Review of Quantitative Analysis For Management (Hardcover)

The authors apply mathematical theories to build business models in very simple statements. In this way, we understand how to make decisions based on the models described in this book. The authors also reveal how to useMicrosoft Excel to solve business models. I think this is very crucialskill for the managers.



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Review of Management Information Systems for the Information Age (Hardcover)

I have used (and continue to use) this text for a college class on MIS.It's among the best textbooks I've ever come across.The book is filled with real world stories of real companies and how they have improved their competitive positions using IT.It also contains numerous 15-25 minute group activities and "On Your Own" assignments.Each chapter ends with a list of Key Terms, as well as several short answer and discussion questions.

Finally, and what I like most about the text, is a section on "Real HOT Group Projects."Many of these require creation of database reports or spread sheet pivot tables.So, you may find yourself diverting some time to teaching spreadsheet and database skills.But how can you teach the application of technology to managing and creating information, without actually using technology to do just that?



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Review of Winning the 3-Legged Race : When Business and Technology Run Together (Hardcover)

In the Preface, Faisal Hoque recalls a time (1999) when he observed that in company after company "how haphazradly people managed technology, particularly technology spending. The business principles they applied in other areas were not being applied to technology...In many firms it was bought and deployed on a hope anbd a prayer...I became convinced that business executives and technology executives still viewed each other across a chasm, even if they were now sitting at the same table. Only when they took off their business or technology hats and worked together to build the business could they succeed."

That description still applies to many organizations today. For their decision-makers, this volume offers invaluable information and counsel as they struggle to achieve effective convergence of business and technology and then manage it, a process as difficult as competing and winning in a "three-legged race." The metaphor is apt. Speed alone is insufficient. Balance is also essential, as are determination and endurance. The first step in the process is to "get BTM on the execxutive agenda" and understand what BTM is and can do; determine strategic positions and make the right investments; agree on "who's in charge"; and complete other preparations, meanwhile sustaining effective communication, cooperation, and collaboration between and among everyone involved.

The authors organize their material within two main sections. In Part I, they examine business technology management (BTM) at the most strategic levels, where the board, CEO, and entire senior management team must be actively involved if an organization expects to be successful. In Part II, they delve deeper into specific issues central to combining and coordinating business and technology initiatives in proper alignment with the given strategy. Readers will appreciate the provision of an "Executive Agenda" section at the conclusion of each chapter which reviews and summarizes key points, and also suggests what "next steps" should be taken. Here in a single volume is a rigorous and thorough examination of "The BTM Standard": a set of guiding principles that create a seamless management approach based on 17 essential capabilities grouped into four functional areas: governance and organization, managing technology investments, strategy and planning, and strategic enterprise architecture.



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Review of Mathematics and Mathematica for Economists (Hardcover)

Here is a practical comment for the typical economics graduate student: Buy this book if you want to start using Mathematica!

What does a typical economics graduate student need? First, s/he needs to review her/his knowledge of mathematics. Second, s/he needs to learn how to use the tool (math) to solve an economic problem. Cliff J. Huang and Philip S. Crooke's book is helpful in both respects. While they are introducing the basic language of Mathematica they review most of the undergraduate math. And they teach with a hands-on approach--that is, you solve almost every problem using Mathematica.

Mathematica is a great tool for economics graduate students, it helps you out in understanding the basic intuitions behind mathematical concepts--because you do not have to solve complex problems (Mathematica solves them for you), you just have to understand them. Of course, students are advised to consult their professors to choose the right computer software or programming language. Mathematica can do a lot, but some other software might be much more practical for your PhD thesis project...

[Also consider Differential Equations: An Introduction with Mathematica by Clay C. Ross]



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Review of The Credit Scoring Toolkit: Theory and Practice for Retail Credit Risk Management and Decision Automation (Hardcover)

As a professional statistician working in the banking industry I highly recommend this book as an invaluable reference for getting an overview of the credit risk management scenario as of today, as well as having an insight into its history and recent developments.

This book fills a long awaited gap for a comprehensive, thorough and complete outlook of the booming area of quantitative credit risk management. Since I graduated and started my professional career in the banking industry I was looking for such a text, both for personal use - as I tried to make a bridge between the academic knowledge acquired in the university and the business world - , as well as a pointer to indicate to colleagues from graduate school or other professionals wanting to start working on the area.

After buying lots of different books and spending hundreds of dollars I can finally say my search is over. This is a sound reference on credit scoring, serving both for the savvy risk professional wanting to brush up his skills as well as for the starter coming from the academy or other professional areas.

