Final Research (2)

The year 1988 marked a round turn in the Vietnamese economy, which concerned initiating the economic transformation from the former centrally planned to a market economic oriented system with state management.

Chapter 1 Introduction

1.1 Rationale

The year 1988 marked a round turn in the Vietnamese economy, which concerned initiatingthe economic transformation from the former centrally planned to a market economicoriented system with state management

To cope with the new environment, the banking sector started its deregulation This isfeatured in the transformation from a uni-type to two-tier system, wherein the State Bank ofVietnam play the role of cash issuer and controller of commercial banking, and other bankswork toward establishing a true business platform

During 1988-1990, the commercial banking business was ‘booming’ in terms of the number

of new banks established and in lending activities However, due to the poor quality inoperation and insecurity, nearly all credit cooperatives and joint stock banks came tobankruptcy Billions of dong were frozen in bad debts within state owned banks and asassets in shareholding banks which still operated

To overcome this situation, one requires a suitable credit policy to:

 Extend credit to every economic sector, public and private; including households

 Diversify funding instruments in light of market developments

 Restructure bank loans with further emphasis on medium and long term credit ratherthan short term lending

 Enhance credit quality, and strictly manage credit risks

 Diversify the credit market for banks

In order to perform this credit policy, Vietnamese banking system initiated computerization,and currently, payments and statistics have been computerized By the end of 1993, banksset up LAN internally In the coming years, the following steps will be taken:

 Computer link between State Bank of Vietnam and banks

 Computer link among banks

 Computer link between banks and customers

Along with computerization, different types of advanced payment facilities are graduallyintroduced; like credit cards, and ATM installation to serve residents and foreign travelers

In that situation, efficiency and effectiveness are critical for the success of the bankingindustry They are not only dependent upon managerial skills but also the adoption of newtechnology.

To control credit quantity and quality, it is necessary to have a new technology to enhancecredit quality, and manage credit risks Determining which loan applicant should beextended credit, as well as the amount of credit, are major practical decisions that confrontcommercial bank lending officers, credit analysts, and loan committees These decisionmakers must assess the financial health of an applicant, which requires analysis of bothquantitative and qualitative information on the outlook of the company To correctly performthe analysis, commercial loan officers need to have an understanding of the primary Cs oflending: credit, collateral, capital, capacity, and character As commercial loan officersevaluate a company, they examine numerous financial ratios, percentages, and trends, andperform many interrelated analyses Because the complex nature of the problem requiresexperience and precludes the use of simple algorithms, expert systems are appropriate forthis type of loosely structured, complex problem

An expert system is a software system that imitates the reasoning results of human experts

in a well defined domain It aims to generate advice about problems in the domaincomparable to the advice that a human expert would deduce for the same problems Inrecent years, practitioners have become more familiar with the technology and thetechnology has advanced to become more supportive of business applications

Recent financial applications of expert systems include:

 Decision analysis in securities trading,

 Cashflow analysis, and

 Venture capital analysis for small telecommunication companies

Another problem area that can benefit from this technology is commercial loan analysis, theprocess of evaluating a company’s financial strengths and weaknesses

1.2 Objective

The purpose of this research is to design an expert system that helps commercial banklending officers, credit analysts, and loan committees to reduce the time devoted to theanalysis in evaluating loan applicants and to improve the quality of the evaluation

1.3 Scope of the research

The system is designed to analyze commercial loans for industrial or retail borrowers Thesystem only focuses on the credit granting decision and does not consider other aspects ofthe credit granting decision process such as keeping track of collateral maturities, collectionfollow-up, etc In addition, the system does not provide a specific evaluation of economic orcompetitive factors within a given industry

Chapter 2 Literature Review

2 1 Financial Perspective

Automation in Banking procedures has been evenly spread out over the last decades withthe advent of fast computing machines Automation in banking began with the use ofcomputers for performing rote clerical tasks and moved on rapidly to automatic transactionprocessing Soon, a need was felt for using computers for making managerial decisions, orassist the upper management in making such decisions One of the departments i.e creditdepartment felt the urgent need for the same and systems based on statistical, expertsystems began to develop and are currently in use The search for better and efficientmethods for making the credit decisions continues in the pursuit of perfection The dream ofall lending institutions for a completely reliable, efficient automated procedure for evaluation

of prospective credit applicants continues to remain a dream

Tamisin (1991) has created a model of the loan negotiation process based on case-based

reasoning process The prototype system designed by the author makes use of previousexperiences to guide the problem solving process The author gave an introduction to theloan negotiation process by first defining clearly the meaning of negotiation and thendescribed the phases or the life cycle of the negotiation process as divided into three

stages Loan negotiation consists of three phases (Marsh, 1984) which are shown in the

figure below:

PREPARATION and SUBMISSION

CREDIT CONDITION NEGOTIATION ANALYSIS and EVALUATION

Figure 2.1 Loan Negotiation Phases

Preparation and Submission Phase: This phase involves the preparation of the applicant's

application using the project proposal, financial report and loan requirements

Analysis and Evaluation phase: This phase takes the output of the last phase, the financial

application and uses it as the input It gives the overall loan application's status as output.This phase involves the analysis and evaluation of the financial report, project proposal andthe loan requirements After this, the credit history of the applicant is thoroughly investigated

and the overall application status is provided The status signifies the decision for approval,rejection or negotiation of the loan

Credit Condition Negotiation Phase: If the applicant is a corporate customer, a negotiation is

generally required This phase is invoked when a talk between the two parties is called for.The inputs to this phase are the analysis status and loan requirements During thenegotiation process, one negotiating agent proposes a set of conditions If the second agentagrees on this set of conditions, then an agreement is reached Otherwise, the secondagent proposes its own set of conditions This process is iterated until an agreement or adeadlock situation is reached

Bryant (1962) gives the entire working of the mortgage department of a lending institution.All the terms and paraphernalia related to the Credit Division are outlined brilliantly.However, the drawback with the book was that of it’s outdation As it was written in the early1960’s, most of the policies and workings of the credit department have undergoneappreciable changes and modifications, as of today The second drawback of the book isthat, it covers only Home Bank Loans, and strictly remains in that domain

Zinkhan (1990) earlier study had indicated the principles of credit require only 'Five Cs' i.e.

capacity, capital, character, collateral, and conditions - in relationship to the evaluation of agiven firm's credit risk This paper suggests the 'Sixth C' of Credit and calls it the 'CustomerProfitability Analysis' A number of efforts have been undertaken to quantify and summarizetwo of these five C's for the purpose of estimation of the bankruptcy of firms: capacity andcapital Notable among these efforts are the Z-score model of Altman (1968), the Zetaanalysis model of Altman et al (1977), and the application of a discriminant analysis model

to small businesses by Edminister (1972) In addition, a credit-scoring model wasdeveloped by Chesser (1974) to determine the creditworthiness of a potential business loancustomer According to the author, the above mentioned quantitative models ignore thecharacter, collateral, and conditions dimensions of credit risk analysis The purpose of thistechnique is to jointly evaluate the objective and subjective estimations of the six C's in order

to generate an overall indicator of the relative attractiveness of a given potential businessloan A hierarchical model of the business loan evaluation process was also suggested asshown in Figure 2.2

Acharya (1990) has designed and tested a Loan Negotiation System based on a rule

based system “Negotiation is a dynamic process of adjustment by which two or more

parties, each with his own objectives, confer together to reach a mutually satisfyingagreement on a matter of common interest The process of negotiation may end with orwithout a consensus” According to the author, Negotiation is oriented toward the future, itsprogress is defined by the negotiating agents It is also oriented with the sole goal that eachparty tries to obtain maximum payoff

O v erall In d ic ato r o f L o a n A ttractiv en ess

C h a ra cter R isk In d icato r o f C o lla teral R isk In d icato r o fC o n d itio n s R isk

Figure 2.2 Hierarchical Model of the Loan Evaluation process

The five phases through which every negotiation proceeds are: exploration, bidding,bargaining, settling, and ratifying Usually, they may not follow sequentially and thenegotiators may alter the sequence of the phases

The negotiators may follow the sequence on one aspect of the deal and then start all overagain on a second aspect

