Credit risk

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Categories of
financial risk

Credit risk
Concentration risk

Market risk
Interest rate risk
Currency risk
Equity risk
Commodity risk

Liquidity risk
Refinancing risk

Operational risk
Legal risk
Political risk

Reputational risk

Volatility risk

Settlement risk

Profit risk

Systemic risk
 v·d·e

Basel II

Bank for International Settlements

Basel Accords - Basel I
Basel II
Background

Banking
Monetary policy - Central bank

Risk - Risk management

Regulatory capital
Tier 1 - Tier 2
Pillar 1: Regulatory Capital

Credit risk
Standardized - F-IRB - A-IRB
PD - LGD - EAD

Operational risk
Basic - Standardized - AMA

Market risk
Duration - Value at risk
Pillar 2: Supervisory Review

Economic capital
Liquidity risk - Legal risk
Pillar 3: Market Disclosure

Disclosure
Business and Economics Portal

Credit risk is an investor's risk of loss arising from a borrower who does not make payments as
promised. Such an event is called a default. Another term for credit risk is default risk.
Investor losses include lost principal and interest, decreased cash flow, and increased collection
costs, which arise in a number of circumstances:

 A consumer does not make a payment due on a mortgage loan, credit card, line of credit,
or other loan
 A business does not make a payment due on a mortgage, credit card, line of credit, or
other loan
 A business or consumer does not pay a trade invoice when due
 A business does not pay an employee's earned wages when due
 A business or government bond issuer does not make a payment on a coupon or principal
payment when due
 An insolvent insurance company does not pay a policy obligation
 An insolvent bank won't return funds to a depositor
 A government grants bankruptcy protection to an insolvent consumer or business

Contents
[hide]

 1 Types of credit risk

 2 Assessing credit risk
o 2.1 Sovereign risk
o 2.2 Counterparty risk
 3 Mitigating credit risk
 4 See also
 5 Further reading
 6 References
 7 External links

[edit] Types of credit risk

 Default risk
 Credit spread risk
 Downgrade risk

[edit] Assessing credit risk

Main articles: Credit analysis and Consumer credit risk

Significant resources and sophisticated programs are used to analyze and manage risk. Some
companies run a credit risk department whose job is to assess the financial health of their
customers, and extend credit (or not) accordingly. They may use in house programs to advise on
avoiding, reducing and transferring risk. They also use third party provided intelligence.
Companies like Standard & Poor's, Moody's Analytics, Fitch Ratings, and Dun and Bradstreet
provide such information for a fee.

Most lenders employ their own models (credit scorecards) to rank potential and existing
customers according to risk, and then apply appropriate strategies. With products such as
unsecured personal loans or mortgages, lenders charge a higher price for higher risk customers
and vice versa. With revolving products such as credit cards and overdrafts, risk is controlled
through the setting of credit limits. Some products also require security, most commonly in the
form of property.

Credit scoring models also form part of the framework used by banks or lending institutions
grant credit to clients. For corporate and commercial borrowers, these models generally have
qualitative and quantitative sections outlining various aspects of the risk including, but not
limited to, operating experience, management expertise, asset quality, and leverage and liquidity
ratios, respectively. Once this information has been fully reviewed by credit officers and credit
committees, the lender provides the funds subject to the terms and conditions presented within
the contract (as outlined above).

Credit risk has been shown to be particularly large and particularly damaging for very large
investment projects, so-called megaprojects. This is because such projects are especially prone to
end up in what has been called the "debt trap," i.e., a situation where – due to cost overruns,
schedule delays, etc. – the costs of servicing debt becomes larger than the revenues available to
pay interest on and bring down the debt.[1]

[edit] Sovereign risk

Sovereign risk is the risk of a government becoming unwilling or unable to meet its loan
obligations, or reneging on loans it guarantees.[2] The existence of sovereign risk means that
creditors should take a two-stage decision process when deciding to lend to a firm based in a
foreign country. Firstly one should consider the sovereign risk quality of the country and then
consider the firm's credit quality.[3]

Five macroeconomic variables that affect the probability of sovereign debt rescheduling are: [4]

 Debt service ratio

 Import ratio
 Investment ratio
 Variance of export revenue
 Domestic money supply growth

The probability of rescheduling is an increasing function of debt service ratio, import ratio,
variance of export revenue and domestic money supply growth. Frenkel, Karmann and Scholtens
also argue that the likelihood of rescheduling is a decreasing function of investment ratio due to
future economic productivity gains. Saunders argues that rescheduling can become more likely if
the investment ratio rises as the foreign country could become less dependent on its external
creditors and so be less concerned about receiving credit from these countries/investors.[5]

[edit] Counterparty risk

Counterparty risk, otherwise known as default risk, is the risk that an organization does not pay
out on a bond, credit derivative, credit insurance contract, or other trade or transaction when it is
supposed to.[6] Even organizations who think that they have hedged their bets by buying credit
insurance of some sort still face the risk that the insurer will be unable to pay, either due to
temporary liquidity issues or longer term systemic issues.[7]

Large insurers are counterparties to many transactions, and thus this is the kind of risk that
prompts financial regulators to act, e.g., the bailout of insurer AIG.

