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Frictionless finance controls – embed AI to drive end-to-end GRC operations


Organizations these days have to comply with an increasingly complex regulatory environment. Among many other regulations, the range extends from Control Objectives for Information and Related Technology (COBIT) and Payment Card Industry Data Security Standard (PCI DSS), to General Data Protection Regulation (GDPR) and the Sarbanes-Oxley Act (SOX). At the same time, businesses are operating under growing transaction volumes, and they are running more sophisticated financial processing routines, too.

The concerns don’t stop there. Fraud examiners estimate that organizations lose an equivalent to 5% of their revenue to digital fraud, equating to €3.7 trillion each year.[1] Internal and external audits detect only 19% of fraud, with organizations having to rely on whistleblowing to prevent 43% of it.[2]

What all this means is that organizations’ governance, risk management, and compliance (GRC) functions are heavily reliant on the expertise of stretched employees who receive very limited technology support.

Frictionless, end-to-end GRC operations

Artificial intelligence (AI) can be of substantial help to businesses in meeting these obligations. It’s especially useful when the processes to which it is being applied are drawn from across an integrated organization – from what we at Capgemini call the Frictionless Enterprise.

What’s needed is seamless, end-to-end GRC operations that combine a set of autonomous, AI-augmented business process controls developed for control interventions, built with AI architecture patterns and machine learning algorithms, and embedded within an integrated GRC platform.

This approach enables an organization to centralize all of its controls and eliminate key business process risks that can impact P&L and balance sheet. In turn, this can transform the finance function to achieve frictionless business outcomes, strengthened brand reputation, enhanced operational efficiency, improved fraud and revenue protection, and improved compliance. I’ve given examples of measurable benefits at the end of this article.

Application areas

Let’s expand a little on some of the outcomes a smart and enterprise-wide, AI-based GRC solution can deliver:

  • Enhanced controls monitoring – businesses can improve the quality of their controls on a real-time basis, and reduce audit duplications, too
  • Improved business processes – CFOs and finance and compliance teams can put in place best-in-class, cost-efficient, and effective processes and controls
  • Enhanced risk management – organizations can integrate their process risk identification, assessment, response, and controls framework functions
  • Improved fraud management – they can also enhance their fraud prevention, detection, and investigation as and when required

AI.GRC in action

The benefits of an AI-based approach to GRC are considerable. Here are real-world examples that we at Capgemini have delivered for a global FMCG client:

  • Up to $3 million reduction in negative P&L risk per control
  • 167% increase in data coverage per control.

Cost savings and efficiency improvements are of course highly desirable – especially at the kind of scale we see in the figures here.

But sometimes, it’s the less tangible factors that are really telling. For governance, risk management, and compliance, what perhaps matters most of all is also one of the hardest things to quantify – and that’s peace of mind.

To learn more about how Capgemini’s AI.GRC solution – part of our Frictionless Finance  offer – can eliminate business process risks around the clock and in real time across your process data, contact:

Thierry Frechet designs sustainable finance and accounting target operating models for multinational market-leading clients. 

[1] ACFE published Report to the Nations 2020, April 16, 2020.

[2] ACFE published Report to the Nations 2020, April 16, 2020.

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