Duchessi et al (1988) describe a knowledge-Engineered system for Commercial LoanDecisions The paper initially describes the Commercial Loan Analysis process which covers

a general and a detailed analysis This is followed by a complete description of the expertsystem 'Commercial Loan Analysis Support System (CLASS)' This expert system isdescribed as one which is designed to evaluate a company's financial posture, recommendcommercial loan decisions and pertinent covenants, and document the loan analysis

Commercial Loan Analysis process as described by the authors examines numerousfinancial factors to uncover a company's financial weaknesses Their evaluation begins with

a general analysis involving an examination of key financial trends and factors When one ofthose factors does not meet the industry norm, commercial loan officers must perform amore detailed analysis to uncover the causes

General Analysis: It consists of trend analysis, and separate analyses of credit, collateral,

capital and capacity General trend analysis provides loan officers with a quick indication of

a company's performance in several key areas: sales, operating income, net income, sellingand administrative expenses, working capital, and cash flow Credit analysis measures acompany's ability to repay its short and long-term obligations The credit analysis reallyconsists of efficiency, profitability, and liquidity analyses Inventory turnover, receivablesturnover, fixed asset turnover, and total asset turnover are the primary efficiency measures.Profitability analysis considers operating margin, profit margin, return on assets, and return

on equity, while liquidity analysis includes the current and quick ratios Collateral analysisexamines the relationship between the value of all assets and pledged assets To estimatetheir value, loan officers appraise book value, age, and condition of assets Capital analysis

provides an indication of a company's leverage position Long term debt to total assets, totaldebt to total assets, interest coverage, and fixed charge coverage are the primary factors Ifall four measures are above industry standards, a company's capital position is strong Ifthey are all below, capital is poor Capacity analysis measures the degree to which a loancan be supported by a company, using the same ratios as capital analysis

Detailed Analysis: This is performed whenever a general analysis indicates a weakness As

problem areas are identified and examined, the loan officers accumulate loan covenants, orrestrictions, which become part of the final loan agreement

Gilliam (1990) describes the financial analysis procedure for the estimation of lending riskwhich can be calculated using tangible factors or ratios The terms and ratios describedcorrespond to the derivable ratio's that are related to the 'Five Cs' of credit The authorstates that the financial analysis can provide valuable information about why a companyneeds to borrow, what the loan will fund, whether operations will be able to generatesufficient cash flow to repay debt, and whether company assets will be available ascollateral If the borrower has met the initial criteria for a desirable customer throughinterviews and credit investigation, the next place to begin risk analysis is with thecompany's financial statements The degree of reliance on financial analysis is directlyrelated to how they are prepared In defining lending risk, there are three general areas ofinvestigation relating to financial statements

Economic Condition, which measures the company's leverage, liquidity, and activity

positions;

Profitability, which addresses break-even analysis and other trends in company sales and

expenses; and

Cash Flow, which assists in determining sources and uses of cash to pinpoint why a

company is borrowing and whether the loan can be repaid

Lenders have access to several tools to analyze these areas Trend analysis focuses on acompany's direction regarding assets, liabilities, revenues, and expenses Trend analysisfocuses on a company's direction regarding assets, liabilities, revenues, and expenses.Comparing management's plan to actual performance highlights management's ability toforecast and plan for future events

The lender's primary purpose is to make loans that can be repaid while minimizing thebank's exposure to loans with poor credit quality To be successful, it is important for lenders

to assess areas of relative strength and weakness by reviewing the economic condition,profitability, and cash flow of a company using tools such as trend analysis and industrycomparison

Harris (1994) gives the fundamentals of trading describing it as a zero-sum game when

measured relative to underlying fundamental values This paper classifies traders into threecategories winning, utilitarian and futile This paper then throws light on the origins oftrading profits , how the contributions of the three categories affect the price efficiency andmarket liquidity

2.2 Existent Systems

Holsapple et al (1988) throw light on the adaptation of expert system technology to financial

management The paper describes the rudimentary architecture of expert systems,recognition of the potential of expert systems by the financial markets and their acceptabilityand impact The conclusion made by the paper is that current technology is inadequate forapplications requiring insight, creativity, and intuition According to the author, an expertsystem is a software system that imitates the reasoning results of human experts in a welldefined domain It aims to generate advice about problems in the domain comparable to theadvice that a human expert would deduce for those same problems

Table 2.1 Expert Systems for Financial Applications of ' Commercial Loan Analysis'

Authorizer's Assistant American Express Credit Authorization

Financial Analyzer Athena Group Commercial Loan ApprovalLending Advisor Syntelligence Credit Analysis

Mortgage Loan Analyzer Arthur Anderson Mortgage Loans EvaluationUnderwriting Advisor Syntelligence Commercial Underwriting

Marble (1988) ( Managing and Recommending Business Loan Evaluation ) : is an expertsystem with inductive learning to evaluate Business Loans The paper first describes theloan evaluation process and the basic difficulties that confront a loan decision The paperthen describes the design and construction of Marble

Countrywide Loan-Underwriting Expert System (CLUES) (1991): The paper gives the lifecycle of a loan and then describes the planning, construction and working principles ofCLUES An important aspect covered in this paper is the enumeration of the reasons as towhy the rule based methodology was used in CLUES The drawbacks and advantages ofthe various AI technologies were also touched upon

Mortgage Risk Evaluator (MRE) (1962): Nestor, one of the earliest neural networkcompanies, has a product that appraises mortgage applications The system was trained onseveral thousand actual applications, about half of which were accepted and the other half

of which were rejected by the human underwriters Learning from the successes and failures

of this body of experience , the system looks for patterns in the data to determine whatconstitutes a bad risk AVCO Financial Services , in Irvine, California, uses this neuralnetwork system for Credit Risk Analysis The system was trained on more than 10,000 creditcase histories A New York Times edition reported that one test indicated there would havebeen a 27 percent increase in profits if the neural network system had been used instead ofthe computerized evaluation system previously used by AVCO Besides credit risk analysis ,other commercial applications of neural networks in Finance include Identification offorgeries, interpreting handwritten forms, rating investments and analyzing portfolios

Neuroforecaster is an advanced windows based, user-friendly, intelligent, neural network

forecasting tool Made by Accel Infotech (S) Pte Ltd., Singapore It is packed with the latesttechnologies including neural network, fuzzy computing and non-linear dynamics Besides

performing the Loan Analysis, it can also be used for other financial applications some ofwhich are enumerated below.

Stock Price Prediction, Stock market six monthly return forecast, Stock selection based oncompany ratios, Stock market index forecast, National GDP forecast, Sales Forecast, US$

to Deutshmark exchange rate forecast, Fraud Detection and Fault Diagnosis, Air passengerarrival forecast, Credit rating of bank loan applications, Property price valuation, BondRating, Construction demand forecast, XOR-A classical problem

2.3 Relative Comparison between Methodologies

Digiammarino et al (1991) elucidate on the gradual evolution of Artificial Intelligencetechnology in the automation of loan underwriting The paper claims that severalapplications of information technology are now experiencing rapid growth as they penetratethe middle-size and smaller-size ranks of the industry The author first describes theScoreCard, a statistical technique for credit evaluation, followed by the comparison betweenthe Statistical, AI and Neural Network methodologies for Loan underwriting

As per the author, the Scorecard is the most familiar kind of decision support system in

consumer credit and dates back to the 1960's or earlier Derived from statistical analysesthat correlate application and credit report data to each lender's actual loan losses, thesemodels accurately measure the probability of each applicant defaulting The score iscalculated by the application processing system and used along with collateral and creditpolicies to recommend a decision to the reviewing credit officer The degree to which theserecommendations are followed varies widely from one organization to another Innovativefinancial institutions' current involvement in advanced research in decision support systems

is currently moving in two dimension: profit-driven objectives and more sophisticatedmodeling techniques, including artificial intelligence The issue of profitability is so important,that leading organizations are beginning to look for ways to address it head-on in decisionsystems In addition to sharpening the objective of decision support models by incorporatingprofitability, innovators are applying more advanced techniques to build the modelsthemselves

The author claims that classical statistical techniques are capable of producing morepowerful models than are commonly used today, but the improvement is only marginalcompared to the difficulty of implementing more complex equations As a result, a great deal

of attention is being devoted to the use of artificial intelligence in consumer credit

