So, it is very important to predict the loan type and loan amount based on the banks' data. In this blog post, we will discuss about how Naive Bayes Classification model using R can be used to predict the loans. As there are more than two independent variables in customer data, it is difficult to plot chart as two dimensions are needed to better visualize how Machine Learning models work. In this blog post, Naive Bayes Classification Model with R is used.
Assessing the fairness of a decision making system with respect to a protected class, such as gender or race, is challenging when class membership labels are unavailable. Probabilistic models for predicting the protected class based on observable proxies, such as surname and geolocation for race, are sometimes used to impute these missing labels for compliance assessments. Empirically, these methods are observed to exaggerate disparities, but the reason why is unknown. In this paper, we decompose the biases in estimating outcome disparity via threshold-based imputation into multiple interpretable bias sources, allowing us to explain when over- or underestimation occurs. We also propose an alternative weighted estimator that uses soft classification, and show that its bias arises simply from the conditional covariance of the outcome with the true class membership. Finally, we illustrate our results with numerical simulations and a public dataset of mortgage applications, using geolocation as a proxy for race. We confirm that the bias of threshold-based imputation is generally upward, but its magnitude varies strongly with the threshold chosen. Our new weighted estimator tends to have a negative bias that is much simpler to analyze and reason about.
Fannie Mae, the nation's largest source of conventional mortgage funds, has made a commitment to use technology to improve the efficiency of processing a loan by reducing the time, paperwork and cost associated with loan origination. The Desktop Underwriter (DU) system which was developed as a result of this commitment, is an automated underwriting expert system that applies both heuristics and statistics to the problem. The system supports both the wholesale and retail mortgage environments and is built to reason and underwrite loans with incomplete, unverified and conflicting data. The system generates a credit recommendation based on the loan's conformity to credit standards and an eligibility recommendation based on the loan's conformity to eligibility
Rockville, MD 20850 Colleen McClintock Infinite Intelligence, Inc. 1155 Connecticut Avenue, #500 Washington 20036 Jacqueline Sobieski Fannie Mae 3900 Wisconsin Avenue Washington 20016 Abstract Business policy can be defined as the guidelines and procedures by which an organization conducts its business. Organizations depend on their information systems to implement their business policy. It is important that any implementation of business policy allows faster application development and better quality management and also provides a balance between flexibility and centralized control. This paper views business rules as atomic units of business policy that can be used to define or constrain different aspects of the business. It then argues that business rules provide an excellent representation for business policy. KARMA was developed and deployed at Fannie Mae. 1 Introduction Business policy can be defined as the guidelines and procedures by which an organization conducts its business. Business policy is often documented in manuals and business guidelines and is reflected in an organization's information systems. Organizations depend on their information systems to implement this policy.
Custom DU is an automated underwriting system that enables mortgage lenders to build their own business rules that facilitate assessing borrower eligibility for different mortgage products. Developed by Fannie Mae, Custom DU has been used since 2004 by several lenders to automate the underwriting of numerous mortgage products. Custom DU uses rule specification language techniques and a web-based, user-friendly interface for implementing business rules that represent business policy. By means of the user interface, lenders can also customize their underwriting findings reports, test the rules that they have defined, and publish changes to business rules on a real-time basis, all without any software modifications. The user interface enforces structure and consistency, enabling business users to focus on their underwriting guidelines when converting their business policy to rules. Once lenders have created their rules, loans are routed to the appropriate rule sets, and customized, but consistent, results are always returned to the lender. Using Custom DU, lenders can create different rule sets for their products and assign them to different channels of the business, allowing for centralized control of underwriting policies and procedures—even if lenders have decentralized operations.