loss

mitigation techniques can control future losses only to a limited extent

relative to the control or loss avoidance offered by making the correct

decision in the first place. Because of this, an obvious area of scoring

application is that of evaluating new credit applicants.

For e.g. the use of mortgage score could have substantially

reduced the foreclosure rate. In this comparison, the approval rate for

the score based approach was set equal to that of manual

underwriting. Although this example comes from the mortgage

industry, the loss avoidance benefit due to scoring is believed to be

similar across all asset classes where there is meaningful data on

which to develop scores. The benefit of loss avoidance is only one of

many. Equally important is the benefit of fast, consistent, unbiased,

and defensible decision making. This is especially true in today’s

42

environment of pre-approvals, on-line banking, Internet Web sites, and

Fair Lending considerations.

Application scoring ranges in degree of scope from providing

supplemental information to the underwriter (who makes final

decision) to totally automated decision making independent of an

underwriter’s intervention. Thus scoring is used to determine the

“processing path” or degree of underwriting and level of

documentation required. Score-suggested declinations typically are

review by a human underwriter before a declination is issued.

Score based underwriting is typically the first application

explored when scoring technology is introduced to a new product type.

A natural extension of this is risk based or score based pricing.

7.2.6 Risk based pricing

Risk based pricing is used extensively. In mortgage industry, for

example, has offered different note rates for different levels of risk for

risk for decades, and for those who put less than 20% of the house’s

value into the down payment, the industry requires that mortgage

insurance premium be paid. The higher loan to value (LTV) ratio loans

has been demonstrated to be significantly higher risk. The mortgage

insurance industry itself uses risk based pricing, as evidenced by a

premium structure based on LTV, product type, and level of coverage.

In the card industry, the practice of different credit card annual

percentage rates (APR) for those of varying risk are commonplace. The

metrics used in risk based pricing structures are being replaced by

scoring technology. The score based pricing mechanism has been used

for some time at a somewhat coarse level in the card industry,

whereas it is only beginning to transform the mortgage industry.

43

The basic premise is the same for score based and risk based

pricing-provide a more competitively priced product to the deserving

consumer by reducing the cross subsidization of losses and expenses,

while better stabilizing the return. Score based pricing algorithms more

accurately support multiple pricing tranche, each of which is

independently priced for the target return.

Many factors go into a score based pricing algorithm beyond the

expected loss level for the score tranche. The factors include, but are

not limited to, the volatility of performance of the score tranche, its

unique capital reserve requirement, specific servicing cost, cash flow,

and other product-specific characteristics differing by score trance,

such as attrition rate for the card product and prepayment rate for the

mortgage product. Basically the bank has to comprehensively consider

all the factors that will determine the future value of the asset.

Essentially, the bank is valuing each score tranche independently. An

optimal score based pricing algorithm should also fully integrate the

secondary market or “execution” price of the individual score tranche

in a real time fashion.

7.3 Pattern Classification Systems

A pattern classification system is a method for deciding to which

of several discrete classes an observation should be assigned, based

on measurements of the observation’s characteristics. For credit risk

pattern classification systems, the measurements are made on

variables that determine credit risk – characteristics like number of

previous delinquencies fro the borrower or loan-to-value ratio of a loan.

The important credit risk pattern classification systems having only two

classes are: acceptable risk and not acceptable risk. The two class

44

system will allow us to discuss all the types of choices that need to be

made in statistical modeling. Credit risk modeling can involve multiple

classifications. A hypothetical set of five classes might be high accept,

accept, low accept, refer to underwriter, and refer to a senior

underwriter, where different actions would be undertaken based on

class.

7.4 Credit risk modeling approaches

There are different approaches to credit risk modeling. The

essential difference between various credit risk modeling approaches –

regression, rules based systems, neural networks, and case based logic

is the form of the function that relates the measurements to the

classes, the inputs to outputs, the independent variables.

The four types of approaches to credit risk modeling are:

7.4.1 Rules based Systems

7.4.2 Linear Regression

7.4.3 Non linear Regression

7.4.4 Neural Networks

7.4.1 Rules based Systems

The simplest credit risk pattern recognition system is rules based

system with a single rule, such as a system for mortgage credit risk

based only on the loan-to-value ratio. (LTV)

Rule 1: If LTV is <= b percent, accept the loan, otherwise refer.

This very simple system qualifies as a pattern classification

system because it takes the value of a variable for a particular

45

observation (a loan application) and uses it to assign the observation

to a particular class (accept or refer). The adjustable parameter, b, set

the assignments.

For every pattern classification system there is an associated

diagram showing how the system assigns the observations to the

various classes, which is called the system’s class diagram. The class

diagram of a rules-based system with one adjustable parameter is a

line, divided into two regions, one for acceptance (all points to the left

of and including the value b) and one for referral (all points to the right

of ).

Rules Based System with One Adjustable Parameter

Rules Based System with Two Adjustable

Parameters

46

Rule 2: if the LTV <= 80 percent and the DTI <= 36 percent,

accept; otherwise refer.

*DTI = Debt-to- income ratio

Rule 2 divides the space into two regions, accept and refer. The

accept region is the rectangle in the lower left hand area, bounded by

80 LTV on the horizontal axis and 36 DTI on the vertical axis. The refer

region is everything outside the accept region.

Rules based systems can be made much more flexible with more

variables and more complex rules. The variables can be continuous (a

credit score) or discrete (loan purpose, with such categories as

purchase, refinance with no cash out, or refinance with cash out). Each

variable adds a dimension to the class diagram. Adding a credit score

would require a three dimensional diagram, adding loan purpose a four

dimensional diagram, and so forth. With a two class system, however,

no matter how many dimensions, the class diagram will be divided into

two regions: accept and refer.