Neural networks are potentially very valuable tools for the credit industry in situations wherethese factors occur together: large amounts of data, complex interactions, quick feedback

on the results of each decision, and a lack of human constraints Expert systems take theopposite approach, eschewing hard data in favor of the judgment of human experts Aweakness is that expert systems do not make use of hard data even when such informationcould enhance human judgment In these instances ( for example, credit applicationscreening ) , the expert system can be modified to include a risk measure as part of the finalrecommendation The key difference between a classical statistical model and a neural net

is that the net typically analyzes a larger number of mathematical forms for the relationshipsbetween predictive variables This procedure can lead to better predictive ability when thepredictors work in complex but stable and well-defined ways

Eyden (1994) This paper compares the use of artificial neural networks (ANNs) and multiple

discriminate analysis (MDA) in the prediction of credit risk It clearly brings out thecomparison between statistical modeling versus neural networks in financial decisionmaking MDA is traditionally regarded as the most applicable statistical method for theprediction of credit risk and is widely used by financial institutions and other organizations.MDA is based on a linear equation and has the limitation of not being appropriate for non-linear data ANN's on the other hand, incorporate both linear and nonlinear components andtherefore, according to the paper, prove that they are more suitable for different data types.The paper presents a case study of financial application (credit scoring problem) to measurethe comparative performances of the two models The findings of the paper indicate that

ANN's outperform MDA in the forecasting of credit risk

Costantino (1991) gives out a very useful eight-point procedure for determining whetherexpert systems and neural networks are in fact superior to current credit-scoring processes.The paper says that, although the framework of this procedures is loosely defined, itaddresses the full range of issues that confront creditors in the adoption of newtechnologies The paper draws a clear cut method of evaluating the superiority or inferiority

of the AI technology over the statistical methods for credit risk analysis According to theauthor, the evaluation of new technologies requires an information base that considers basicbusiness components-economic returns, people, systems, and control The impact on thesebasis entities can be best understood by addressing the eight issues as given below:

The cost of new technologies vs the old: This calculation depends on the number of

credit-scoring systems requiring redevelopment and the configuration of each of the newtechnologies

Thorough understanding of the current process: Answers to questions like "Which

applicants are rejected/ accepted at high rates? " ; " Which applicant profiles are accepted

as often as they are rejected? "; for each credit-scoring system in use helps to understandthe nature of the incoming populations and how the current process selects new accounts

Relationship of the approval rate to the bad rate: This relationship can be developed by

analyzing a sufficiently large random sample of good performers, bad performers, andrejects Knowing the relationship between approval rates and bad rates helps to determinethe size and potential risk associated with the applicants most likely to be affected by thenew technology

Long term cost-return tradeoffs: To evaluate this, a summary table is drawn from the last

three evaluation steps and ratios are calculated

Service Level maintenance or enhancement: Whether the new technology will cause

decision turnaround times to increase to unacceptable levels or improve This can be testedduring the test-benchmark step Real-time environments can significantly degrade theperformance of technologies that were designed as batch systems

The Readiness of organization to manage the development and implementation of the new technology: This step is the most qualitative one to evaluate

The firm's data processing capacity: This can be found by the answers to questions like "Are

there enough programmers available to code and test the required programs ?"; "Will newhardware have to be acquired in order to implement the new technology ?" ; " Will newhardware and interfaces need to be acquired ?"

The technology's auditability and security capability: Auditability comes in two

flavors-applicant and technology Auditability is key to maintaining system controls Security is thekey to maintaining competitive advantage and to preventing fraudulent transactions

Chapter 3 Theoretical consideration

3.1 Credit analysis: what make a good loan?

Credit Department must satisfactorily answer three major questions regarding each loanapplication:

1 Is the borrower creditworthy? How do you know?

2 Can the loan agreement be properly structured and documented so that the bank and itsdepositors are adequately protected and the customer has a high probability of beingable to service the loan without excessive strain?

3 Can the bank perfect its claim against the assets or earnings of the customer so that, inthe event of default, bank funds can be recovered rapidly, with low cost and low risk?

3.1.1 Is the borrower creditworthy?

The question that must be dealt with before any other is whether or not the customer canservice the loan - that is, pay out the credit when due, with a comfortable margin for error.This usually involves a detailed study of six aspects of loan application - character, capacity,cash, collateral, conditions, and control All must be satisfactory for the loan to be a goodone from the lender’s point of view

 Character The loan officer must be convinced that the customer has a well-defined

purpose for requesting bank credit and a serious intention to repay Once the purpose isknown, the loan officer must determine if it is consistent with the bank’s current loanpolicy Even with a good purpose, however, the loan officer must determine that theborrower has a responsible attitude towards using borrowed funds, is truthful inanswering the bank’s questions, and will make every effort to repay what is owed.Responsibility, truthfulness, serious purpose, and serious intention to repay all moneysowed make up what a loan officer calls character

 Capacity The loan officer must be sure that the customer requesting credit has the

authority to request a loan and the legal standing to sign a binding loan agreement Thiscustomer characteristic is known as the capacity to borrow money

 Cash Does the borrower have the ability to generate enough cash, in the form of

income or cash flow, to repay the loan? In general, borrowing customers have only threesources to draw upon to repay their loans: (a) cash flows, (b) the sale or liquidation ofassets, or (c) funds raised by issuing debt or equity securities However, bankers have astrong preference for cash flow as the principal source of loan repayment because assetsales can weaken a borrowing customer’s balance sheet, while additional borrowing by aloan customer can make the bank’s position as creditor less secure Moreover, shortfalls

in cash flow are common indicators of failing businesses and troubled loan relationships.The loan officer’s evaluation of a borrower’s cash involves asking and answering suchquestions as: Is there a history of steady growth in earning or sales? Is there a highprobability that such growth will continue to support the loan?

 Collateral Does the borrower posses adequate net worth or own enough quality assets

to provide adequate support for the loan? The loan officer is particularly sensitive to suchfeatures as the age, condition, and degree of specialization of the borrower’s assets.Technology plays an important role here as well If the borrower’s assets aretechnologically obsolete, they will have limited value as collateral because of thedifficulty of converting them into cash if the borrower’s income falters

 Conditions The loan officer and credit analyst must be aware of recent trends in the

borrower’s line of work or industry and how changing economic conditions might affectthe loan A loan can look very good on paper, only to have its value eroded by decliningsales or income in a recession or by the high interest rates occasioned by inflation Toassess industry and economic conditions, most banks maintain files of information -newspaper clippings, magazine articles, and research reports - on industries represented

by their major borrowing customers

 Control Whether changes in law and regulation could adversely affect the borrower and

whether the loan request meets the bank’s and the regulatory authorities’ standards forloan quality

3.1.2 Can the loan Agreement be properly structured and documented?

The loan officer is responsible to both the customer and the bank’s depositors andstockholders and must seek to satisfy the demand of all This requires, first of all, thedrafting of a loan agreement that meets the borrower’s need for funds with a comfortablerepayment schedule The borrower must be able to comfortably handle any required loanpayments, because the bank’s success depends fundamentally on the success of itscustomers If a major borrower gets into trouble because it is unable to service a loan, thebank may find itself in serious trouble as well

A properly structured loan agreement must also protect the bank and those it represents principally its depositors and stockholders - by imposing certain restrictions on theborrower’s activities when these threaten the covery of bank funds The process ofrecovering the bank’s funds - when and where the bank can take action to get its fundreturned - also must be carefully spelled out in the loan agreement

-3.1.3 Can the bank perfect its claim against the borrower’s collateral?

The collateral pledged behind a loan and the other assets that a borrower may own are thesecond line of defense against loan default, after the borrower’s cash flow When theborrower’s cash flow or income falters, the lender must look to the borrower’s assets.Therefore, the key issues for any lender include whether or not the bank can get clear title toany assets that are available to backstop the loan, which creditors have priority of claim if aborrower’s assets must be liquidated to cover a loan, and whether the borrower has

assigned the bank exclusive interest in certain assets or has pledged those assets tosomeone else An important technical issue here - crucial in mortgage lending - is whetherdeeds to property have been properly filed with local governmental authorities so that thebank knows for sure who currently has title to the property If a home owner is borrowingmoney and using his or her home as collateral, the loan officer must verify not only that thecustomer has title to the home but also whether other lenders have legitimate claims againstthat property.

