The lexical connotation root of the "credit" comes from Latin origin "credo", which means "believe". Believing requires trust between the two parts of the equation;  lenders and borrowers. Loan is the act of giving money, property or other material goods to another party in exchange for future repayment of the principal amount along with interest or other finance charges. A loan may be for a specific, one-time amount or can be available as open-ended credit up to a specified ceiling amount. Before taking the loan decision of either granting it or not, lenders tend to evaluate the risk of borrower default for reference purpose. Among various tools to assess risk is credit scoring. Credit scoring is thought to be the most successful applications of statistical and operations research modeling in finance and banking. Credit scoring has been crucial in allowing the phenomenal growth in consumer credit in the last five decades. Without a truthful, precise and automatically operated risk assessment tool, lenders of consume credit couldn't have expanded their loan books in the way they have. Credit scoring was one of the earliest financial risk assessment tools developed as well as being the grandfather of data mining because it was one of the earliest uses of data on consumer behavior.  

Credit scoring is the set of decision models and their underlying techniques that help lenders in the granting of consumer credit. These techniques decide who will get credit, how much credit they should get, and what operational strategies will enhance the profitability of the borrowers to the lenders. Credit scoring techniques assess the risk in lending to a particular consumer. The most common understanding that credit scoring assesses creditworthiness of the consumer is not precise. Creditworthiness is not an attribute of individuals like height or weight or even income. It is an assessment by a lender of a borrower and reflects the circumstances of both and the lender's standpoint of the likely future economic scenarios. Thus some lenders will assess an individual as creditworthy while others will not. One of the ongoing dangers of credit scoring is that this might cease to be the case, and there will be those who can get credit from all lenders and those who cannot. Describing someone as creditworthy cause's offense. It is better for the lender to state the reality, which is that the proposition of lending to this consumer represents a risk that the lender is unwilling to take. 

Historically, it is universally believed that David Durand is the pioneer of credit scoring. The history of credit scoring begins with the study of Durand in the area of consumer instalment financing published in 1941 by the National Bureau of Economic Research - a US nonprofit organization engaged in knowledge diffusion of how the economy works. The study was commissioned in 1937 after observing that consumer financing faced the Great Depression better and registered relatively small losses compared to other credit markets. In 1941, Durand applied discriminant analysis proposed by Fisher (1936) to classifying prospective borrowers. Another account goes that mail-order companies brought in the numerical scoring system in the 1930s to overcome the inconsistency among credit analysts in their interpretation of the company credit policy. After World War II broke out, many finance houses and mail-order companies lacked the experts to perform the work of credit analysis as many experienced people in the field joined the war. Those companies then asked experienced experts to put down their knowledge in credit assessment in the form of guidelines to help the relatively inexperienced make lending decision. The statisticians that designed the scorecard in the early days hoped to model after the practice of insurance companies who scored applicants based on age and gender to determine the premium. They reckoned that if banks could also have a scorecard for loan applicants as basis for making lending decision, it would help save the loan processing time and accomplish the objective of risk management.  In the 1950s, attempts had been made to combine automated credit decision making with statistical techniques to develop models that would help the making of credit decisions. But due to the lack of powerful computing tools, those models were substantially limited in sample size and model design. In that period, mathematician Earl Isaac and engineer Bill Fair founded the first credit scoring consulting firm in San Francisco. In the 1960s, the emergence of credit card made banks and credit card issuers realize what an excellent tool credit scoring was. As they were faced with massive credit card applications each day, automated decision making process could help save considerable costs and manpower, while maintaining certain level of decision quality. With the rapid advancement of computer technology, credit scoring was further incorporated with decision support system that enabled the scoring system to extend its applications. For example, auto loan, credit card, mail-order, direct sale, home loan, insurance policy, and mobile phone accounts can be better managed through the credit scoring system.

Saudi Credit Bureau (SIMAH), a leader in credit and information management in the MENA region, has introduced the full empirical SIMAH  Score development V4.0 developed by FICO. Financial institutions in the Saudi market used to have V1.0 and V2.0. SIMAH introduced its score V3.0 to the local market then V4.0 to both local and GCC markets. SIMAH's Score offers a new scoring model which incorporates important credit  factors that present unique and enhanced value to GCC lenders and creditors. By leveraging FICO's state-of-the-art analytic capabilities and predictive technologies, coupled with the rich repository of Saudi consumer credit information and experience of the Saudi market provided by SIMAH, SIMAH® Score provides a new optimized risk score built on a full and expanded view of a Saudi consumer. The SIMAH Score includes some key changes when compared to the previous version.  Saudi Arabia specific characteristics were developed to account for the nuances, constraints and trends of the Saudi market. Due attention and analysis was given to features such as salaried loans, expatriates, and telecom data. These important local factors have been used in the creation of predictive characteristics that present unique and enhanced value of V 4.0 to Saudi lenders. Over 700 characteristics were generated covering the standard five categories of a FICO score. The scoring system includes six segment models. The main segmentation is driven by historic delinquency in line with the performance bad definition, time on books and thickness of file. The segmentation enables greater identification of risk patterns across more similar groups of consumers based on the depth, breadth and experience of credit usage. 

SIMAH Score shows that the predictive power of a scoring system can be greatly enhanced by segmenting a population by certain predictive characteristics and then developing a separate scorecard specifically designed for each of these subpopulations. The reason this technology is so powerful is that relevant predictive characteristics, as well as the predictive patterns associated with those characteristics, vary from subpopulation to subpopulation. This method of model development is referred to as multiple scorecard technology. The segmentation enables greater identification of risk patterns across more similar groups of consumers based on the depth, breadth and experience of credit usage. Close to one hundred schemes were tested on a variety of splitters.

• Enabling management to become well acquainted with its clients and to be notified of any risks associated with working with them.
•  Reducing rates of defaulting on payments as well as the losses incurred through credit operations.
  
• Enhancing competencies by focusing on the most important aspects of credit operations.
  
• Effectively managing risk.
  

(Saudi Credit Bureau) SIMAH is the leading credit bureau in the MENA region, serving individual consumers, small businesses and large corporations with a full range of credit and financial and risk management products and services.

Established in 2002, and operating since 2004 SIMAH has issued more than 104 million credit reports since 2004 with data quality score of 99.4%. Serving over 17.2 Million customers and having over 63 Million CI's, SIMAH is the largest credit bureau in the MENA region offering industry-leading product and services like SIMAT (Business Solutions), SIMATI (Consumer Solutions) and Value-Added Services (VAS) to cater to business and consumers alike.

SIMAH offers integrated solutions in the domain of analytics and decisioning with its subsidiary QARAR making it the first bureau linked analytics company in the region. MOARIF (The Legal Identifier), SNTR (SIMAH National Trade Repository) and TASNIF (SIMAH Rating) are also managed and operated by SIMAH ensuring vertical and horizonal integration of services.

SIMAH head office is based in Riyadh and can be reached on www.simah.com