Intelligent Credit Scoring

Intelligent Credit Scoring
Автор книги: id книги: 821389     Оценка: 0.0     Голосов: 0     Отзывы, комментарии: 0 2815,89 руб.     (30,56$) Читать книгу Купить и читать книгу Купить бумажную книгу Электронная книга Жанр: Зарубежная образовательная литература Правообладатель и/или издательство: John Wiley & Sons Limited Дата добавления в каталог КнигаЛит: ISBN: 9781119282334 Возрастное ограничение: 0+ Оглавление Отрывок из книги

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“Make sure your students follow your instructions.” That sounds like a straightforward instruction, but in fact, it’s fairly abstract. What does a teacher actually have to do to make sure students are following? Even the leader delivering this direction may not know, and the first-year teacher almost certainly doesn’t. The vast majority of teachers are only observed one or two times per year on average—and even among those who are observed, scarcely any are given feedback as to how they could improve. The bottom line is clear: teachers do not need to be evaluated so much as they need to be developed and coached. In Get Better Faster: A 90-Day Plan for Coaching New Teachers, Paul Bambrick-Santoyo shares instructive tools of how school leaders can effectively guide new teachers to success. Over the course of the book, we break down the most critical actions leaders and teachers must enact to achieve exemplary results. Designed for coaches as well as beginning teachers, Get Better Faster is an integral coaching tool for any school leader eager to help their teachers succeed. It’s the book’s focus on the actionable—the practice-able—that drives effective coaching. By practicing the concrete actions and micro-skills listed here, teachers will markedly improve their ability to lead a class, producing a steady chain reaction of future teaching success. Though focused heavily on the first 90 days of teacher development, it’s possible to implement this work at any time. New and old teachers alike can benefit from the guidance of Get Better Faster and close their existing instructional gaps. Packed with practical training tools, including agendas, presentation slides, a coach’s guide, handouts, planning templates, and 35 video clips of real teachers at work, Get Better Faster will teach you: The core principles of coaching: Go Granular, Make Feedback More Frequent, Top action steps to launch a teacher’s development in an easy-to-read scope and sequence guide The four phases of skill building: Phase 1 (Pre-Teaching): Dress Rehearsal Phase 2: Instant Immersion Phase 3: Getting into Gear Phase 4: The Power of Discourse

Оглавление

Siddiqi Naeem. Intelligent Credit Scoring

More Praise for Intelligent Credit Scoring

Wiley & SAS Business Series

Acknowledgments

Chapter 1. Introduction

Scorecards: General Overview

Chapter 2. Scorecard Development: The People and the Process

Scorecard Development Roles

Intelligent Scorecard Development

Scorecard Development and Implementation Process: Overview

Chapter 3. Designing the Infrastructure for Scorecard Development

Data Gathering and Organization

Creation of Modeling Data Sets

Data Mining/Scorecard Development

Validation/Backtesting

Model Implementation

Reporting and Analytics

Chapter 4. Scorecard Development Process, Stage 1: Preliminaries and Planning

Create Business Plan

Create Project Plan

Why “Scorecard” Format?

Chapter 5. Managing the Risks of In-House Scorecard Development

Human Resource Risk

Technology and Knowledge Stagnation Risk

Chapter 6. Scorecard Development Process, Stage 2: Data Review and Project Parameters

Data Availability and Quality Review

Data Gathering for Definition of Project Parameters

Definition of Project Parameters

Segmentation

Methodology

Review of Implementation Plan

Chapter 7. Default Definition under Basel

Introduction

Default Event

Prediction Horizon and Default Rate

Validation of Default Rate and Recalibration

Application Scoring and Basel II

Summary

Chapter 8. Scorecard Development Process, Stage 3: Development Database Creation

Development Sample Specification

Sampling

Development Data Collection and Construction

Adjusting for Prior Probabilities

Chapter 9. Big Data: Emerging Technology for Today’s Credit Analyst

The Four V’s of Big Data for Credit Scoring

Credit Scoring and the Data Collection Process

Credit Scoring in the Era of Big Data

Ethical Considerations of Credit Scoring in the Era of Big Data

Conclusion

Chapter 10. Scorecard Development Process, Stage 4: Scorecard Development

Explore Data

Missing Values and Outliers

Correlation

Initial Characteristic Analysis

Preliminary Scorecard

Reject Inference

Final Scorecard Production

Choosing a Scorecard

Validation

Chapter 11. Scorecard Development Process, Stage 5: Scorecard Management Reports

Gains Table

Characteristic Reports

Chapter 12. Scorecard Development Process, Stage 6: Scorecard Implementation

Pre-implementation Validation

Strategy Development

Chapter 13. Validating Generic Vendor Scorecards

Introduction

Vendor Management Considerations

Vendor Model Purpose

Model Estimation Methodology

Validation Assessment

Vendor Model Implementation and Deployment

Considerations for Ongoing Monitoring

Ongoing Quality Assurance of the Vendor

Get Involved

Appendix: Key Considerations for Vendor Scorecard Validations

Chapter 14. Scorecard Development Process, Stage 7: Post-implementation

Scorecard and Portfolio Monitoring Reports

Reacting to Changes

Review

Appendix A: Common Variables Used in Credit Scoring

Appendix B: End-to-End Example of Scorecard Creation

Bibliography

About the Author

About the Contributing Authors

WILEY END USER LICENSE AGREEMENT

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The Wiley & SAS Business Series presents books that help senior-level managers with their critical management decisions.

Titles in the Wiley & SAS Business Series include:

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Scorecard characteristics may be selected from any of the sources of data available to the lender at the time of the application. Examples of such characteristics are demographics (e.g., age, time at residence, time at job, postal code), existing relationship (e.g., time at bank, number and types of products, payment performance, previous claims), credit bureau (e.g., inquiries, trades, delinquency, public records), real estate data, and so forth. The selection of such variables and creation of scorecards will be covered in later chapters in much more detail.

Each attribute (“age” is a characteristic and “23–25” is an attribute) is assigned points based on statistical analyses, taking into consideration various factors such as the predictive strength of the characteristics, correlation between characteristics, and operational factors. The total score of an applicant is the sum of the scores for each attribute present in the scorecard for that applicant.

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