3.2 Financial ratio analysis of a customer’s financial statements

The calculation of financial ratios provides the basis of most technical, quantitative creditanalysis Information from balance sheets and income statements is typically supplemented

by financial ratio analysis By careful selection of items from a borrower’s balance sheetsand income statements, the loan officer can shed light on such critical areas in businesslending as

1 A borrowing customer’s ability to control expenses;

2 A borrower’s operating efficiency in utilizing resources to generate sales and cash flow;

3 The marketability of the borrower’s product line;

4 The coverage that earnings provide over a business firm’s financing cost;

5 The borrower’s liquidity position, indicating the availability of ready cash;

6 The borrower’s track record of profitability or net income;

7 The amount of financial leverage a business borrower has taken on; and

8 Whether a borrower faces significant contingent liabilities that may give rise tosubstantial claims in the future

3.2.1 The business customer’s control over expenses

How carefully a business firm monitors and controls its expenses is a barometer of thequality of its management and how well its earnings are likely to be protected Selectedfinancial ratios usually computed by loan analysts to monitor a firm’s expense controlprogram include the following:

Wages and salaries / net sales

Overhead expenses / net sales

Depreciation expenses / net sales

Interest expense on borrowed funds / net sales

Cost of goods sold / net sales

Selling, administrative, and other expenses / net sales

Taxes / net sales

3.2.2 Operating efficiency: measure of a business firm’s performance

How effectively are assets being utilized to generate sales and cash flow for the firm andhow efficiently are sales converted into cash? Important financial ratios here are

Annual cost of goods sold / average inventory (or inventory turnover ratio)

Net sales / total assets

Net sales / net fixed assets

Net sales / accounts and notes receivable

average collection period = Accounts receivable / Annual credit sales / 360

In general, the higher a firm’s inventory ratio, the better it is for banks and other creditors,because this ratio shows the number of times during a year that the firm turns over itsinvestment in inventories by converting those inventories into goods sold When theinventory turnover ratio is too low, it may indicate poor customer acceptance of the firm’sproducts or ineffective production control and inventory control policies Too high aninventory turnover ratio could reflect underpricing of the firm’s product or inadequate stocks

of goods available for sale, with frequent stockouts, which drives customers away

The ratio measuring turnover of fixed assets indicates how rapidly sales revenues are beinggenerated as a result of using up the firm’s plant and equipment to produce goods orservices If the fixed-asset turnover ratio falls, this may indicate the firm has invested tooheavily in plant and equipment, given the strength of current market demand for its product,and thus has substantial productive capacity that isn’t being used Alternatively, a fixed-asset ratio that is too high would lead the analyst to believe the firm has not devoted enough

of its resources to increasing or upgrading its physical plant in order to achieve greaterefficiency and productivity

The collection period ratio reflects the firm’s effectiveness in collecting cash from its creditsales and provides evidence on the overall quality of the firm’s credit accounts Alengthening of the average collection period suggests a rise in past-due credit accounts andpoor collection policies

3.2.3 Marketability of the customer’s product, service, or skill

In order to generate adequate cash flow to repay a loan, the business customer must beable to market goods, services, or skills successfully A bank can often assess publicacceptance of what the business customer has to sell by analyzing such factors as thegrowth rate of sales revenues, changes in the business customer’s share of the availablemarket, and the gross profit margin (GPM), defined as

GPM = (Net sales - cost of goods sold) / Net sales

A closely related and somewhat more refined ratio is the net profit margin (NPM):

NPM = Net income after taxes / Net sales

The GPM measures both market conditions - that is, demand for the business customer’sproduct or service and how competitive a marketplace the customer faces - and the strength

of the business customer in its own market, as indicated by how much the market price ofthe firm’s product exceeds the customer’s unit cost of production and delivery

The NPM, on the other hand, indicates how much of the business customer’s profit fromeach unit of sales survives after all expenses (including taxes) are deducted, reflecting boththe effectiveness of the firm’s expense-control policies and the competitiveness of its pricingpolicies.

3.2.4 Coverage ratio

Coverage refers to the protection afforded creditors of a firm based on the amount of thefirm’s earnings The best-known coverage ratios include the following:

Interest coverage = Income before interest and taxes / Interest payments

Coverage of interest and principal payments = Income before interest and taxes /

(Interest payments + principal repayments/(1 - Firm’s marginal tax rate))Coverage of all fixed payments = (Income before interest and taxes + Lease

payments) / (Interest payments + Lease payments)The interest coverage ratio indicates the margin of safety that earnings provide creditors inrelation to interest charges

The coverage of all fixed payment simply extends the interest coverage ratio to account forcontractual commitments under leasing agreements

3.2.5 Liquidity indicators for business customers

The borrower’s liquidity position reflects his or her ability to raise the cash in timely fashion atreasonable cost, including the ability to meet loan payments when they come due Popularmeasures of liquidity include the following:

Current ratio = Current assets / Current liabilities

Acid-test liquidity ratio = (Current assets - Inventories) / Current liabilities

Net liquid assets = Current assets - Inventories of raw materials or goods -

Current liabilitiesNet working capital = Current assets - current liabilities

An individual or institution is liquid if it can convert assets into cash or borrow immediatelyspendable funds precisely when cash is needed Liquidity is, therefore, a short-run concept

in which time plays a key role For that reason, most measures of liquidity focus on theamount of current assets (cash, marketable securities, accounts receivable, inventory,prepaid expenses, and any other assets that normally roll over into cash within a year’s time)and current liabilities (accounts payable, notes payable, taxes payable, and other short-termclaims against the firm, including any interest and principal payments owed on long-termdebt that must be paid during the current year)

The current ratio indicates the extent to which the claims of short-term creditors are covered

by assets that can be readily converted into cash without loss High current ratios suggest ahigh margin of safety for short-term creditors However, the ratio does not considerdifferences in the quality of receivables and inventories

Concern over the quality of liquidity of inventories is purged in the quick ratio Only the

“quick” assets of cash, marketable securities, and receivables are included For manyindustries in which inventory values may be suspect, the quick ratio is a more reliablemeasure of liquidity than the current ratio

3.2.6 Profitability indicators

The ultimate standard of performance in a market-oriented economy is how much netincome remains for the owners of a business firm after all expenses (except stockholderdividends) are charged against revenue Most loan officers will look at both pre-tax netincome and after-tax net income to measure the overall financial success or failure of aprospective borrower relative to comparable firms in the same industry Popular bottom-lineindicators of the financial success of business borrowers include the following:

Before-tax net income: Total assets, net worth, or total sales

After-tax net income: Total assets, net worth, or total sales

Return on equity = Net income available to common stock / common stock equityReturn on assets = Net income after tax /Average total assets

Profit margin = Net income after tax / Net sales

Return on equity is a summary measure of how effectively common stockholders’ fundshave been employed, including the effectiveness of the use of financial leverage

Return on assets indicates the efficiency with which management employed the total capitalresources available to it It is a better measure of operating performance than return onequity because the latter is effected by the degree of financial leverage

Profit margin measures the profit per currency unit of net sales Its complement (1 - profitmargin) indicates the expense incurred to generate one currency unit of revenue andreveals the effectiveness of cost controls and pricing policies

3.2.7 The financial leverage factor as a barometer of capital structure

The term financial leverage refers to the use of debt in the hope that the borrower cangenerate earnings that exceed the cost of debt, thereby increasing the potential return to abusiness firm’s owners Key financial ratios used to analyze any borrowing business’s creditstanding and use of financial leverage are as follows:

Leverage ratio = Total liabilities / Total assets

Capitalization ratio = Long-term debt / Total long-term liabilities and net worth

Debt-to-sales ratio = Total liabilities / Net sales

The greater the amount of indebtedness a borrowing customer has already taken on, otherfactors held equal, the less well secured is any particular lender’s position The higher theleverage ratio becomes, the less likely it is that additional loans will be granted to a customeruntil he or she pays down some of the outstanding indebtedness Moreover, if a loan isgranted to a highly leveraged borrower, it is likely to carry a higher interest rate plus arequirement that more collateral be pledged

The capitalization ratio focuses upon the business customer’s use of permanent financing,essentially comparing the degree to which the firm is supported by long-term creditors asopposed to its owners’ equity capital (net worth) Business debt can also be linked tobusiness sales, because those sales ultimately provide the funds needed to retire the debt.

