The use of AI in financial services continues to grow. Especially nowadays with the global COVID-19 pandemic, industry take up is increasing and use cases are expanding out from the back office and into customer-facing applications.

This presentation from UK Finance and EY seeks to advance the thinking on how financial services firms can implement a framework that supports explainable artificial intelligence (AI), thus building trust among consumers, shareholders and other stakeholders. — helping to ensure compliance with emerging regulatory and ethical norms.

This expansion brings many opportunities for industry to improve efficiency, better manage risk and provide exciting new products and services to customers.

However, to take full advantage of this opportunity, there needs to be trust.

As with all innovations, ethical considerations must keep pace with technological development. Building trust requires transparency and communication. Indeed, this is a topic of growing regulatory and government interest in many countries. Transparency and communication with customers have long been key considerations for financial services but AI will require new approaches and techniques if explanations are to be meaningful.

Effective explanations will also require a degree of subtlety; given the huge potential range of use cases, close attention to the context of each will be key.

In this session you’ll learn how Tailwind Traders took their support ticket text and audio files, convert and extract insight metadata from each ticket using Azure Cognitive Services Text Analytics and Speech-to-Text.

They then aggregated their findings to inform their product backlog and implement improvements.

Tailwind Traders have a great website and application for customers and partners. However, they are seeing an increased amount of support tickets regarding usage of these offerings. They want to store, analyze and extract insights from their text and audio data to make better product backlog decisions and reduce their support tickets.