On the methodological side, counterparty risk can be affected by wrong way risk, namely the
risk that different risk factors be correlated in the most harmful direction. Including correlation
between the portfolio risk factors and the counterparty default into the methodology is not trivial,
see for example Brigo and Pallavicini[8]

A good introduction can be found in a paper by Michael Pykhtin and Steven Zhu.[9]

[edit] Mitigating credit risk

Lenders mitigate credit risk using several methods:

 Risk-based pricing: Lenders generally charge a higher interest rate to borrowers who are
more likely to default, a practice called risk-based pricing. Lenders consider factors
relating to the loan such as loan purpose, credit rating, and loan-to-value ratio and
estimates the effect on yield (credit spread).
 Covenants: Lenders may write stipulations on the borrower, called covenants, into loan
agreements:
o Periodically report its financial condition
o Refrain from paying dividends, repurchasing shares, borrowing further, or other
specific, voluntary actions that negatively affect the company's financial position
o Repay the loan in full, at the lender's request, in certain events such as changes in
the borrower's debt-to-equity ratio or interest coverage ratio
 Credit insurance and credit derivatives: Lenders and bond holders may hedge their
credit risk by purchasing credit insurance or credit derivatives. These contracts transfer
the risk from the lender to the seller (insurer) in exchange for payment. The most
common credit derivative is the credit default swap.
 Tightening: Lenders can reduce credit risk by reducing the amount of credit extended,
either in total or to certain borrowers. For example, a distributor selling its products to a
troubled retailer may attempt to lessen credit risk by reducing payment terms from net 30
to net 15.
  Diversification: Lenders to a small number of borrowers (or kinds of borrower) face a
high degree of unsystematic credit risk, called concentration risk. Lenders reduce this
risk by diversifying the borrower pool.
 Deposit insurance: Many governments establish deposit insurance to guarantee bank
deposits of insolvent banks. Such protection discourages consumers from withdrawing
money when a bank is becoming insolvent, to avoid a bank run, and encourages
consumers to holding their savings in the banking system instead of in cash.

Credit and Finance Risk Management

Credit risk analysis (finance risk analysis, loan default risk analysis) and credit risk management
is important to financial institutions which provide loans to businesses and individuals. Credit
can occur for various reasons: bank mortgages (or home loans), motor vehicle purchase finances,
credit card purchases, installment purchases, and so on. Credit loans and finances have risk of
being defaulted. To understand risk levels of credit users, credit providers normally collect vast
amount of information on borrowers. Statistical predictive analytic techniques can be used to
analyze or to determine risk levels involved in credits, finances, and loans, i.e., default risk
levels.

Is the current financial meltdown caused by Holistic Risk Management?

It's been argued that the current global financial meltdown is the consequence of abusive use of
risky holistic risk management methods such as derivative-based insurance and Monte Carlo
techniques by management and professionals. Note that Monte Carlo is a gambler's method that
does not reflect real risk. By doing so, they encouraged risky lending such as NINJA and
subprime mortgages to occur. They prescribed risk models which in essence hide risk involved
and transfer directly to insurers using shadowy derivative financial instruments such as CDS
and CDO. Isn't it a time to implement sound predictive risk management systems based on
empirical data as described in this page?

Why internal credit scoring?

Personal credit scores are normally computed from information available in credit reports
collected by external credit bureaus and ratings agencies. Credit scores may indicate personal
financial history and current situation. However, it does not tell you exactly what constitutes a
"good" score from a "bad" score. More specifically, it does not tell you the level of risk for the
lending you may be considering. Internal credit scoring methods described in this page address
the problem. It is noted that internal credit scoring techniques can be applied to commercial
credits as well.

Credit Risk Analysis and Modeling

In this page, the following credit risk analysis methods are described;
  Credit risk factor profiling or loans default analysis.
 Credit predictive modeling or loans default predictive modeling.
 Credit risk modeling or finance risk modeling.
 Credit scoring (Internal).