If a firm’s liabilities increase relative to its sales, management will have to compensate forthe heavier debt burden by either finding less expensive sources of credit or loweringexpenses so that more sales revenue reaches the firm’s bottom line of net income

3.2.8 Contingent liabilities

Usually not shown on customer balance sheets are other potential claims against theborrower that the loan officer must be aware of, such as

1 Guaranties and warranties behind the business firm’s products

2 Litigation or pending lawsuits against the firm

3 Unfunded pension liabilities the firm will likely owe to its employees in the future

4 Taxes owed but unpaid

3.3 Commercial Loan Decision Making Process

In practice, the evaluation of a loan application is based on the information presented infinancial statement plus any qualitative information, such as the quality of management, theability to repay the loan, and the availability and value of collateral Frequently the qualitativeinformation is of greater value in the lending decision than the financial statement analysis.Exhibit 1 presents the decision-making process for evaluating commercial loans Exhibit 1represents a generic overview of the lending process and was an underlying framework inthe designing of the Expert System The evaluation of a firm’s credit worthiness is a scorethat weighs each of characteristics presented in Exhibit 1, Block  When the credit riskscore is calculated, the risk classification of the applicant is established by comparing it to anobjectively determined standard

If the loan is approved, the bank establishes the terms of the loan with the customer in order

to assure repayment The final phase of the process involves organizing all the data andinformation used in the decision process and storing it in the loan documentation file Thisfile is the basis for future performance reviews

3.4 Architecture Of Expert Systems

Keeney (1988) suggested a concept called value-driven Expert System concept which isimplements for multiattribute utility decision making in developing countries However, thisconcept requires strong background of utility function.

On the other hand, Olave et al (1988) focused on the side of Expert System that can solveproblems based on symbolic and qualitative data which often requires human thinking Furthermore, Hopsapple et al (1988) showed that Expert System can generate advice onthe same problem or similar problems which human experts have deduced before

The Expert System was stated by Ignizio (1990), shows the role of model rather thanreporting medium (i.e computer) Furthermore, Ignizio in the same paper emphasized analgorithmic method

According to Ignizio, an Expert System can be defined as a model and associatedprocedures, that exhibits the degree of expertise in problem solving, within a specificdomain

CUSTOMER OF THE

BANK?

IS ANEXTENSIVE CREDIT

CHECK ON THE FIRM

REQUIRED?

SHOULD LOAN

BE RECOMMENDED?

EVALUATE THEPOTENTIAL OF A NEWCUSTOMERRELATIONSHIP

INVESTIGATE THE CREDIT- WORTHINESS

OF THE PROPOSED LOAN

BY ANALYZING THE FIRM’S

- Quality of Financial information

- Economic Characteristics (Size, Market Share, Diversification)

- Competitive Position in Industry

- Financial Characteristics (Profitability, Liquidity, Leverage, Growth)

- Management (Quality, Experience, Depth)

- Availability of Funds (Equity or Debt Markets)

- Ability to Repay Loan (Cash Flow Analysis, Security)

- Supplier Experience

- Experience at Previous Bank

- Value of Collateral DETERMINE A CREDIT

DETERMINE THE CREDIT NEEDS OF THE CUSTOMER (PURPOSE OF LOAN, AMOUNT, MATURITY)

DETERMINE THE CREDIT NEEDS OF THE CUSTOMER (PURPOSE OF LOAN, AMOUNT, MATURITY)

LOAN MONITORING PROCESS

Timing of Payments

Value of Collateral

Compliance with Covenants

Periodic Financial Reports

THE LOAN (Type of

Financing Amount, Interest

Rate, Collateral, covenants,

Repayment)





END

In an Expert System generally the knowledge is obtained from an authority in a specific,narrow field of activity This field is called domain and the authority is called the domainexpert The program-developer or knowledge engineer interviews the domain expert andenters the factual, judgmental and procedural knowledge into the Expert System program as

a knowledge base

The conventional architecture of an expert system is shown in Figure 3.2

Figure 3.2 Generic Architecture of Conventional Expert System

The knowledge system stores application-specific reasoning knowledge about a particular

domain Each piece of reasoning knowledge specifies what conclusion is valid when aparticular situation exists Such a fragment of knowledge is commonly represented as somevariant of a production rule A set of rules pertaining to a defined problem area is called arule set and the collection of rule sets available for dealing with all problems in the expertsystem’s domain is sometimes called “rule-based systems”

In its most rudimentary form, a rule is composed of 2 parts:

 The premise consists of one or more conditions, and

 The conclusion is composed of a series of one or more actions that are to be taken asvalid if the premise is satisfied

C1 C2 Cn A1 A2 Am n,m  1

Ci is essentially a predicate, whose value at any moment is either true, false, or unknown.Here, we assume that these conditions are conjunctively related, although disjunctions arealso permissible in a rule’s premise

Inference Engine

Aj can also be denoted as a predicate When the premise is true, the truth of Aj is likewiseasserted That is, it represents an action that can be legitimately performed.

There are many diverse, yet equivalent, syntactic conventions for formally specifying a rule.Each rule in a rule set encapsulates some fragment of reasoning knowledge that can, inprinciple, be developed and modified independently of other rules

An important feature of many expert systems is their ability to cope with uncertain situations

or inexact reasoning One way to support dealing with uncertainty involves stating aconfidence factor (CF) for each rule, depicting the degree of belief or weight an expertascribes to a rule A confidence factor may be linearly related to the degree of certaintyasserted by the experts on a rule A low value, say 0.2, is interpreted as a less valid rulewhile a high value, say 0.8, is viewed as a more valid one Much more flexible andsophisticated CF means for dealing with reasoning under uncertainty exist

Rules, such as those above which form the core of a knowledge system, are acquired by aknowledge engineer Knowledge engineers use techniques such as interviewing,videotapes, protocol analysis, and personal observations to understand and capture anexpert’s reasoning knowledge The rules elicited from one expert may be very different fromthose acquired from another, which reflects the phenomenon of differences among experts.Care must be exercised in constructing a rule set

How well an Expert System performs depends on the extent to which a knowledge engineersucceeds in formalizing an expert’s reasoning knowledge in a rule set

In addition to a rule set, an Expert System must be able to keep track of the current state ofthe world For instance, the Expert System may need to know the most recent budget deficit

to use it as a basis for rules determining whether the impending deficit can be inferred to belarge Thus, the knowledge system would need a state variable for the budget deficit figure

An Expert System’s state variables are sometimes characterized as being attribute/valuepairs The individual predicates of the previous rule is example of attribute/value pairs Statevariables can be organized to form frames A frame consists of a number of attributes thatdescribe a concept or object The value of each attribute can either be specified, determined

by default, or inherited from other frames

A more natural way of handling state descriptions for business applications is to use suchcommon business computing techniques as database management The database concept

is essentially a prefabricated frame

The inference engine is the control mechanism of an Expert System It is activated when the

user initiates a consultation session with the Expert System by requesting advice about aspecific problem The problem statement identifies a goal to be sought and may specifyinitial values for some state variables A goal is a variable whose value is to be deduced bythe inference engine Moreover, some of the other state variables may also have unknownvalues Beginning with known state variable values (if any), the inference engine appliesrules that allow it to infer values for unknown variables It may also prompt the user toestablish values for unknown variables The inference process moves through a series ofstates with more and more variables’ values becoming known until the goal state is reached(i.e., the goal variable’s value becomes known) The inferred result is then reported to theuser as advice

There are many dimensions along which inference engines can be characterized anddifferentiated Many of these are concerned with how an engine applies rules in making theunknown known For instance, inference engines can differ in terms of:

 whether rules are processed in “forward” or “backward” fashion;

 The order for selecting candidate rules

 The way in which confidence factors are combined; and

 The language of implementation

Forward and backward inference refer to the way in which the engine examines a rulepremise first or conclusion first In the latter case, an inference engine looks at ruleconclusions to identify those rules that have actions affecting the goal variable (i.e., thatcould give it a known value) The premise of such a candidate rule can then be examined Ifcurrent state variable values make the premise true, the rule is “fired” by taking the actionsstated in its conclusion However, if those variables currently have unknown values, thenthey become subgoals as the basis for further backward inference

In forward reasoning, an inference engine examines each rule’s premise with respect to thecurrent state variable values If such values make its premise true, the rule is fired causingvariable values to change as directed by the conclusion’s actions The inference enginecontinues this non-goal-directed reasoning until either the goal variable value is established

or no more rules can be fired The goal is reached in the first case, but fails to be attainedotherwise

