The AI Book

CHAPTER 67Automated Machine Learning and Federated Learning

By Andreas Deppeler¹

¹Adjunct Associate Professor, National University of Singapore

Introduction

Artificial Intelligence (AI) is everywhere in financial services. Insurance marketing and underwriting models are based on behavioural micro-segmentation; call centre agents rely on natural language processing and voice transcription; banking compliance departments are using machine learning to reduce the number of “false positives” in anti-money laundering transaction monitoring. Nevertheless, financial services firms still seem to be slow in adopting AI.

There are three main reasons for this.

Shortage of Data

It is true that “the machine learning race is really a data race”.¹ Even though financial services firms are traditionally data-rich, many struggle with outdated legacy architecture, organizational silos and poor data quality. Executives are now realizing that data is a strategic asset that needs to flow freely throughout the organization. Building a data-centric organization therefore means going beyond compliance with regulatory obligations like BCBS 239 or creating a chief data officer role: it means generating new business value by unlocking data.

Lack of Trust in AI

As a discipline, Machine Learning is still in its infancy. We speak about “Data Science”, but heuristic and iterative methods of algorithm selection and tuning resemble alchemy more than science.² In the past 12–24 months, as companies’ unrestrained ...