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ERM Symposium 2012 Washington, D.C. Jefferson Braswell Tahoe Blue Ltd 4/19/12 1

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Page 1: ERM Symposium 2012 Washington, D.C....• Predictive models – Credit scoring, fraud detection – Customer relationship management (CRM) – Risk Management, Underwriting – Regressions,

ERM Symposium 2012 Washington, D.C.

Jefferson Braswell

Tahoe Blue Ltd

4/19/12 1

Page 2: ERM Symposium 2012 Washington, D.C....• Predictive models – Credit scoring, fraud detection – Customer relationship management (CRM) – Risk Management, Underwriting – Regressions,

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Page 3: ERM Symposium 2012 Washington, D.C....• Predictive models – Credit scoring, fraud detection – Customer relationship management (CRM) – Risk Management, Underwriting – Regressions,

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• Requires the extraction of information and associations from data in order to attempt to predict future trends and behavior patterns.

• Involves the identification of relationships between explanatory variables and the predicted variables based on past occurrences in order to anticipate or predict future outcomes.

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Page 8: ERM Symposium 2012 Washington, D.C....• Predictive models – Credit scoring, fraud detection – Customer relationship management (CRM) – Risk Management, Underwriting – Regressions,

• Predictive models – Credit scoring, fraud detection – Customer relationship management (CRM) – Risk Management, Underwriting – Regressions, Support Vector Machines, Supervised Learning

• Descriptive Models

– Categorization and Classification, Cluster Analysis – Development of Simulation Sub-Models – Data Mining, Unsupervised Learning

• Decision Support

– Optimization, Maximization/Minimization, Objective Functions – Many variables and factors, including outputs of other models

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Page 9: ERM Symposium 2012 Washington, D.C....• Predictive models – Credit scoring, fraud detection – Customer relationship management (CRM) – Risk Management, Underwriting – Regressions,

Data Mining Anomaly Detection Association Rule Learning Clustering Classification Regression Summarization Regression Models Ordinary Least Squares Linear Regression models Discrete Choice models Time Series models Survival or Duration models

Supervised Learning Naïve Bayes Neural networks Radial basis functions Support Vector Machines Linear SVM Multiclass SVM Structured SVM Gradient boosting K-nearest neighbors Random Forest Random Multinomial Logit Random Naïve Bayes Geospatial predictive models Group Method of Data Handling

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Page 10: ERM Symposium 2012 Washington, D.C....• Predictive models – Credit scoring, fraud detection – Customer relationship management (CRM) – Risk Management, Underwriting – Regressions,

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Page 11: ERM Symposium 2012 Washington, D.C....• Predictive models – Credit scoring, fraud detection – Customer relationship management (CRM) – Risk Management, Underwriting – Regressions,

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Page 12: ERM Symposium 2012 Washington, D.C....• Predictive models – Credit scoring, fraud detection – Customer relationship management (CRM) – Risk Management, Underwriting – Regressions,

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Page 13: ERM Symposium 2012 Washington, D.C....• Predictive models – Credit scoring, fraud detection – Customer relationship management (CRM) – Risk Management, Underwriting – Regressions,

Source: “Banking and the Real Economy”, by Dr. Robert Mark and Jefferson Braswell, in the forthcoming Handbook of Financial Risk Information, to be published by Cambridge University Press

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Page 14: ERM Symposium 2012 Washington, D.C....• Predictive models – Credit scoring, fraud detection – Customer relationship management (CRM) – Risk Management, Underwriting – Regressions,

Source: “Banking and the Real Economy”, by Dr. Robert Mark and Jefferson Braswell, in the forthcoming Handbook of Financial Risk Information, to be published by Cambridge University Press

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Page 15: ERM Symposium 2012 Washington, D.C....• Predictive models – Credit scoring, fraud detection – Customer relationship management (CRM) – Risk Management, Underwriting – Regressions,

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Source: CRUZ, M.G. 2002. Modelling, measuring and hedging operational risk, John Wiley & Sons Ltd