For both forward and backward inference, as well as hybrids thereof, several rules can becandidates for immediate processing The approach that an inference engine uses tochoose the order for processing candidate rules is called a selection strategy It caninfluence how rapid the consultation will be and possibly what its results will be

Inference engines can also differ in terms of their approaches to combining confidencefactors as rules are fired The alternative approaches are often called certainty algebras.They range from conservative to venturesome methods of propagating certainties aboutrules and variables’ values along to the result, allowing the expert system to express adegree of confidence in the advice it deduces Some inference engines support no certaintyalgebras Others support only one Sometimes a programmer must be employed to code analgebra In other cases, an inference engine has built-in switches for easy selection of adesired algebra

It is important to understand that there is a major distinction between the language used tostate rules and the language used to implement an inference engine that processes thoserules Modern tools for developing expert systems provide a high-level language fordevelopers to formally state human expertise in the form of rules Users and developers areprimarily interested in the flexibility and power of knowledge representation provided by such

a language and in the knowledge processing muscle of the corresponding inference engine

An expert system’s user interface supports the interaction between a user and the inference

engine during a consultation session Common interfacing techniques include commands,forms, icons, menus, and their combinations The general principle of designing a userinterface is that it should match what users of the noncomputer system have beenaccustomed to Dual communication is provided by an interface: users ask the inferenceengine for advice and users are asked for specific data by the inference engine duringconsultations In addition, a user can ask to explore the inference engine’s line of reasoningafter the deduced advice is presented The ability to explain the reasoning behind arecommendation enables both naive users and experts to understand the rationaleunderlying a piece of advice

3.5 Applying Expert System To Commercial Loan Decisions

The user interface allows users to supply facts about the specific problem, execute theknowledge system, and obtain reports Written reports are especially important for loananalysis because loan decisions often must be justified A loan decision is also subject toreview in future periods if the borrower requests another loan or defaults on the current loan.The knowledge system contains expert knowledge regarding the kinds of problems thesystem is designed to solve For commercial loan analysis, this knowledge is acquired fromexperts in lending and includes procedures, rules, heuristics, and general facts

The inference engine applies the knowledge of the expert to the facts that describe aspecific situation For commercial loan analysis, these facts come from loan application, theborrower’s financial statements, and average financial ratios for the borrower’s industry.The inference engine uses the knowledge system to conclude new facts from the largenumber of detailed facts supplied through the interface The final conclusion of thecommercial loan system is advise on whether or not to grant the loan in question

3.5.1 Knowledge Engineering

The process of building the knowledge system of an expert system is called knowledgeengineering and consists of the following activities: knowledge elicitation, knowledgerepresentation, and knowledge programming

During knowledge elicitation, the knowledge engineer extracts from the expert the concepts,rules, and procedures normally used

Next, the engineer represents the knowledge in the expert system software

Finally, the knowledge engineer constructs and revises the system to reflect the changes(misfits and enhancements) that result from testing the system

3.5.2 Tools

Expert System can be developed from scratch using high level symbolic programminglanguages However, recently powerful software environments exist for knowledgerepresentation and processing on general purpose computers, PC’s

Today, over 60 PC based Expert System Development software tools are available in themarket These tools are designed specifically to provide a framework to represent theknowledge system and to provide built-in inference engine.

These Expert System tools have some advantages:

 The built-in Inference engine provides a strong mechanism for searching solution

 Provides convenient interface with the user and other external programs

 Most of them are suitable for popular computers

Expert system shells and other software tools make it possible to built expert systems morequickly and inexpensively than if general purpose programming languages were used Sometools, often called shells, provide all the expert system components except the knowledgesystem The knowledge engineer has only to fill the knowledge system with expertise about

a specific kind of problem, such as commercial loan analysis If a more general purposeprogramming language were used, the system developers would also have to code theinference engine and user interface

Chapter 4 Design and methodology

4.1 Methodology

4.1.1 Research Methodology

Data relevant to the research will be collected from both primary and secondary sources.This requires working with some banks in order to conduct interviews/discussions forcollecting the rules and experience in commercial loan decisions in the actual conditions ofVietnam

The bank head offices and branches in HCMC will be chosen to visit Publications of thebank will also be collected at the time of visiting Commercial bank lending officers, creditanalysts, and loan committees will be interviewed and requested to test the software afterdesigning

4.1.2 Research Framework

The process of building the expert system includes the following steps as follows:

 Defining the problem

 Discovering the basic concepts and their interrelationships

 Developing the relevant rules and procedures

 Building an initial prototype and testing it

 Revising and expanding in a series of iterations until the system solves the overallproblem as originally intended

4.2 Design

4.2.1 Overall Structure Design

The system is a microcomputer-based credit analysis tool in commercial lending It includesthree parts:

 Install the system information

It provides the means to the users to install or revise the system information as desired such

as which files the system needs and content of these files, which outputs the systemgenerates and how to calculate them

 Database management

Background Rules

Credit Risk Rating Rules

Overall Financial Rules Rulebase

It provides the friendly user interface to the users to interact with the database and updatethe data In addition, it processes data provided by users to prepare historical and analyticalinformation, as well as proformas projections The program will generate the following oneither an historical or proformas basis:

4.2.2 Implementing Knowledge Base Design

The knowledge base for the expert credit granting system prototype consist of twocomponents: a customer database and a rulebase

INSTALL SYSTEM INFORMATION

DATABASE MANAGEMENT

CONSULTATION

Structure of Files List of Files

Format of Outputs List of Outputs

System Information Database

Financial Statement Files

Customer Files

Reports Files

Projections Files Customer Database

4.2.2.1 Customer Database

The customer database contains all available data pertaining to the customers/borrowers.The structure of the database is shown in Figure 4.2 The various blocks of data (rectangularboxes in Figure 4.2) are ‘files’, and each data item belonging to a file is a ‘field’ Thedatabase is logically divided into many files according to the attribute group to which aspecific data item might belong The files are related to each other through appropriate keyfields

Each of the files in the database has at least one unique index key and may have multipleindex keys to efficiently perform various search and retrieval tasks The unique index keysmay be single fields or a combination of several data fields belonging to the file Forexample, a unique record in the income statement database is a combination of two fields: acustomer’s code and the year-end

Figure 4.2 Database Structure

 Company/borrower Information

Before entering actual financial information, it is necessary to first record the borrower’sname and provide information about the borrower

 Financial Information

Financial information consists of Balance Sheet and Income Statement at least one full year

In Balance Sheet, the data must be balanced; that is, assets must equal liabilities plus networth in all historical periods

Customer Information

Key: Cust Code

Key: Cust Code

 Setting up Annual Projections

There are only a few steps involved in setting up annual projection Program requires atleast two full years of historical data In addition, the data must be balanced; that is, assetsmust equal liabilities plus net worth in all historical periods

The initial annual projection scenario generated for a borrower will always be a defaultsenario All projected values may be based on the performance and account relationshipsfor the three-year average historical performance For example, if sales increased in lastthree year by 5%, then program will automatically increase sales by 5% in all projectionperiods

Note that, if there are three or more years of historical data in the system, programautomatically uses the three or more year average default scenario to initially establish a set

of projected values for the same number of periods If there are two years of complete yearhistorical data in system, program uses the two-year default scenario to initially establish aset of projected values

Regardless of which default scenario is generated, every account on the projected balancesheet and income statement is accessible for override via one of the Annual ProjectionRules However, not all of the Annual Projection Rules apply to each account on the balancesheet and income statement For example, using turnover makes no sense as a method forprojecting operating expenses

 Annual Projection Rules

The Balance Sheet and Income Statement Accounts are projected using one of thefollowing:

Growth Factor Applies Growth Factor (Misc Account Growth Factor + 1) *

Prior Period AccountHistoric Growth Uses the historic ratio of (PP1 - PP2) / PP2

Last Period Uses the amount from the Prior Period

Specific Account Formula Uses a specific formula

Gross Margin (1 - Gross Margin) * Net Sales

which is prorated across all Cost of Goods Sold account Ifany other Cost of Goods Sold are overridden or rulechanged, then Total minus overrides is prorated across theremaining accounts If the Gross Margin variable isoverridden in the rule (1 - Gross Margin) * Net Sales forthis account

