Well, the answer is: Numerous.
In order to make a comment about the methods used for credit scoring, one has to know the idea behind credit scoring. Moreover, the processes involved in building a model for credit scoring also entails knowing the possible methods that can be used. Because some of the methods used has advantages in one area but drawbacks in other areas.
The following part which is taken from the academic article “What is the point of credit scoring?” very well summarizes what can be done and what has been done up to know. Article was written by Loretta Mester who is a vice president and economist in the Research Department of the Philadelphia Fed. She is also the head of the department’s Banking and Financial Markets section.

Scoring Methods
Several statistical methods are used to develop credit scoring systems, including linear probability models, logit models, probit models, and discriminant analysis models. (Saunders discusses these methods.) The first three are standard statistical techniques for estimating the probability of default based on historical data on loan performance and characteristics of the borrower. These techniques differ in that the linear probability model assumes there is a linear relationship between the probability of default and the factors; the logit model assumes that the probability of default is logistically distributed; and the probit model assumes that the probability of default has a (cumulative) normal distribution. Discriminant analysis differs in that instead of estimating a borrower’s probability of default, it divides borrowers into high and low default-risk classes.

Two newer methods beginning to be used in estimating default probabilities include options pricing theory models and neural networks. These methods have the potential to be more useful in developing models for commercial loans, which tend to be more heterogeneous than consumer or mortgage loans, making the traditional statistical methods harder to apply. Options-pricing theory models start with the observation that a borrower’s limited liability is comparable to a put option written on the borrower’s assets, with strike price equal to the value of the debt outstanding. If, in some future period, the value of the borrower’s assets falls below the value of its outstanding debt, the borrower may default. The models infer the probability a firm will default from an estimate of the firm’s asset-price volatility, which is usually based on the observed volatility of the firm’s equity prices (although, as McAllister and Mingo point out, it has not been empirically verified that short run volatility of stock prices is related to volatility of asset values in a predictable way. Saunders discusses other assumptions of the options-pricing approach that are likely to be violated in certain applications.) Saunders reports that KMV Corporation has developed a credit monitoring model based on options-pricing theory.

Neural networks are artificial intelligence algorithms that allow for some learning through experience to discern the relationship between borrower characteristics and the probability of default and to determine which characteristics are most important in predicting default. (See the articles by D.K. Malhotra and coauthors and by Edward Altman and coauthors for further discussion.) This method is more flexible than the standard statistical techniques, since no assumptions have to be made about the functional form of the relationship between characteristics and default probability or about the distributions of the variables or errors of the model, and correlations among the characteristics are accounted for.

Some argue that neural networks show much promise in credit scoring for commercial loans, but others have argued that the approach is more ad hoc than that of standard statistical methods. (The article by Edward Altman and Anthony Saunders discusses the drawbacks.) A study by Edward Altman, Giancarlo Marco, and Franco Varetto analyzed over 1000 healthy, vulnerable, and unsound Italian industrial firms from 1982-92 and found that performance models derived using neural networks and those derived using the more standard statistical techniques yielded about the same degree of accuracy. They concluded that neural networks were not clearly better than the standard methods, but suggested using both types of methods in certain applications, especially complex ones in which the flexibility of neural networks would be particularly valuable.”

You are in charge of your own credit, your credit report, and your credit score. It is important to maintain good credit habits. It’s a fact of life, companies need to manage risk successfully or they won’t be in business for long. Demonstrate good credit behavior and you will be presenting a positive picture of yourself to lenders. Show them that you know how to use credit responsibly, and that you are a risk well worth taking.

Here are some good credit habits that could pay big dividends!

1. Pay your bills consistently on time. This is one of the most important things you can do to present a positive picture to lenders. Recent payment history is a major factor in determining your credit score.
2. Limit the amount of money you borrow to what you can afford to repay. Sounds like an obvious and simple principle? Sure, but it’s where countless consumers get into trouble. Take on too many loans, too many credit cards, or high interest rates, and before you know it, you’re in trouble. You’re behind on your payments, penalties and late fees are adding up, and a major portion of your income goes to servicing your debt, instead of securing your financial future.
3. Limit the total number of credit cards that you maintain.

1. Don’t “over-apply”. Applying for too much credit, or too many credit cards at the same time is not a good sign to lenders, and will lower your credit score.
2. Use credit responsibly by maintaining a modest level of credit accounts in various categories: Retail accounts, installment loans, mortgage loans, finance company accounts, etc.
3. Shop around for the best credit terms! Companies want your business. Even if your credit is less than perfect, very competitive rates still may be readily available to you. You can potentially save yourself a lot of money if you shop around for the best deal. Secure the best deal possible, then maintain a strong record of on-time payments!
4. Stay on top of your credit! Check your credit report, your credit score, and monitor your credit information on all three credit bureaus on a regular basis. The only one who knows if the information in your credit report is accurate is you! So, it’s up to you to make sure the information in your credit reports is accurate. It’s a very small price to pay to potentially save tens of thousands of dollars or more!