Profiling Risky Credit Segments

Credit risk profiling (finance risk profiling) is very important. The Pareto principle suggests that
80%~90% of the credit defaults may come from 10%~20% of the lending segments. Profiling
the segments can reveal useful information for credit risk management. Credit providers often
collect a vast amount of information on credit users. Information on credit users (or borrowers)
often consists of dozens or even hundreds of variables, involving both categorical and numerical
data with noisy information. Profiling is to identify factors or variables that best summarize the
segments.

Combinational factor analysis and Combinatorial blowout!

Analyzing such vast information is an extremely difficult and challenging task! In conventional
methods, factor analysis is performed on a few (to several) variables at a time using statistical
software. As the total number of variables increases, the number of combinations to be
examined in this way grows combinatorially. When a large number of variables is involved, the
number of combinations is too large to be examined manually. Thorough systematic accurate
analysis is all but impossible! A conventional method to this problem is to examine
combinations that are likely to have influence. However, hunch can leave out important factors
without being noticed.

Fortunately, this problem can be overcome with CMSR Hotspot Profiling Analysis. Hotspot
profiling analysis drills-down data systematically and detects important relationships, co-factors,
interactions, dependencies and associations amongst many variables and values accurately using
Artificial Intelligence techniques, and generate profiles of most interesting segments. Hotspot
analysis can identify profiles of high (and low) risk loans accurately through thorough systematic
analysis of all available data. The followings are examples of hotspot profiling applied to credit
information.

Finance risk factor profiling examples

Finance risk factor profiles can be easily developed with CMSR. The followings describe how
CMSR hotspot analysis tools can be used in developing profiles.

[Example 1] A financing firm (or bank) keeps loan records on motor vehicle purchase in its
database including default information: gender, age, education, occupation, income; vehicle type,
manufacturer, model, year make, price, loan amount, default, default amount, etc. The firm
wishes to know which types of loans for motor vehicle purchases are at the highest risk, i.e.,
highest default ratio by probability;
[Example 2] For the same data, the bank wishes to know which types of loans for motor vehicle
purchases are at the lowest risk in terms of lowest average default amounts;

Credit Risk Modeling

If past is any guide for predicting future events, predictive modeling is an excellent technique for
credit risk management. Predictive models are developed from past historical records of credit
loans, containing financial, demographic, psychographic, geographic information, etc. From the
past credit information, predictive models can learn patterns of different credit default ratios, and
can be used to predict risk levels of future credit loans. It is important to note that statistical
process requires a substantially large number of past historical records (or customer loans)
containing useful information. Useful information is something that can be a factor that
differentially affects credit default ratios.

Credit Risk Predictive Modeling and Tools

CMSR supports robust easy-to-use predictive modeling tools. Users can develop models with the
help of intuitive model visualization tools. Application and deployment of credit risk models is
also very simple. CMSR supports the following predictive modeling tools;

 Neural Network is a very powerful modeling tool. It generally offers most accurate and versatile models. It's
very easy to develop neural network predictive models with CMSR. Network visualization tools will guide
users from configuration, training, testing, and more importantly direct application to databases.
  Cramer Decision Tree produces most compact and thus most general decision trees. Decision tree can be
used for predicting segmentation-based statistical probability of credit loan defaults.
 Regression produces mathematical functions for predicting default risk levels. It can be very limiting to be
used as general-purpose credit risk predictive modeling methods. However when it is used with above
methods, it can be a very useful method.
Pitfalls of classification modeling techniques

Classification models predict events into categorical classes, say, "risky" or "safe".
Classification methods are supported by decision tree, SVM, neural network, etc. Intuitively,
this is a very appealing approach as prediction is made using terms that anyone can understand!
However, there is a serious drawback in applying classification techniques to credit risk
management. The problem lies with the fact that credit defaults are in general very low ratio
events, say, less than 10%. Developing predictive models with skewed data is very difficult,
especially with decision tree classification. Decision trees develop predictive models by
segmenting populations into smaller groups recursively. It uses the dominant category (or most
frequent value) of each segment as the predicted value for the segment. Dominant categories are
the values represented by over 50% segment population. Credit users are already well screened.
It is possible that no segments may contain risky customers in excess over 50%! Even it exists,
it may be slightly over 50%! Segments in which 49% customers have default-history will be
predicted as "not" risky, although they are in very high risk segments! This type of models will
have very low accuracy in predicting risky customers as "risky". Much worse is that, as a
consequence, more non-risky customers may end up being classified as "risky". Not much
useful properties! It is important to note that all classification techniques have this limitation. To
overcome this problem, you may be tempted to use tricks by introducing extra instances.
However, such tricks will necessarily distort overall representation of population. Still the
problem remains! A better approach is credit scoring using statistical probability described in
the next sections.

