Financial Institution Advantage and the Optimization of Information Processing

A PROVEN APPROACH FOR CREATING and IMPLEMENTING EFFECTIVE GOVERNANCE for DATA and ANALYTICS

"Nearly every month, the media report cases of major blunders by financial institutions in trading, reporting of financial information, and mishandling of customer information, or censure from a regulator caused by a failure in data management or information processing."
From the Introduction

Financial Institution Advantage and the Optimization of Information Processing offers a key resource for understanding and implementing effective data governance practices and data modeling within financial organizations. Sean Keenan a noted expert on the topic outlines the strategic core competencies, includes best practices, and suggests a set of mechanisms for self–evaluation. He shows what it takes for an institution to evaluate its information processing capability and how to take the practical steps toward improving it.

Keenan outlines the strategies and tools needed for financial institutions to take charge and make the much–needed decisions to ensure that their firm′s information processing assets are effectively designed, deployed, and utilized to meet the strict regulatory guidelines.

This important resource is filled with practical observations about how information assets can be actively and effectively managed to create competitive advantage and improved financial results. Financial Institution Advantage and the Optimization of Information Processing also includes a survey of case studies that highlight both the positive and less positive results that have stemmed from institutions either recognizing or failing to recognize the strategic importance of information processing capabilities.

Introduction ix

Acknowledgments xv

Chapter 1 Financial Institutions as Information Processors 1

Financial Institutions Raison d Être 1

Cultural Issues 6

IT Literacy and the Spreadsheet Deluge 13

Other Challenges to Establishing an IT–Savvy Culture 19

Notes 25

Chapter 2 Strategic Hardware and Software Management 27

Overview 27

An Integrated Data Architecture 41

Information Processing Efficiency as an Institutional Objective 52

A Digression on Unstructured Data 58

Notes 60

Chapter 3 Data, Models, and Information 61

Model Risk Mania 61

Definitions and Epistemology 69

Data Quality 73

Models and Their Role 79

Regulatory Regimes and Guidance 88

Notes 99

Chapter 4 Model Risk Measurement 101

Three Phases of Model Management 102

Model Governance 103

Defining Model Risk 105

Objectifying the Downside 108

Model Risk Attribution: An Information Entropy Approach 121

Notes 126

Chapter 5 The Return on Analytic Assets 127

Measuring the Productivity of Models 128

Complementarity of Data Inflow with Information Processing 132

A Digression on Price Taking 134

Notes 135

Chapter 6 Data Risk Measurement 137

Strategic Data Acquisition 139

The Information Conversion Rate 140

Other Approaches for Data Risk Assessment 142

Notes 143

Chapter 7 A Higher Level of Integration 145

Alternate Views of Integration 146

Identifying Key Information Cycles 146

An Integrated Physical View 149

Multidimensional Information Asset Management 151

Chapter 8 A Strategy for Optimizing the Information Processing Complex 155

Evaluation 156

A Path toward Improvement 160

Notes 167

Chapter 9 Case Studies 169

The Pricing of Automobile Insurance 170

Moody s KMV 174

The London Whale 177

The Mortgage–Backed Securities Disaster 178

The Value of Annuities 180

Notes 182

Chapter 10 Conclusions 183

References 189

About the Author 193

Index 195

SEAN C. KEENAN, PHD, is Senior Managing Director–Model Risk Management at a leading international insurance organization. Prior to joining this organization, he was the Quantitative Risk Analytics Leader at GE Capital, Vice President in the Risk Architecture Group at Citigroup, Vice President at Moody′s, and Senior Econometrician at Loan Pricing Corporation.