Rules Based System with Two Adjustable Parameters –

Compound Rules

47

Rule 3: if the LTV <=80 percent and the DTI <=38 percent,

accept;

Otherwise: If the LTV <=85 percent and the DTI <=36 percent,

accept;

Otherwise: If the LTV <=90 percent and the DTI <=34 percent,

accept;

Otherwise refer.

This system embodies a tradeoff between the higher risk of

increased LTV and lower risk of lower DTI. In principle, it is possible to

build as elaborate a system of rules as required for any degree of

flexibility.

7.5.2 Linear Regression (Hypothetical)

Linear regression is an improvement over pure rules-based

systems. The linear regression shown in below figure has a single

independent variable, LTV, and a single dependent variable, the

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proportion of losses in each LTV category. Because it does not

separate the data into discrete classes, the regression line by itself is

not a complete pattern recognition system. Rather it creates a function

that relates the probability of a loss to the LTV. This function can be

combined with a rule to create a pattern recognition system that

accepts or refers the loan.

The regression based system’s ability to handle trade off’s

among variables becomes apparent when we increase the number of

variables. The linear regression will generally create an acceptance

region that is triangular in shape, rather than a set of boxes. The

regression equation is also simple in form than compound rules.

The general form of regression equation is:

1. P = a + b * LTV + c * DTI

The fact that the acceptance region is a triangle show that there is a

risk trade-off between LTV and DTI. The boundary line is the set of all

DTI/LTV combinations that have a risk of 3.5 percent:

2. DTI = ((0.035 – a) – b * LTV)/c = (0.035 – a)/c - (b/c) * LTV

The lower of the DTI, the higher the LTV that can be accepted to

achieve the same level of risk.

Linear Regression (Hypothetical)

Probability of Loss vs. LTV

Accept if p<=0.03 Refer if p>0.03

49

Pro

bability

of Lo

ss

LTV

Linear Regression (Hypothetical)

7.5.3 Non Linear Regression

A non linear regression fits a curve rather than a line to a set of

observations. The curve may take virtually any form that can be

described by a function. The below given figure shows a power curve

fitted to the same set of points that was used for the linear regression,

probability of a loss versus LTV. When the power curve is fitted to the

previous LTV/DTI data, the accept-refer region again shows a trade-off

between LTV and DTI, but this time with an important difference. As

with the linear regression, the curve slopes downward, showing that

the higher the LTV, the lower must be the DTI in order to maintain a

constant probability of loss. But now the boundary line separating the

accept from the refer region is curved, bowed out, reflecting a more

complex relationship between DTI and LTV.

Non linear regression can take infinity of forms. One form used in

credit risk modeling is the logistic or sigmoid curve. Many applications

50

in credit risk consist of binary data; an event takes place or it does not

– foreclosure or no foreclosure, delinquency or no delinquency.

Generally, no curve will fit binary data close to perfectly, soothe

problem is how to fit a curve to the probability of the event. In this

case, it is desirable to have the curve be constrained to values

between 0 and 1, so that the estimated probability does not take o an

impossible value.

Power Function vs. Linear Regression (Hypothetical)

Probability of Loss vs. LTV

0.0

6

0.0

5

0.0

4

0.0

3

0.0

2

0.0

1

0 20 40 60 80 100 120

LTV

Accept if p<=0.03 Refer if p>0.03

LTV

51

Linear Regression Non linear regression

Pro

bab

ility o

f Loss

7.5.4 Neural Networks

Neural networks are pattern classification systems whose

structures in suggested by the interconnection of neurons in the

human brain. The below given figure shows a single artificial neuron

that takes a weighted sum of the inputs and indexes it with a value

from 0 to 1, using a logistic function. In a neural network, there are one

or more layers of neurons with outputs from one layer forming the

inputs for the subsequent layer. Layers between the input and output

layers are considered hidden from general view. These interconnected

neurons form a system that can model highly non-linear processes

with complex interactions among the variables.

Artificial Neuron – 3 Inputs, Logistic Transfer

Function

Weights

a1

a2

a3

The simplest neural network system – a

single neuron – is, in fact, essentially a logistic regression with additive

inputs. The flexibility of neural network derives from combining inputs

and outputs from many logistic curves. The potential flexibility of

regression derives from the use of non-linear functional forms,

transformed variables, and interaction terms.

Neural Network – 3 Inputs, 1 Hidden Layer with 2 Neurons

52

Input 1

Input 2

Input 3

Logistic Function

OfWeightedAverage

OfInputs

Output

53

8. Conclusion

Lending which is the most important function in any bank

involves assessing the creditworthiness of a customer. It involves

financial and non financial analysis. Ratio analysis and cash flow

forecasting is a financial measure whereas non financial analysis

include economy analysis, industry analysis and business analysis.

Banks use different credit analysis models such as traditional

credit analysis, numerical scoring model, sequential credit analysis,

pattern recognition systems etc. Statistical tools like discriminant

analysis, regression analysis, linear and non linear regression are used

to a great extent for credit analysis.

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Bibliography

Reference Books & Articles

Credit Risk Management – Andrew Fight

Developing and Applying Credit Risk Models – Elizabeth Mays

Financial Management Theory and Practice – Prasanna Chandra

RBI guidelines

Papers

The Economic Times of India

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Business Standard

World Wide Web

www.rbi.org.in

www.bankersacademy.com/riskmanagement.php

www.iba.org.in

www.bis.org

www.financialinvestmentplanner.com/

Economy_Microeconomics_Lending-models.html

www.gdrc.org/icm/model/1-credit-model.html

http://www.frbsf.org/econrsrch/workingp/wp99-06.pdf

www.gdrc.org/icm/model/1-credit-model.html

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