Singapore University of Social Sciences
Workshops

Leveraging AI in Finance

January 15, 2020

Registration has closed, thank you for your support!

Introduction

Artificial Intelligence (AI) promises to be the most disruptive class of technologies in the next ten years. AI-derived business value is forecast to reach $3.9 trillion in 2022. Yet, to many people, AI is the exclusive domain of people with well-developed mathematics and computer science skills. The aim of this workshop is to break down the complexities behind AI, make it accessible, and enable the participants to govern AI – regardless of their background.

In this workshop, participants will learn how to frame AI problems and guide its usage and implementation. The workshop will demonstrate the capabilities and limitations of AI with examples on how it is being used by forward thinking organisations. We will explore real-world case studies across a range of finance scenarios including credit scoring, insurance claims partitioning, customer segmentation, predicting interest rate rises, predicting interest rate values, predicting the value of stocks, finding market drivers and predicting market movements. The workshop culminates in developing and presenting ideas and plans for a new financial product that leverages AI.

The workshop will demystify key concepts, models, and deployment options. For example, concepts such as weights & biases, Deep Learning and Explainable AI (XAI); models such as SVC, ARIMA and PCA; and deployment options such as cloud, proprietary and open source.

Objectives

A. Knowledge and Understanding (Theory Component)

At the end of this workshop, participants should be able to:

  • Describe the evolution of Artificial Intelligence (AI) and how it is applied across industries and scenarios
  • Discuss the implications of AI for the world of finance
  • Examine potential AI-driven changes in an organisation

B. Key Skills (Practical Component)

At the end of this workshop, participants should be able to:

  • Assess AI projects in an organisation
  • Choose the optimal data, model, process and AI implementation method
  • Compose an AI implementation plan
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