Artificial intelligence in finance: a synthesis of human and machine

Devajyoti Ghose and George Soulellis

Contents

Foreword

Preface

Acknowledgements

The evolution of models

The foundations of risk and uncertainty

Uncertainty: a taxonomy

Model risk and uncertainty: a survey of the institutional landscape

Model specification risk and uncertainty

Model operation risk and uncertainty

Data, models and their purpose

Artificial intelligence in finance: a synthesis of human and machine

A deeper dive into machine learning methods: their opportunities, limitations, risks and uncertainties

10.

Measurement of risk and estimation of uncertainty in prediction models

11.

Using models under risk and uncertainty

12.

When models fail

Epilogue: models and the future

Computers will overtake humans with AI within the next 100 years. When that happens, we need to make sure the computers have goals aligned with ours.

Stephen Hawking (speaking at Zeitgeist 2015)

8.1 INTRODUCTION

The advent of the idea of “artificial intelligence” (although not the term “AI” itself) in the popular zeitgeist, dates back to Alan Turing’s influential paper in the philosophy journal Mind, where he proposed a response to the question “Can machines think?” (Turing 1950). Aware of the difficulties involved in defining terms such as “machine” and “think”, he proposed a test (which has come to be known as the Turing test), which transformed the question to “Can a digital computer (or more generally, discrete state machines) convince a human interrogator that it is human?”. Turing motivated his test with a game that he called the “imitation game”. The game is played with three players, A (male), B (female) and C (interrogator of any gender). The object is for C to determine who is male and who is female with a series of questions. The Turing test proposes that if a digital machine were to replace either A or B, and a human interrogator C makes the wrong decision about as often

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