What are the social implications of using AI in finance?

1 Human-AI collaboration

One of the main implications of using AI in finance is the need for human-AI collaboration, where humans and machines work together to achieve optimal outcomes. This requires financial professionals to develop new skills and competencies, such as data literacy, critical thinking, creativity, and emotional intelligence, to complement and oversee the AI systems. It also requires financial institutions to establish clear and transparent governance and accountability mechanisms, to ensure that the AI systems are aligned with the human values, goals, and standards.

2 Customer trust and privacy

Another implication of using AI in finance is the impact on customer trust and privacy. AI can enhance customer experience and satisfaction, by providing personalized and convenient services, such as chatbots, robo-advisors, and fraud detection. However, AI can also pose threats to customer privacy and security, by collecting and analyzing large amounts of personal and sensitive data, such as financial transactions, credit scores, and biometric information. Therefore, financial institutions need to ensure that the AI systems are respectful of customer consent, preferences, and rights, and that they comply with the relevant laws and regulations.

3 Social inclusion and fairness

A third implication of using AI in finance is the influence on social inclusion and fairness. AI can promote social inclusion and fairness, by expanding access to financial services and products, especially for the underserved and marginalized groups, such as the unbanked, the poor, and the rural. AI can also help reduce bias and discrimination, by using data and algorithms to assess creditworthiness, risk, and eligibility, rather than relying on human judgments. However, AI can also exacerbate social exclusion and unfairness, by creating digital divides, amplifying existing inequalities, and reproducing or introducing new forms of bias and discrimination, due to the quality, availability, and representation of data and algorithms.

4 Economic growth and stability

A fourth implication of using AI in finance is the effect on economic growth and stability. AI can foster economic growth and stability, by increasing productivity, efficiency, innovation, and competitiveness in the finance industry, and by creating new markets and opportunities for businesses and consumers. AI can also help improve financial regulation and supervision, by enhancing transparency, monitoring, and compliance. However, AI can also pose challenges to economic growth and stability, by disrupting the labor market, creating unemployment or underemployment, reducing wages or income, and changing the nature and distribution of wealth and power. AI can also introduce new sources of uncertainty, volatility, and complexity in the financial system, such as cyberattacks, systemic failures, or market bubbles.

5 Global cooperation and competition

A fifth implication of using AI in finance is the impact on global cooperation and competition. AI can facilitate global cooperation and competition, by enabling cross-border and cross-sector collaboration, communication, and integration, and by fostering mutual learning and exchange of best practices and standards. AI can also help address global challenges and opportunities, such as climate change, poverty, and financial inclusion. However, AI can also create tensions and conflicts in global cooperation and competition, by creating asymmetries and imbalances in the development, adoption, and regulation of AI, and by raising geopolitical and geo-economic issues, such as data sovereignty, trade disputes, and security threats.

6 Ethical and social responsibility

A sixth implication of using AI in finance is the responsibility to ensure that the AI systems are ethical and social, meaning that they respect the human dignity, rights, and values, and that they contribute to the common good and well-being of society. This requires financial institutions to adopt ethical principles and frameworks, such as fairness, accountability, transparency, and explainability, to guide the design, development, and deployment of AI systems. It also requires financial institutions to engage with various stakeholders, such as customers, employees, regulators, policymakers, civil society, and academia, to foster dialogue, participation, and trust, and to address the ethical and social concerns and expectations of using AI in finance.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?