Price/Quantity/Usage Used when an account is projected by Price, Quantity,

and/or Usage; each affected by its growth factor

Inc./Dec by $ amount Increase/Decreases the prior period amount by the value

entered

% of SG&A Applied % of SG&A

% of SG&A * Current Period Total SG&A ExpensesClass Override Used when an account which is projected by its class is

overridden by user Remaining accounts in class are thenprojected using the class formula

Balancing line Determined by deficit or surplus User cannot override

 Annual Forecast Variables

Projections based on a three-year average calculate many forecast variables using anaverage of the three historical years

Sales, Cost of Goods Sold Variables

Sales Growth Factor (%) Affects: Sales, Other Operating Revenue

Historical Sales Growth is calculated using (CP Net Sales

-PP Net Sales) / -PP Net Sales (Three year average usesSquare Root) These values will be used to compute totalsales for each projected year This is the most importantvariable to consider when making a projection

Gross Margin (%) Affects: Cost of Goods Sold

Gross Profit divided by sales gives the fraction of salesrepresented by gross profit Depreciation in Cost of GoodsSold is not considered part of COGS when calculatinggross profit

SG&A / Sales (%) Affects: SG&A Expenses

Selling, general, and administrative expenses divided bysales gives the fraction of sales represented by SG&A

Days Turnover Variables

Days Receivable Affects: Accounts Receivable, Promissory notes, Other

receivablesReceivables divided by Sales, times days in year

Days Inventory Affects: Inventory

Inventory divided by COGS, times days in year If noCOGS, then Net Sales

Days Payable Affects: Accounts Payable, Notes Payable

Payables divided by COGS, times days in year If noCOGS, then Net Sales

Fixed Assets Variables

Building & Improvement

Furniture & Fixtures

Machinery & Equipment

Miscellaneous Variables

Cash / (SG&A & COGS) Affects: Cash

This ratio results from the historical relationship betweenactual cash balances divided by SG&A expenses plus Cost

of Goods Sold It is used to project cash balances in eachprojection year Note: COGS does not include Depreciation

in COGS

Dividends/Net Profit Affects: Cash dividend - Common Stock

This ratio shows the historical pattern of dividend payout It

is assumed that forecast year dividends will be paid out inthe same ratio to Net Income as in the prior year NetIncome

Misc Account Growth

Factor

Affects Assets: Accounts Receivable - Affiliate, AccountsReceivable - Related Cos., Accounts Receivable - Other,Other Receivables, Costs in Excess of Billings, PrepaidExpenses, Other Current Assets, Due From Affiliates -Current, Other Non-Operating Current, Due fromEmployees, Prepaid Expenses - Non-Current, DeferredCharges, Deposits, Cash Value Life Insurance, OtherAssets, Other Non-Operating Assets

Affects Liabilities: Accounts Payable - Affiliates, AccountsPayable - Related Co., Accounts Payable - Other, Wages/Salaries Payable, Interest Payable, Tax Accruals, P/S PlanContribution, Bonuses Payable, Other Accruals, Billings inExcess of Costs, Other Current Liabilities, Other Non-Operating Current, Deferred Income, Other Liab Non-CurrNon-Int Bearing

Affects Income Statement: Other Income, Other Expense,Minority Interest (Non-Cash), Earnings from Sub (Non-Cash), Remitted Earnings from Sub (Non-Cash), RemittedEarnings from Sub, Unremitted Earn from Sub (Non-Cash)

This rate will display the Sales Growth Rate User mayoverride the rate to affect the above accounts.

Prime Rate A default of .09 was arbitrarily chosen This value can

change as appropriate for each year

short-term Spread over

Prime

The default is 02 Change as appropriate

The short-term Rate will be calculated when the programconverged

Interest Income Rate A default of Prime Rate - 02 was arbitrarily chosen

long-term Spread over

if the value is not appropriate

Will not exceed short-term Rate (Prime + Spread)

Short-Term Notes Payable Banks and Other

Balance The historical short-term Notes Payable amount is

displayed on the balance lines The amount carriesforward You may override the loan amount

Interest Rate Enter the Interest Rate corresponding to each of the

Notes The default is short-term Rate

Existing Senior Debt

Current Portion The historical Current Maturities - Long-Term Debt amount

is displayed on the Current Portion line Long-Term Debt iscalculated by the Total Outstanding less Current Portion(not more than the outstanding balance) We may overridethe amount

Total Outstanding The sum of the historical long-term debt and the historical

Current Maturities - long-term Debt display on the historicalTotal Outstanding line The first projection period iscalculated by the PP total Outstanding less PP CurrentPortion (net less than zero)

Interest Rate Enter the Interest Rate for each of the Notes The default

is the Average Historical Interest Rate We may overridethe rate

Months in Period Default to 12 Enter the number of months in period for

which the loan is outstanding

New Long-Term Debt

Current Portion First year of the New Debt: Enter the amount of the

expected current portion in the appropriate projectionamount

Remaining Projected Years: In future years the CurrentPortion will carry forward but not to exceed the outstandingbalance

Total Outstanding First year of the New Debt: Newly Disbursed amountminus

any Principal Paydown is reflected here automatically.Remaining Projected Years: In the future the TotalOutstanding will be calculated by the PP total outstandingless PP current Portion (not less than zero)

Interest Rate The default is the Long-Term Rate We can override the

rateMonths in Period Default to 12 Enter the number of months in period for

which the loan is outstanding

Subordinated debt

Current Portion The historical Current Maturities Subordinated Debt

amount is displayed on the Current Portion line

Subordinated Debt is calculated by the Total Outstandingless Current Portion (not more than the outstandingbalance) We may override the amount.

Total Outstanding Displays the sum of the historical Subordinated Debt from

the Balance Sheet plus historical Current Portion Theprojected period is calculated as the PP Total Outstandingless PP Current Portion (not less than zero)

Interest Rate The default is the Average Historical Interest Rate

Months in Period Default to 12 Enter the number of months in period for

which the loan is outstanding

Preferred Stock

Preferred Stock Display the historical Preferred Stock value from the

balance sheet The projected period is calculated as lastperiod amount

Dividend Rate The default value is Long-Term Rate (Prime+Spread)Months in Period The default value is 12

Valuation Rates

Risk Free Rate Interest Income Rate

Risk Premium Default value is 0.06

Beta Factor Default value is 1.00

P.V Non Operating

Assets

Historical Amount carried forward

# of Shares Historical Amount carried forward

Earnings Multiple Historical Amount carried forward

Market / Book Multiple Historical Amount carried forward

Marginal Tax Rate Default value is 0.34

Alternate Cost of Capital Arbitrarily set at Prime Rate + 0.02

 Cash Flow Report

Sales - Net Net Sales

Change in Receivables Accounts Receivable Net: PP - CP

CP Accounts Receivable is subtracted from Net Salesbecause it represents sales for which payment has not yetbeen received PP Accounts Receivable is added to NetSales because it represents cash received in the CP forsales that remained unpaid at the beginning of the CP.CASH FROM SALES Net Sales + Change in Receivables

Cost of Goods Sold Cost of Goods Sold

Depreciation is excluded from Cost of Goods Sold

Change in Inventories Inventory: PP - CP

Inventory that is not sold is a cost of doing business

Change in Payables Accounts Payable: CP - PP

Accounts Payable is an offset to the cash cost of holdinginventory

CASH PRODUCTION

COSTS

Cost of Goods Sold + Change in Inventories + Change inPayables

GROSS CASH PROFITS Cash from Sales + Cash Production Costs

SG & A Expense SG & A Expenses

SG & A includes: General & Administrative, Selling,Operating, Officers’ Compensation, Lease & Rental, BadDebt, Travel & Entertainment, Auto & Delivery, OtherOperating Expense, Other Selling Expense, OperatingExpenses (Affiliates)

Depreciation and amortization are excluded from SG & Abecause they are non-cash expenses Bad Debt Expense

is not excluded even though it is a non-cash expensebecause Accounts Receivable has already been reduced

by this amount

Change in Prepaid Prepaid Expenses Non Current: PP - CP

Deferred Charges: PP - CPPrepaid Expenses: PP - CPChange in Accruals Accruals: CP - PP

Accruals include: Wages/Salaries Payable, Tax Accruals,P/S Plan Contribution, Bonuses Payable, Other Accruals,Foreign Exchange Adjustment

Change in Other B/S

Accounts

Accounts Receivable: PP - CPNotes Receivable - Current: PP - CPOther Receivables: PP - CP