The book is outlined in eight thematic sections, which helps delivering the huge amount of information covered in a manageable way. Some of the highlights/weaknesses of the book are the following:

Section A: Setting the scene. This section covers an overview of credit, scoring and credit scoring setting the ground for what is coming next in the book. It glances through the Credit Risk management cycle (CRMC) and the reasons for and against use of scoring in credit retail operations. It also has a chapter on the history of credit as well as an introductory chapter on the mechanics of credit scoring, which summarizes the main technical aspects of scoring in the day-to-day business operation.

Section B: Risky Business. In this section the author contextualizes credit risk into the larger framework of risk management, relating it to the other three primary risks in the banking industry environment, namely business, market and operational risk. A nicely put overview of the philosophies of science on the Chapter on Decision Science is one of the highlights. It ends with a chapter on assessment of enterprise risks, from SME lending to middle and large corporations.

Section C: Stats and maths. This is the densest part of the book, covering in less than 100 pages and 4 chapters a content that spans over a dozen of statistics text-books. Kudos to the author for the extensive research carried but the section also has its shortcomings. Chapter 7 - ZPredictive statistics 101' abuses of mathematical notation and mixes some statistical concepts in its explanation of modeling techniques (specially regarding LPM - Linear Probability Modeling). Nonetheless, the author got the core concepts right and the overall coverage of the statistical methods (such as Logistic Regression and Regression Threes) in the other chapters are very good, with highlights to the definition of information value in terms of the Kullback-Leiber distance. The last Chapter on the section, on software and people resources is also very useful, going into some of the technical aspects for model implementation.

Section D: Data!: Data is the single most important aspect of any statistical analysis, and it is not different for credit scoring and decision automation. So it's not surprising that this is the largest section of the book and it deserves your special attention no matter your background. The chapters in this section cover the most relevant issues with data treatment, quality assessment and preparation that you are faced with in the industry. The chapter on Data preparation is particularly enlightening and discusses important decisions in credit scoring modeling, such as staggered versus static outcome windows, good/bad/indeterminate definitions, observation excludes, sampling considerations and use of external data.

Section E: Scorecard development. This section covers the practical aspects of day-to-day scorecard development, discussing transformation of variables (and statistical methods for it), characteristic selection, segmentation, reject inference, calibration, validation and development management issues. Highlights are its discussions of characteristic selection and reject inference, both comprehensive and filled with examples and references for further reading. It's the section that the scoring analyst will refer to the most while at the development of a new scoring model.

Section F: Implementation and use. A scorecard that's not implemented is useless by itself. This section covers the implementation of one or more developed scorecards, along with all the impacts to portfolio and business, and the monitoring of the implemented model. The Chapter on Monitoring is a highlight of this section, covering all the major issues with scorecard monitoring, such as misalignment, population and score drifts, stability reports, book rates, selection process, policy rules and proper treatment of overrides. It also has a nice chapter on finance (26), which gives insights into how to analyze financial impacts of current and tentative decision processes. There is also a linkage to Basel II model parameters, such as the Loss Given Default (LGD).

Section G: Credit Risk management cycle. This is a brief overview of other components of the CRMC, such as marketing, application processing, account management, collections and recoveries and fraud. The chapters in this section are somewhat concise but it seems to be intentional, as the focus of the book is specifically on credit scoring. Nonetheless, enough of the topics is covered to give the reader a good notion of how scoring fits into the credit cycle and other potential applications of the statistical techniques and process improvement. The chapter on Fraud is fairly comprehensive if you take in account that this is a book about credit risk (and fraud, strictly speaking is a concern of operational risk).

Section H: Regulatory environment. As the author poses, since the 1960s there has been an increasing regulation of financial institutions, and retail consumer credit did not escape it. This section of the book covers issues such as the Equal Credit opportunity Act (ECOA) in the US and other Fair Lending related legislation, as well as data privacy, capital adequacy and anti-discrimination. Of particular interest for practitioners worldwide are the comparisons of such legislation against a set of English-speaking countries, such as the US, Canada, Australia, UK and the Republic of South Africa. Although it does not cover the country I'm working on currently (Brazil), its broad coverage and analysis gave insights into understanding Brazil's current legislation on the matter and implications on credit risk model development.

I believe the author has done an excellent job on assembling all this information together in this format and highly recommend it for beginners and practitioners alike. The list price is somewhat above average but it's definitely worth it, as the only other option I have found so far to acquire the information covered by the book is to work in a retail credit area of a big player in the financial industry.



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Review of Business Statistics: Contemporary Decision Making (Hardcover)

The book is well written. The author kept the narration relevant to issues the business manager faces today.The sample problems are very instructive. There are inputs at the end of each chapter of how to use Excel and Minitab to solve the problems. I struggled with Stats in Undergrad. Ready now for the MBA class!!! Bring it on!!



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