Do regression methods work?

Generally speaking, regression methods don't work well for complex modeling. This is
especially true if modeling data have severe skews. It tends to produce rather randomly
predictions. The following histograms show comparison between different modeling techniques
under severe data skew;

By Neural network
Neural network is a very powerful modeling framework. As
shown in the left figure, it can learn in very detail. Most
green areas are located below 0.4. Most red areas are
located above 0.4.
The above figure shows CMSR decision tree. Customer loan segments are partitioned
recursively in a way that increases the proportion of either defaulted or fully-recovered loans. In
the figure, reds represent defaulted loan portions and greens for fully-recovered loans. Nodes in
red indicate that over 50% customers of the segments have defaulted loans. Green nodes have
less than 50% of defaulted customers.

For new loan applications, when customer's information is applied to the tree, it will normally
lead to a terminal node segment. The default ratio of the node is used as the credit score of the
customer. If the segment has 35% default ratio in the past, the score will be 35% (0r 0.35). For
more information, please read Decision Tree Software.

Better modeling method: Predicting relative default risk level

Tree-based credit scoring provides coarse level prediction. It lacks the accuracy that neural
network models can produce. Neural Network is a very powerful predictive modeling technique.
Neural network is derived from animal nerve systems (e.g., human brains). The heart of the
technique is (artificial) neural network. Neural networks can learn to predict in detail with high
accuracy. The following shows the neural network module of CMSR;
Neural network works differently from decision tree. It can be trained to predict either relative
default levels or expected default amounts. When the former is used, network will predict
relative level of credit defaults. The latter will predict expected default amounts. The followings
are histograms, showing distribution of credit scores predicted by a neural network credit scoring
model. Note that reds are credit loans defaulted. Greens represent credit loans fully recovered.
Clearly, the neural network model predicts default loans with higher scores and loans fully-
recovered with lower scores. Analyzing distribution of scores, default probability may be
deduced.

*** Find out the limitations of predictive modeling based credit risk management in the next
section.

Judgmental Scoring and Predictive rule engines

Credit industries heavily rely on judgmental methods. Judgments are made from past experience
on important factors such as customer payment history, debt service capacity, leverages, relevant
references, credit agency ratings, and information extracted from various financial statements.
Judgmental rules are used to arrive at ratings.
Normally, this process is performed manually. With the advancement of predictive rule engines,
it is now possible to automate this process. This can incorporate the best of both judgmental
scoring and statistical scoring methods. Critical data which are the basis of judgment can be
collected from financial statements, credit agency reports, past customer payment records, and so
on. Judgmental data may be included as well. Judgmental data are subjective soft data. From
financial statements, certain judgmental data may be extracted as subjective assessment by staff.
Rules are developed to score risks based on critical and judgmental data. This type of automated
systems will promote scoring consistency and accuracy in ratings while maintaining flexibility.

Predictive models may be included in judgmental rules. That is, rules can be used to assess
outcomes of statistical predictive models. Combining both judgmental and statistical predictive
models can result in best industry practices.

Real-time Expert Advisor for Credit Scoring

Predictive modeling is based on past statistical evidences. If there is not enough evidence,
predictive modeling can fail to predict reliably. In general, most of high risky applications are
filtered manually by various regulations, policies and judgmental discretions. They are not in
modeling data records. Most statistical evidence for high risk credits are not present in historical
data. Thus predictive models will fail to predict even the most obvious risks. Predictive modeling
alone cannot be used as the whole solution.

Rule-based modeling is a very powerful platform that combines the best of the knowledge of
experienced human experts and the power of predictive modeling. It is ideally suited to
overcome the limitations of predictive modeling for risk management. This incorporates
judgemental scoring. Rosella BI Platform provides two rule-based modeling engines: RME and
RME-EP. Both are based on SQL-like rule specification languages. They are very powerful
languages incorporating predictive models along with logical expressions and mathematical
formulas. RME is a procedural language. RME-EP is for rule-based expert systems. Together
they serve as a very powerful platform for risk modeling. For more, please read Expert Systems
Shell - Rule Engines.

The following figure shows examples of web-embedded risk management dashboard

components for credit risk analysts. It shows visualized risk levels inferred using rule-based
predictive models. Models are evaluated from Rosella BI server and fed to internal charting
system;
Rule-based model specification language in Rosella Platform is based on powerful SQL database
query language with enhanced predictive modeling support. Intuitive-ness and expressive power
of SQL is well proven. It can easily incorporates the followings into credit scoring models;

 Government regulations.
 Internal business policies.
 Common sense and judgmental rules.
 Industry professional heuristics.

