Chapter 28

CREDIT MANAGEMENT

OUTLINE

• Terms of Payment

• Credit Policy Variables

• Credit Evaluation

• Credit Granting Decision

• Control of Accounts Receivable

• Credit Management in India

TERMS OF PAYMENT

• Cash Terms

• Open Account

• Consignment

• Bill of Exchange

• Letter of Credit

CREDIT POLICY VARIABLES

The important dimensions of a firm’s credit policy are:

• Credit standards

• Credit period

• Cash discount

• Collection effort

CREDIT STANDARDS

Liberal Stiff

• Sales Higher Lower

• Bad debt loss Higher Lower

• Investment Larger Smaller in receivables

• Collection costs Higher Lower

IMPACT ON RESIDUAL INCOME OF RELAXATION

RI = [S(1 – V) - Sbn] (1 – t ) – k I

where RI = change in residual income

S = increase in sales

V = ratio if variable cost to sales

bn = bad debt loss ratio on new sales

t = corporate tax rate

I = increase in receivables investment

EXAMPLE

Pioneer Limited is considering relaxing its credit standards.

S = Rs.15 million, bn = 0.10, V = 0.80,

ACP = 40 days, k = 0.10, t = 0.4

RI = [15,000,000 (1 – 0.80) – 15,000,000 x 0.10] (1 – 0.4)

15,000,000 – 0.10 x x 40 x 0.80

360

= Rs.766,667

CREDIT PERIOD

Longer Shorter

• Sales Higher Lower

• Investment in Larger Smaller

receivables

• Bad debts Higher Lower

IMPACT ON RESIDUAL

INCOME OF LONGER CREDIT PERIOD

RI = [S(1 – V) - Sbn] (1 – t ) – k I

INCREASE IN RECEIVABLES

INVESTMENT

S0 S

I = (ACPn – ACP0) + V (ACPn) 360 360

where: I = increase in receivables investment

ACPn = new average collection period (after lengthening

the credit period)

ACP0 = old average collection period

V = ratio of variable cost to sales

S = increase in sales

EXAMPLE

Zenith Limited is considering extending its credit period from 30 to 60 days.

S = Rs.50 million, S = Rs.5 million, V = 0.85, bn = 0.08, k = 0.10, t = 0.40

RI = [5,000,000 x 0.15 – 5,000,000 x 0.08] (0.6)

– 0.10 (60 – 30) x + 0.85 x 60 x

= [750,000 – 400,000] (0.6) – 0.10 [4,166,667 + 708,333]

= – 277,500

50,000,000360

5,000,000360

LIBERALISING THE CASH

DISCOUNT POLICY

RI = [S(1 – V) - DIS] (1 – t ) + k I

DECREASING THE RIGOUR

OF COLLECTION PROGRAMME

RI = [S(1 – V) - BD] (1 – t ) – k I

ERRORS IN CREDIT EVALUATION

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

TRADITIONAL CREDIT ANALYSIS

Five Cs of Credit

Character : The willingness of the customer to honour his obligations

Capacity : The operating cash flows of the customer

Capital : The financial reserves of the customer

Collateral : The security offered by the customer

Conditions : The general economic conditions that affect the customer

Should credit be granted?

Character

Capacity Capacity

Capital Capital Capital Capital

Excellent risk Fair risk Doubtful risk Dangerous risk

How much credit

should be granted ?

SEQUENTIAL CREDIT ANALYSIS

Strong Weak

StrongWeak Weak

Strong

StrongStrong

StrongWeakWeak Weak

WeakStrong

NUMERICAL CREDIT RATING INDEX

Factor Factor Rating Factor weight 5 4 3 2 1 score

Past payment 0.30 1.20 Net profit margin 0.20 0.80 Current ratio 0.20 0.60 Debt-equity ratio 0.10 0.40 Return on equity 0.20 1.00 Rating index 4.00

DISCRIMINANT ANALYSIS

Z = 1 Current ratio + 0.1 Return on equity

°

+

°

°

°

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+

+

+°°

°

°°

°

++

++

+

+

+

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++

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Return on equity

Currentratio

Risk Class Description 1 Customers with no risk of default

2 Customers with negligible risk of default (default rate less than 2 percent)

3 Customers with little risk of default (default rate between 2 percent and 5 percent)

4 Customers with some risk of default (default rate between 5 percent and 10 percent)

5 Customers with significant risk of default (default rate in excess of 10 percent)

RISK CLASSIFICATION SCHEME

Offer credit

Refuse credit

Customer pays

Customer defaults

p

(1 – p)

Rev – Cost

– Cost

0

CREDIT GRANTING DECISION

Expected Pre-tax Profit

p (Revenue – Cost) – (1 – p) Cost

EXAMPLE

ABC Company is considering offering credit to a customer. The probability that the customer would pay is 0.8 and the probability that the customer would default is 0.2. The revenues from the sale would be Rs.1,200 and the cost of sale would be Rs.800.

The expected profit from offering credit, given the above information, is:

0.8 (1,200 – 800) – 0.2 (800) = Rs.160

Expected profit on Probability of payment Expected profit on initial order and repeat order repeat order [ p1(REV1 – COST1) – (1-p1) COST1]

+ p1 x [ p2 (REV2 – COST2) – (1-p2) COST2]

[0.9 (2000-1500) – 0.1(1500)]

+ 0.9 [0.95 (2000-1500) – 0.05 (1500)]

= 660

+ x

REPEAT ORDER

DECISION TREE FOR GRANTING CREDIT

Offer credit

Pays

p 1 = 0.9

Defaults(1 – p

1 ) = 0.1

Offer credit

p 1 = 0.95Pays

Defaults(1 – p1 )

= 0.05

CONTROL OF ACCOUNTS

RECEIVABLES

• Days’ Sales Outstanding

• Ageing Schedule

• Collection Matrix

COLLECTION MATRIX

Percentage of Receivables January February March April May June Collected During the Sales Sales Sales Sales Sales Sales Month of sales 13 14 15 12 10 9 First following month 42 35 40 40 36 35 Second following month 33 40 21 24 26 26 Third following month 12 11 24 19 24 25 Fourth following month - - - 5 4 5

SUMMING UP

• The important dimensions of a firm’s credit policy are : credit standards, credit period, cash discount, and collection effort

• In general, liberal credit standards tend to push sales up by attracting more customers. However, this is accompanied by a higher incidence of bad debt loss, a larger investment in receivables, and a higher cost of collection. Stiff credit standards have opposite effects.

• Three broad approaches are used for credit evaluation : traditional credit analysis, numerical credit scoring, and discriminant analysis.

• The traditional approach to credit analysis calls for assessing a prospective customer in terms of the five Cs of credit, viz. character, capacity, capital, collateral, and conditions.

• Three methods are commonly employed for monitoring accounts receivable : days’ sales outstanding, ageing schedule, and collection matrix.