Distribution of Operational Losses by Frequency and Magnitude

Page 16: ERM Symposium 2012 Washington, D.C....• Predictive models – Credit scoring, fraud detection – Customer relationship management (CRM) – Risk Management, Underwriting – Regressions,

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Supervised Learning

Business Unit Op Risk Score

Page 17: ERM Symposium 2012 Washington, D.C....• Predictive models – Credit scoring, fraud detection – Customer relationship management (CRM) – Risk Management, Underwriting – Regressions,

Source: Pages 2- 4, Thought Recognition: Predicting and Decoding Brain Activity Using the Zero-Shot Learning Model, Doctoral Thesis by Marc M. Palatucci , April 25, 2011, Robotics Institute School of Computer Science, Carnegie Mellon University

“Thought recognition is the application of pattern recognition and machine learning methods to discriminate patterns of brain activity associated with a particular cognitive state or physiological condition”. - Marc M. Palatucci

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Page 18: ERM Symposium 2012 Washington, D.C....• Predictive models – Credit scoring, fraud detection – Customer relationship management (CRM) – Risk Management, Underwriting – Regressions,

Source: Page 27, Thought Recognition: Predicting and Decoding Brain Activity Using the Zero-Shot Learning Model, Doctoral Thesis by Marc M. Palatucci , April 25, 2011, Robotics Institute School of Computer Science, Carnegie Mellon University

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Page 19: ERM Symposium 2012 Washington, D.C....• Predictive models – Credit scoring, fraud detection – Customer relationship management (CRM) – Risk Management, Underwriting – Regressions,

Source: Page 36, Thought Recognition: Predicting and Decoding Brain Activity Using the Zero-Shot Learning Model, Doctoral Thesis by Marc M. Palatucci , April 25, 2011, Robotics Institute School of Computer Science, Carnegie Mellon University

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Page 20: ERM Symposium 2012 Washington, D.C....• Predictive models – Credit scoring, fraud detection – Customer relationship management (CRM) – Risk Management, Underwriting – Regressions,

Page 17, Thought Recognition: Predicting and Decoding Brain Activity Using the Zero-Shot Learning Model, Doctoral Thesis by Marc M. Palatucci , April 25, 2011, Robotics Institute School of Computer Science, Carnegie Mellon University

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Page 21: ERM Symposium 2012 Washington, D.C....• Predictive models – Credit scoring, fraud detection – Customer relationship management (CRM) – Risk Management, Underwriting – Regressions,

Source: Page 26, Thought Recognition: Predicting and Decoding Brain Activity Using the Zero-Shot Learning Model, Doctoral Thesis by Marc M. Palatucci , April 25, 2011, Robotics Institute School of Computer Science, Carnegie Mellon University

4/19/12 21

Page 22: ERM Symposium 2012 Washington, D.C....• Predictive models – Credit scoring, fraud detection – Customer relationship management (CRM) – Risk Management, Underwriting – Regressions,

Source: Page 75, Thought Recognition: Predicting and Decoding Brain Activity Using the Zero-Shot Learning Model, Doctoral Thesis by Marc M. Palatucci , April 25, 2011, Robotics Institute School of Computer Science, Carnegie Mellon University

4/19/12 22

Page 23: ERM Symposium 2012 Washington, D.C....• Predictive models – Credit scoring, fraud detection – Customer relationship management (CRM) – Risk Management, Underwriting – Regressions,

Source: Page 59, Thought Recognition: Predicting and Decoding Brain Activity Using the Zero-Shot Learning Model, Doctoral Thesis by Marc M. Palatucci , April 25, 2011, Robotics Institute School of Computer Science, Carnegie Mellon University

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Page 24: ERM Symposium 2012 Washington, D.C....• Predictive models – Credit scoring, fraud detection – Customer relationship management (CRM) – Risk Management, Underwriting – Regressions,

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Page 25: ERM Symposium 2012 Washington, D.C....• Predictive models – Credit scoring, fraud detection – Customer relationship management (CRM) – Risk Management, Underwriting – Regressions,

Jefferson Braswell

[email protected]

4/19/12 25 Washington, D.C.

mailto:[email protected]

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