Revisión del aprendizaje automático modelos para puntuación de análisis de crédito

Introduction:Increase in computing power and the deeper usage of the robust computing systems in the financial system is propelling the business growth, improving the operational efficiency of the financial institutions, and increasing the effectiveness of the transaction processing solutions used by the organizations. Problem:Despite that the financial institutions are relying on the credit scoring patterns for analyzing the credit worthiness of the clients, still there are many factors that are imminent for improvement in the credit score evaluation patterns.  Objective:Machine learning is offering immense potential in Fintech space and determining a personal credit score. Organizations by applying deep learning and machine learning techniques can tap individuals who are not being serviced by traditional financial institutions. Methodology:One of the major insights into the system is that the traditional models of banking intelligence solutions are predominantly the programmed models that can align with the information and banking systems that are used by the banks. But in the case of the machine-learning models that rely on algorithmic systems require more integral computation which is intrinsic.  Results:The test analysis of the proposed machine learning model indicates effective and enhanced analysis process compared to the non-machine learning solutions. The model in terms of using various classifiers indicate potential ways in which the solution can be significant. Conclusion: If the systems can be developed to align with more pragmatic terms for analysis, it can help in improving the process conditions of customer profile analysis, wherein the process models have to be developed for comprehensive analysis and the ones that can make a sustainable solution for the credit system management. Originality:The proposed solution is effective and the one conceptualized to improve the credit scoring system patterns.  Limitations: The model is tested in isolation and not in comparison to any of the existing credit scoring patterns.&nbsp

Organizational Strategies in the Mortgage Industry

Mortgage managers lack the organizational strategies to evolve after the 2008 economic decline of the U.S. and the global economy. The significance in the lack of organizational strategies threaten the U.S. and global communities with challenges in defaults for homeowners, performance and profits for the mortgage industry that jeopardize solvent economies. The purpose of this qualitative single case study was to explore the strategies mortgage loan managers use to avoid mortgage crises and maintain profitability. The conceptual framework for this study was the social audit theory. The participants of the study were 7 mortgage managers, in the North-Eastern region of the U.S. Data were collected using semistructured interviews as the primary source, and as the secondary source data from public files, press releases, archives, public databases, and the company website. Using methodological triangulation, data, were analyzed and the following 5 themes emerged: adherence to government regulations, training strategies, credit history strategies, work history strategies and income-to- debt-ratio strategies. The potential implications for positive social change include increasing the success rate of lending for mortgage managers, which in return could create profit for mortgage firms, generate employment opportunities, increase the government tax revenues, and contribute to the growth of the U.S economy

Understanding, Analyzing and Predicting Online User Behavior

abstract: Due to the growing popularity of the Internet and smart mobile devices, massive data has been produced every day, particularly, more and more users’ online behavior and activities have been digitalized. Making a better usage of the massive data and a better understanding of the user behavior become at the very heart of industrial firms as well as the academia. However, due to the large size and unstructured format of user behavioral data, as well as the heterogeneous nature of individuals, it leveled up the difficulty to identify the SPECIFIC behavior that researchers are looking at, HOW to distinguish, and WHAT is resulting from the behavior. The difference in user behavior comes from different causes; in my dissertation, I am studying three circumstances of behavior that potentially bring in turbulent or detrimental effects, from precursory culture to preparatory strategy and delusory fraudulence. Meanwhile, I have access to the versatile toolkit of analysis: econometrics, quasi-experiment, together with machine learning techniques such as text mining, sentiment analysis, and predictive analytics etc. This study creatively leverages the power of the combined methodologies, and apply it beyond individual level data and network data. This dissertation makes a first step to discover user behavior in the newly boosting contexts. My study conceptualize theoretically and test empirically the effect of cultural values on rating and I find that an individualist cultural background are more likely to lead to deviation and more expression in review behaviors. I also find evidence of strategic behavior that users tend to leverage the reporting to increase the likelihood to maximize the benefits. Moreover, it proposes the features that moderate the preparation behavior. Finally, it introduces a unified and scalable framework for delusory behavior detection that meets the current needs to fully utilize multiple data sources.Dissertation/ThesisDoctoral Dissertation Business Administration 201

Impact of Mortgage Characteristics on Retail Mortgage Transaction Completion Time

In the mortgage industry, many mortgage lenders cannot manage mortgage workflow systems while meeting and exceeding organizational objectives. Organizations with an above-industry average turnaround time (ATT) to complete a retail mortgage transaction (RMT) from origination to funding experience revenue losses. Grounded in the proposition that mortgage loan purpose (MLP), mortgage loan type (MLT), and subject property type (SPT) impact ATT to complete an RMT, the purpose of this causal-comparative study was to assess the impact of MLP, MLT, and SPT on ATT to complete an RMT. Using archival data records (N = 146) from a selected mortgage institution in the state of Florida, the results of the 2 x 2 x 2 factorial ANOVA showed that there were no main or interaction effects F(5,140) = 0.42, p = .83. Implications for social change include the possibility for mortgage lenders to implement improved workflow processes to reduce costs and improve efficiency metrics and intrinsic value, thereby benefitting organizational stakeholders such as employees and consumers