Costs in Excess of Billings: PP - CPOther Current Assets: PP - CPOther Non-Operating Current: PP - CPNotes Receivables - Non Current: PP - CP

Due from Officers/Stockholders: PP - CPDue from Employees: PP - CP

Other Non-Current Receivables: PP - CPDeposit: PP - CP

Cash Value Life Insurance: PP - CPOther Assets: PP - CP

Other Non-Operating Assets: PP - CPAccounts Payable: CP - PP

Due to Officers/Stockholders: CP - PPOther Current Liabilities: CP - PPOther Non-Operating Current: CP - PPDeferred Income: CP - PP

Other Non Cur Liab-Interest Bearing: CP - PPOther Non Cur Liab-Non Interest Bear: CP - PPProvision Pension: CP - PP

Provision Price Fluctuation: CP - PPProvision Fiscal: CP - PP

Other Income/Expense Interest Income

Other IncomeOther Income (Affiliate)Other Expense (Affiliate) Other Expense

Remitted Earnings from SubForeign Exchange GainForeign Exchange LossCash Operating Expense SG&A Expense + Change in Prepaid + Change in Accruals

+ Change in Other B/S Accounts + Other Income/ExpenseIncome Taxes Paid Income Tax Current + Income Tax Deferred +

Income Tax Receivable: PP - CPIncome Tax Payable: CP - PPDeferred Taxes

NET CASH AFTER

Capital Expenditures Due

to Replacement

Capital Expenditures due to Replacement

CASH FOR DEBT

SERVICE

Net Cash After Operations + Capital Expenditures Tangible

-Interest Expense Interest Payable: CP - PP

Interest Expense (Not including non-cash Interests:

Interest ELTD Note 4 + Sub Debt 4 + PIK Interest Sub.Debt)

Bank Fees and

Commissions

Bank Fees and Commissions

Dividends Paid Preferred

Stock

Cash Dividend, Preferred StockDividends Payable, Preferred StockFinancing Costs Interest Expense + Bank Fees & Commissions + Dividends

Paid, Preferred Stock

NET CASH INCOME Cash for Debt Service + Financing Costs

Current Portion LTD Current Maturities LTD (PP)

Current Maturities NLTD (PP)Current Maturities Subordinated Debt (PP)The prior period’s current portion is a cash drain in thecurrent year

If the prior period is an interim, the prior period CMLTD isdivided by the annualization factor to determine the cashdrain

CASH AFTER DEBT

AMORTIZATION

Net Cash Income + Current Portion Long-Term Debt

Dividends Paid Common

Stock

Cash Dividend - Common Stock Dividends Payable, Common Stock: CP - PPAccounts Receivable

Intra-Company

Accounts Receivable, Affiliate: PP - CPAccounts Receivable, Related Co.: PP - CPAccounts Payable Intra-

Company

Accounts Payable - Affiliate: CP - PPAccounts Payable - Related Co.: CP - PPDue from/to Intra-

Company

Due from Affiliates - Current: PP - CPDue from Affiliates - Non Current: PP - CPDue to Affiliates: CP - PP

Due to Parent/Subsidiary: CP - PPDue to Related Companies: CP - PPExtraordinary Items Extraordinary Gain/(Loss)

Accounting ChangeOther

Capital Expenditures

Tangible

Net Fixed Assets: PP - CPLess: Depreciation Expense, Depreciation in COGSGain/Loss on sale of Assets

Capitalized InterestLess: Capital Expenditures Due to Replacement

When Net Fixed Assets increases (PP - CP < 0), it is acash outflow (An increase in an asset implies cash use)The depreciation expense is also an increase in cashoutflow since the ending Net Fixed Assets Balance wouldhave been greater by the amount of the depreciationexpense (a non-cash charge)

Capital Expenditures

Intangible

Intangibles: PP - CPLess: Amortization ExpenseLong-Term Investments Investments in Subs: PP - CP

Investments: PP - CPOther Advances: PP - CPOther Investments: PP - CPMinority Interest (Non-Cash)Earnings from Subsidiary (Non-Cash)Unremitted Earnings from Sub (Non-Cash)Discretionary

Transactions

Dividends Paid, Common Stock + Accounts Receivable,Intra-Company + Accounts Payable, Intra-Company + Duefrom/to Intra-Company + Extraordinary Items + CapitalExpenditures - Intangibles + Long-Term Investments

 Financial Ratios

GROWTH RATIOS

Net Sales Growth,

Composite,%

((CP Net Sales - PP Net Sales)/ABS PP Net Sales)*100

Net Income Growth,% ((CP Net Profit - PP Net Profit)/ PP Net Profit)*100

Total Assets Growth,% ((CP Total Assets - PP Total Assets)/ PP Total Assets)*100Total Liabilities Growth,% ((CP Total Liabilities - PP Total Liabilities)/ PP Total

Liabilities)*100Total Worth Growth,% ((CP Total Worth - PP Total Worth)/ PP Total Worth)*100

PROFITABILITY RATIOS

Profitability Ratios reflect the firm’s ability to generate sufficient profits from its operations, tosimultaneously yield satisfactory returns to owners and creditors, and also yield asatisfactory return on the total invested assets

Gross Margin, Composite,

%

(Gross Profit or Revenue(Net Sales - Cost of Goods Sold)/Net Sales)*100

SG&A, % (SG&A Expenses/Net Sales)*100

Cushion (Gross Margin -

SG&A),%

Gross Margin, Composite % - SG&A %

Depreciation,

Amortization,%

(Depreciation and Amortization/Net Sales)*100

Operating Profit Margin,% Cushion % - Depreciation, Amortization %

Interest Expense, % (Interest Expense / Net Sales) * 100

Operating Margin, % Cushion % - Depreciation, Amortization,% - Interest

Expense, %Net Margin, % (Net Profit / Net Sales) * 100

(Net Profit / Average Tangible Net Worth)*100

Tangible Net Worth Net Worth - Intangibles

Dividend Payout Rate, % (Cash Dividend / Net Profit) * 100

Depreciation / CMLTD

(Net Profit +Depreciation + Amortization / PP Curr Mat Existing LTD + PP Curr Mat New LTD + PP Curr Mat Sub Debt)

Cash for Debt Service /

Interest

Cash for Debt Service / Interest Expense

Cash for Debt / Interest

+CMLTD

Cash for Debt Service / Interest Expense + PP Curr Mat Existing LTD+ PP Curr Mat New LTD + Curr Mat Sub Debt)

Interest / Avg Interest

Days Sales)*365 Days

Working Capital Current Assets - Current Liabilities

Quick Ratio (Cash, Certificate of Deposit + Marketable Securities

+Surplus Marketable Securities + Accounts Receivable Net) / Current Liabilities

Current Ratio Current Assets / Current Liabilities

Sales/Net Working Capital Net Sales / Working Capital

LEVERAGE RATIOS

Leverage is the balance between creditors’ funds and owners’ funds maintained by acompany When debt grows too large in relation to worth, the risk to creditors is greater.Total Liabilities/T Net

Cash Margin, % (Gross Cash Margin / Net Sales)*100

The Cash Margin ratio captures the net impact of critical operating areas: gross margin,receivables, Inventory and Payables It indicates the percent of the sales dollar whichremains to pay for operating expenses, financing costs and capital outlays

Cash Coverage Net Cash after Operations / (Financing Costs + Current

Portion Long-term Debt + New Long-term Debt Payment)The Cash Coverage ratio indicates whether the firm has generated enough cash to meet itstotal financing costs A ratio greater than one indicates the firm has done so A ratio of lessthan one indicates the opposite, i.e., the firm is not solvent by reference to its internal cashgenerating capability

Net Cash Income Cash for Debt Service + Financing Costs

P(the firm’s profit margin

on sales)

Net Profit / Net Sales

Ñ(the target dividend

payout ratio)

Dividends / Net Profit

L(the target debt-to-equity

ratio)

Total Senior Debt / (Net Worth - Intangible +sub Debt)

T(the capital-output ratio) Total Assets / Net Sales

Sustainable Growth N / (T - N)

BANKRUPTCY

Based on the Altman Bankruptcy model Z Score is computed as follows:

1.2 * Working Capital to Total Assets

!.4 * Retained Earnings to Total Assets

3.3 * EBIT to Total Assets

0.6 * Market Value Equity / Book value (BV=Total Liabilities)