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.

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.

Fortunately, this problem can be overcome with CMS 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 CMS. The followings describe how CMS 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

CMS 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. CMS 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 CMS. 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. By Cramer Tree Segmentation The left is a result from probability modeling using Cramer decision tree segmentation. Although it is not as good as neural network, it still produces useful result patterns. By Regression: General Linear Model This result is produced with general linear regression models. With general linear model of RR=0.99936, it produces totally useless predictive patterns! This figure shows no patterns in distributions of reds.

Credit Scoring

(Internal) credit score is a numerical rating of credit loans. It measures the level of risk of being defaulted. The level of default risk can be best predicted with predictive modeling. Credit scores can be measured in term of default probability or relative numerical ratings. The following subsections outline several credit scoring methods;

Method 1: Predicting default probability

Decision tree divides customer loan segments into smaller sub segments recursively. At each segment, splitting is made in a way that boosts proportions of either defaulted loans or fully-recovered loans, in each resulting sub segment. This process repeats until no further improvement can be made.

The above figure shows CMS 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 CMS;

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.