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Introduction

Digital risk encompasses more than cybersecurity. It includes all the ways new technologies can be applied to factoring and managing risk in the digital era. Continuous and real-time data enabled insights are a key contribution, and AI should be applied to risk management as well as customer interactions and transactions.

Highlights

  • The digital era requires a digital approach to risk management. The business environment has increased its velocity, complexity, and level of automation. Risk management needs to keep pace with all three.
  • Organizations should employ the tools of the digital era to manage the associated risks.
  • When starting to implement digital risk management, applying the techniques to specific business issues, using tools and data that are already to hand, can deliver tangible benefits.

Features and Benefits

  • Discover how to apply the tools of the digital era to manage risks.
  • Learn how to use data to gain insight on risks in real time and automate first-line responses for high velocity, digital era risks.

Key questions answered

  • How should the changing risk profile of the digital era be addressed?
  • Which digital era tools can help manage digital era risks?

Table of contents

Summary

  • Catalyst
  • Ovum view
  • Key messages

Recommendations

  • Recommendations for enterprises
  • Recommendations for vendors

The digital era requires a digital approach to risk management

  • Speed matters
  • Fewer human touchpoints are a two-edged sword for risk
  • Risk needs to be managed across the extended enterprise
  • Risk culture needs to evolve alongside technology

The tools of the digital era can also help manage its risks

  • Experience and intuition are sharpened by timely, trustworthy data insights
  • AI is a source and manager of risk
  • Back-room analysis tools can benefit the front office
  • Blockchain offers a high-integrity record of events
  • Agile risk management can keep pace in a rapidly changing world
  • Good architecture and governance are foundations for high-quality, relevant data

Appendix

  • Methodology
  • Further reading
  • Author