1.

Record Nr.

UNINA9910812224503321

Autore

Ṣiddīqī Naʻīm

Titolo

Intelligent credit scoring : building and implementing better credit risk scorecards / / Naeem Siddiqi

Pubbl/distr/stampa

Hoboken, New Jersey : , : Wiley, , 2017

©2017

ISBN

1-119-28233-0

1-119-28229-2

1-119-28239-X

Edizione

[Second edition.]

Descrizione fisica

1 online resource (459 pages) : illustrations (some color), tables

Collana

Wiley and SAS Business Series

Disciplina

658.88

Soggetti

Credit scoring systems

Lingua di pubblicazione

Inglese

Formato

Materiale a stampa

Livello bibliografico

Monografia

Nota di bibliografia

Includes bibliographical references and index.

Sommario/riassunto

Intelligent Credit Scoring presents a business-oriented process for the development and implementation of risk prediction scorecards. The credit scorecard is a powerful tool for measuring the risk of individual borrowers, gauging overall risk exposure and developing analytically driven, risk-adjusted strategies for existing customers. In the past 10 years, hundreds of banks worldwide have brought the process of developing credit scoring models in-house, while 'credit scores' have become a frequent topic of conversation in many countries where bureau scores are used broadly. In the United States, the 'FICO' and 'Vantage' scores continue to be discussed by borrowers hoping to get a better deal from the banks. While knowledge of the statistical processes around building credit scorecards is common, the business context and intelligence that allows you to build better, more robust, and ultimately more intelligent, scorecards is not. As the follow-up to Credit Risk Scorecards, this updated second edition includes new detailed examples, new real-world stories, new diagrams, deeper discussion on topics including WOE curves, the latest trends that expand scorecard functionality and new in-depth analyses in every chapter. Expanded coverage includes new chapters on defining



infrastructure for in-house credit scoring, validation, governance, and Big Data.--