AI-Augmented Compliance: A Three-Part Exploration - Part 1

Introduction

In today's complex regulatory environment, compliance has become a critical function for businesses across all sectors. Failure to comply with relevant laws and regulations can result in severe consequences, including hefty fines, reputational damage, and sometimes even imprisonment for responsible individuals.

Compliance headaches: in 2019, Facebook was fined $5 billion by the Federal Trade Commission for privacy violations, while in the banking sector, in 2021 ABN Amro was fined €480 million by prosecutors in the Netherlands over money laundering allegations.

The compliance landscape is vast and varies significantly across industries. Government agencies must adhere to strict transparency and accountability standards. Pharmaceutical companies navigate complex regulations surrounding drug development, testing, and marketing. Banks face a myriad of rules related to anti-money laundering, know-your-customer practices, and capital requirements.

Artificial Intelligence (AI) is emerging as a powerful tool to assist organisations in navigating this complex compliance landscape. AI can be leveraged for knowledge extraction from legislative texts, comparison of existing policies against regulatory requirements, and even content creation for new compliance policies.

In this article series, we will consider three use cases for AI to help compliance teams.

Use Case 1: Evaluation against Existing Legislation

One of the primary applications of AI in compliance is to check organisational policies and procedures against existing legislation. Here's how this process typically works:

1. Loading the legislation: This step involves digitising and inputting the full text of relevant laws, regulations, or industry standards into the AI system. It may require converting physical documents to digital format using OCR technology or directly importing digital versions of legislative texts. The AI system needs to process and understand various document formats, potentially including PDFs, Word documents, or HTML.
2. Summarisation: In this step, the AI system uses natural language processing (NLP) techniques to analyse the full text of the legislation. It identifies key themes, requirements, deadlines, and obligations. The AI then generates a concise summary that captures the essential points of the legislation, making it easier for humans to quickly grasp the main regulatory requirements without reading through the entire document.
3. Expert input: Once the AI has generated a summary, it's reviewed by subject matter experts (SMEs) in the relevant field. These experts validate the accuracy of the AI's interpretation, add context where necessary, and clarify any ambiguities. They might also highlight specific areas of importance based on their industry knowledge. This step ensures the AI's understanding aligns with human expertise and industry best practices.
4. Procedure analysis: In this step, the organisation's existing policies, procedures, and practices related to the legislation are input into the AI system. This could involve uploading internal policy documents, process flowcharts, employee handbooks, or other relevant materials. The AI system then analyses these documents to understand the organisation's current approach to compliance in the appropriate area.
5. Comparison and gap analysis: The AI system now compares the organisation's existing procedures (from step 4) against the legislation's requirements (from steps 1-3). It identifies areas where the organisation is compliant, partially compliant, or non-compliant. The AI generates a detailed report highlighting gaps between current practices and regulatory requirements, potential risks, and areas needing improvement.

AI can significantly streamline the compliance process, allowing organisations to quickly identify areas where they may fail to meet regulatory requirements. It reduces the time and resources needed for manual compliance checks while improving accuracy.

A Practical Example - GDPR

Let's consider a practical example in the context of a multinational corporation adapting to the European Union's General Data Protection Regulation (GDPR):?

1. Loading the legislation: The company uploads the full text of the GDPR into the AI system, including all articles and recitals.
2. Summarisation: The AI generates a concise summary of the GDPR, highlighting key requirements such as data subject rights, consent requirements, data breach notification procedures, and the need for Data Protection Impact Assessments.
3. Expert input: The company's legal team and data protection officer review the AI-generated summary. They add notes about specific interpretations relevant to their industry and highlight areas of particular importance for their business model.
4. Procedure analysis: The company inputs its current data protection policies, privacy notices, consent forms, data breach response plans, and relevant IT security procedures into the AI system.
5. Comparison and gap analysis: The AI compares the company's existing procedures against GDPR requirements. It identifies several gaps:

• ?The current consent forms don't meet GDPR standards for explicit and granular consent.
• The data breach notification procedure doesn't include the 72-hour notification requirement.
• There's no formal process for conducting Data Protection Impact Assessments.
• The privacy notice lacks information on data subject rights and international data transfers.

Based on this analysis, the company can prioritise addressing these gaps to ensure GDPR compliance, potentially avoiding significant fines and reputational damage.

Conclusion

This example demonstrates how AI can efficiently process complex regulations and compare them against existing practices, enabling organisations to proactively identify and address compliance issues.

Our next article will explore how AI can help organisations prepare for new legislation and regulatory changes.

We have developed our Shaping Success with AI? (SS:ai?) framework to help Boards and senior leadership understand, introduce, and govern AI in their organisations. SS:ai? aims to help Boards move from Learner or User to Trailblazer and lead profoundly impactful organisations.

If you're a Board Director introducing AI into your organisation, don't hesitate to contact me for an exploratory discussion. You can reach me at [email protected] or book a 15-minute confidential session directly at https://bit.ly/ai-15min using the QR code below.

References

1. https://www.cnbc.com/2019/07/24/facebook-to-pay-5-billion-for-privacy-lapses-ftc-announces.html
2. https://fintech.global/2022/01/05/the-top-five-compliance-failure-fines-of-2021/
3. https://eur-lex.europa.eu/eli/reg/2016/679/oj