In you are interested in trial, please write to us.

Financial Solution Developer or Provider?

Rosella BI Platform is the multi-purpose end-to-end developer platform for financial solutions.
It supports all the tools needed in developing financial risk management solutions: profiling,
segmentation, decision tree, neural network, rule-base modeling, business rules, model
validation, model deployment, charting and report engines, and so on. It provides all the
features for CPM, BAM, BEP, CEP and Balanced Scorecard for financial solutions. For more,
please read Rosella BI Platform, Expert Systems Rule Engines and Predictive Modeling.

Published Articles by David Balovich

Title: Credit Analysis Versus Risk Analysis

Published in: Creditworthy News
Date: 2/25/04
 
Among the list of seminar topics my company, Business Education Services,
provide are credit analysis and risk analysis. Prospective clients often ask
us, “What is the difference between credit analysis and risk analysis, aren’t
they the same thing?” Our response to this question is, no they are not.

Credit analysis involves determining the probability of payment and is the

most common practiced of the two analyses. Probability of payment involves
gathering bank and trade payment experience, analysis of the customer’s
balance sheet and the overall evaluation of character, capacity and capital.
It is short-term analysis and most often the only analysis used in determining
whether or not to approve a customer for credit terms.

Risk analysis involves identifying the potential loss or gains in selling to

the customer over a period of time and is considered by many to be advanced
credit analysis.

In today’s business environment risk analysis often plays a more important

role than credit analysis. The reason being that more and more businesses are
informing their credit departments that the credit sale is a given and though
it is the responsibility of the credit professional to identify and control
risk, “not to sell on credit” is not an option. This is not a new concept;
many organizations have operated their credit departments employing risk
analysis for decades.

To understand the concept of risk analysis one has to focus on profit and
loss. Under this premise there are four options.

1. Grant credit - customer pays – seller makes a profit. 

2. Refuse credit - customer would not have paid – seller saves cost of
sale. 
3. Grant credit - customer does not pay – seller loses cost of sale. 
4. Refuse credit - customer would have paid – seller loses profit.

As one can see, the ultimate question is, “What is the potential gain or loss
in taking the risk?” Keep in mind that the first principle of credit is that
risk is inherent in every transaction conducted on credit terms. The key to
risk analysis is first being aware of your company’s margin of profit in their
products or services and second, where is the break-even point. The credit
professional that utilizes these two tools will make credit decisions that: 

 Improve company revenues.  

 Eliminate the impact of bad debt on company operations and finances.
 Make the credit department a hero in the eyes of the sales department.  

Credit analysis relies on the use of customer provided information. Risk

analysis uses our company information, primarily revenue and cost of sales, to
determine how much risk there is in selling to the customer on open terms over
a period of time. The use of risk analysis can produce a favorable decision to
sell even when credit analysis says differently.

In our seminars we employ several examples to illustrate risk analysis. The

following is one of those examples.

Let’s assume a very marginal prospect applies for credit. Their credit
references are less than sparkling and if we were to credit score them they
would not qualify for credit. The sales department wants to sell them $30,000
each month.

Knowing our profit margins we use break-even analysis to identify the time
line where our company’s cost of sales and profit equal zero. Utilizing this
tool we can determine the necessary credit limit and provide it with
restrictions. If we sell through the break-even month and have to write money
off to bad debt, we will still recover our costs. Thus, any purchases and
payments made after the break-even month will produce a profit.

Now, let’s assume our break-even point is two months. We’ve established a
credit limit of $30,000 and our margins are 33%. The customer purchased and
paid through twelve months and then experienced problems in the thirteenth
month. Our revenue through thirteen months is $390,000 and profits would be
close to $120,000.

If we write off the purchases made in the thirteenth month to bad debt it
would appear, on the surface, to put the credit department in a bad light.
However, when offset against the profit made during the twelve months, it no
longer is material. The company will still realize eleven times the amount
written off, in profits.

In our example, had the credit manager refused to grant credit based solely on
credit analysis, the company would have not realized over $350,000 in
additional revenue and over $100,000 in profits.

We also employ examples using lower profit margins that may take longer to
meet the break-even point and thus require a lower credit limit. However, it
becomes easier to justify the assigning of credit limits and the basis for the
credit decision to the sales department with information that reflects the
differences in overall profit vs. loss then just relying on prior payment
history and a “feeling” that the customer will fail.

The efficient credit department, today, utilizes both credit and risk analysis
in making decisions that benefit all concerned.

I wish you well.