OECD Business and Finance Outlook 2021: AI in Business and Finance

The OECD Business and Finance Outlook is an annual publication that presents unique data and analysis on the trends, both positive and negative, that are shaping tomorrow's world of business, finance and investment. Artificial Intelligence (AI) has progressed rapidly in recent years and is being applied in settings ranging from health care, to scientific research, to financial markets. It offers opportunities, amongst others, to reinforce financial stability, enhance market efficiency and support the implementation of public policy goals. These potential benefits need to be accompanied by appropriate governance frameworks and best practices to mitigate risks that may accompany the deployment of AI systems in both the public and private sphere. Using analysis from a wide range of perspectives, this year's edition examines the implications arising from the growing importance of AI-powered applications in finance, responsible business conduct, competition, foreign direct investment and regulatory oversight and supervision. It offers guidelines and a number of policy solutions to help policy makers achieve a balance between harvesting the opportunities offered by AI while also mitigating its risks. Foreword Editorial Abbreviations and acronyms Executive summary 1 Trends and policy frameworks for AI in finance 1.1. Introduction to AI in finance 1.2. Insights from OECD.AI on AI diffusion in the financial sector 1.3. Framing policy discussions on AI in finance 1.3.1. Defining AI, its different types and its lifecycle 1.3.2. Policy through the lens of the OECD AI Principles 1.3.3. Policy through the lens of the AI system lifecycle 1.3.4. Policy through the lens of the OECD Framework for the classification of AI systems 1.4. National policies to seize opportunities and mitigate risks of AI in the financial sector 1.4.1. Several national AI policies promote AI development and deployment in the finance sector 1.4.2. Regulators are promoting safe and secure innovation while addressing specific challenges raised by the deployment of AI systems in financial services References Notes 2 AI in finance 2.1. Introduction 2.2. AI and financial activity use-cases 2.2.1. Asset management and the buy-side 2.2.2. Algorithmic Trading AI algorithms, HFT and potential unintended consequences 2.2.3. Credit intermediation and assessment of creditworthiness Risks of bias and disparate impact in credit outcomes Safeguarding mechanisms to mitigate risks of disparate treatment and bias 2.2.4. AI in blockchain -based financial services Using AI to augment the capabilities of smart contracts 2.3. Emerging risks and challenges from the deployment of AI in finance 2.3.1. Data management, privacy/confidentiality and concentration risks 2.3.2. Algorithmic bias and discrimination in AI 2.3.3. The explainability conundrum Auditability and disclosure of AI techniques used by financial service providers 2.3.4. Training, validation and testing of AI models to promote their robustness and resilience 2.3.5. Governance of AI systems and accountability 2.3.6. Other sources of risks in AI use-cases in finance: regulatory considerations, employment and skills Employment and skills 2.4. Policy considerations References Notes 3 Human rights due diligence through responsible AI 3.1. Introduction 3.2. Overview of human rights impacts of AI 3.2.1. Right to privacy 3.2.2. Right to non-discrimination 3.2.3. Right to fair trial and due process 3.2.4. Freedom of expression 3.2.5. Freedom of Association 3.3. RBC applied to AI supply chain actors 3.3.1. Six Step OECD Due Diligence Framework 3.3.2. Roles/responsibilities of different supply chain actors Developers: Including key actors involved in data collection & processing, planning & design, model building & interpretation Vendors End Users 3.3.3. Risk prevention/mitigation at different stages of the AI lifecycle 3.4. National / International / Industry-led efforts to address AI risks 3.4.1. Leveraging existing legislation Dual-use export controls Data protection RBC legislation 3.4.2. AI-specific initiatives 3.5. AI uses to support RBC 3.6. Looking forward References Notes 4 Competition and AI 4.1. Introduction 4.2. Competition problems associated with AI 4.2.1. AI and collusion AI and explicit collusion AI and tacit collusion 4.2.2. AI and abuses of dominance AI developing or implementing anticompetitive strategies Anticompetitive design of consumer-facing AI Personalised pricing 4.2.3. AI and mergers 4.3. Challenges for competition policy in addressing AI-related competition problems 4.3.1. Legal challenges 4.3.2. Investigative challenges 4.3.3. Competition policy approaches to addressing competition issues raised by AI Market studies and advocacy Co-operation with other regulators and stakeholders 4.3.4. Considering reforms to current enforcement frameworks and new regulatory measures 4.4. Conclusion References Notes 5 The use of SupTech to enhance market supervision and integrity 5.1. Introduction 5.2. Drivers and typology of SupTech developments 5.3. The benefits of SupTech 5.3.1. Improving detection capabilities Use cases by financial and securities regulators: better detecting market manipulation and insider trading Use cases by agencies involved in combatting corruption: better detecting criminal allegations and fraud Use cases by competition authorities: better detecting cartels and other types of anti-competitive practices Cartel screening Adapting techniques to investigate harm facilitated by algorithms Price monitoring tools 5.3.2. Improving efficiency in enforcement actions Use cases by securities regulators: better determining compliance with disclosure requirements and guiding enforcement actions Use cases by agencies involved in combatting corruption and foreign bribery: better resolving cases Use cases by competition authorities: facilitating evidence review in cartel investigations and enhancing the monitoring of remedies 5.3.3. Improving data collection Use cases by financial and securities regulators: improving regulatory reporting Use cases by competition and anti-corruption authorities: improving the collection of evidence during unannounced inspections 5.3.4. Improving data management 5.4. Challenges and risks of SupTech 5.4.1. Data quality, standardisation and completeness 5.4.2. Legal and procedural challenges Due process rights of companies as a legal challenge Data location as a legal challlenge 5.4.3. Algorithmic models and human oversight 5.4.4. Third-party dependencies, digital security and privacy concerns 5.4.5. Legacy systems 5.4.6. Financial and human resources, procurement rules, and barriers to change 5.5. Considerations for devising adequate SupTech strategies 5.5.1. Leadership, budget and skills 5.5.2. Collaboration between authorities, regulated entities and technology service providers within and across jurisdictions References Notes 6 Managing access to AI advances to safeguard countries’ essential security interests 6.1. Managing risk without stifling opportunities: new challenges require new solutions 6.2. Managing essential security interests related to foreign acquisitions of AI assets in context 6.2.1. New vulnerabilities emerge from international investment in advanced technology 6.2.2. Greater scrutiny has not ended the international investment boom in AI 6.3. Foreign investment in research: a new challenge calling for an adequate solution 6.4. Managing the implied risks of openness without forgoing benefits References Notes The OECD Business and Finance Outlook is an annual publication that presents unique data and analysis on the trends, both positive and negative, that are shaping tomorrow_s world of business, finance and investment