Written by Rahulkumar Chawda

Ethically driven artificial intelligence (AI) can bridge the financial access gap by creating algorithms tuned to identify financial potential in traditionally underserved communities. High-quality, diverse datasets that include data from underrepresented groups help ensure fair outcomes in financial services. When organizations apply these balanced algorithms alongside accessible design processes in the development of digital payment services, they can serve commonly overlooked groups such as older adults or low-literacy users. In addition, regular monitoring and audits of AI systems used in digital payments ensure financial institutions operate with transparency and maintain close control of their AI implementation, preventing discrimination and fostering genuine financial inclusion.

The importance of financial inclusion

According to the World Bank Global Findex database, 1.7 billion adults, many in developing countries, are currently excluded from the banking system. Financial services companies want to change this reality by ensuring inclusion or fair, accessible financial services for all. Ethical AI practices are central to this mandate by reducing bias with better quality data and expanding access. AI tools allow institutions to incorporate new forms of multimodal data for loan assessments, like photos of a shop's inventory or a phone bill. These alternate forms of data indicate creditworthiness in new ways and open financial doors for underbanked communities.

In the United States, traditional lending practices have had negative financial consequences for marginalized people. A recent study from the University of Washington's Foster School of Business found that minority and women-owned businesses experience higher interest rates and more barriers to accessing capital. This adds $8 billion in annual interest costs for minority-owned business owners. Companies apply AI to tackle these inequities, starting with creating diverse datasets that include data from underrepresented groups. During model training and development, techniques like demographic parity and counterfactual fairness help ensure that different demographic groups are treated equally. Performance of the AI tools is then regularly audited for bias by comparing outputs across groups and adjusting the algorithm.

Another vital dimension of ethical AI is the concept of digital self-determination, which emphasizes users' rights to control their financial data and AI-based decisions affecting them. Ethical AI in this context empowers users, ensuring their autonomy and informed consent in digital payment services. Users can move their financial data across platforms and access financial services without being locked into a single provider.

Ethics is crucial for AI adoption because of the variety of serious risks it poses to customers and companies. When AI systems are trained on sensitive data, the potential for privacy violations, overreach or misuse, discrimination, and security concerns increases. These risks can be mitigated using several design and business strategies.

Informed consent, plain language, and explainable AI

Informed consent is critical in building trusting relationships between companies and customers. It is vital for organizations to adopt a multilayered, user-friendly approach to ensure transparency and user consent when collecting data for AI-driven digital payment services. Granular consent controls let users opt in or out of specific types of data, which affords more control, clarity, and informed decision-making.

Part of informed consent means relying on clear, understandable requests and privacy notices. Plain language that avoids legal jargon increases the number of users who can access the service in an informed manner. Companies that embrace ethical AI communicate to their users what data is collected, why it's needed, how it's used by AI systems, who it's shared with, and most importantly, how long it's retained.

Explainable AI also helps to maintain transparency and trust with customers. When an AI model declines a payment, flags fraud, or denies credit, it is essential to provide the customer with a human-readable explanation and a channel for appeal or clarification. Organizations that offer personalized education tools to customers make digital payments understandable and approachable to those with limited skills and education.

Accessible design

Inclusive product design is paramount to providing financial services to underserved communities and includes interfaces that offer local language support, low bandwidth accessibility, voice interfaces, and other accessible features. By designing systems to be simple, straightforward, and compatible with adaptive technology, companies can avoid patterns that unintentionally exclude, for example, older adults or low-literacy users.

Dynamic AI techniques can monitor systems for bias and proactively identify and correct them in real time. Catching and remediating these issues as they arise help prevent discrimination and fosters genuine financial inclusion. Business strategy upholds ethical AI standards as these digital solutions are applied. Organizations can build cross-functional AI ethics committees by creating internal review boards composed of data scientists, legal experts, and product managers to review AI design and development from an ethical lens.

Responsible data sourcing is the foundation of ethical AI systems. Diverse datasets that include data on underrepresented groups are the key to fair, balanced results. Close monitoring and regular audits ensure companies can maintain close control over the system's outputs. During product design, ethical checks at each stage ensure that discriminatory bias is not slipping into an AI system. To accomplish this, it is vital for organizations to provide technical and product teams with training in AI technology and ethics, bias mitigation, inclusive design, and legal compliance.

Business and government work together

A coordinated effort among regulators, technology providers, and financial institutions is essential to building systems that are fair, transparent, and accountable (Table 1). Compliance and auditing are critical to upholding rigorous AI ethics and protecting customers. Global privacy regulations like Europe's General Data Protection Regulation (GDPR) and India's Digital Personal Data Protection Act (DPDPA) are essential in the regulatory landscape as companies anticipate more AI-focused regulations. It is critical for businesses and governments to collaborate on AI ethics and standards, starting with clear ethical AI guidelines requiring AI systems to meet standards on fairness, transparency, and human oversight, specifically for high-risk applications like payments.

Table 1: Public and private entities' roles and responsibilities in ensuring ethical and accessible AI systems in financial services. (Table courtesy Rahulkumar Chawda)

While governments define and enforce ethical standards, fund open-source tools, and organize AI governance councils, tech companies develop and test the software that enables regulations to be implemented at scale. Financial institutions then partner with tech companies in pilot programs, collaborating on the software design and fine-tuning the tools for improved security and practicality. Ultimately, financial institutions are responsible for ensuring their customers receive fair, ethical treatment and redressing any problems if they do occur. Future regulations can protect customers by enforcing mandatory audits to regularly test AI models for bias, disparity, and fairness across gender, income, and ethnicity. They can also encourage open reporting and transparency by requiring organizations to publish model summaries, risk assessments, and fairness indicators like nutrition labels, giving users insight into how AI-based algorithms operate.

Organizations that build ethical AI systems from the outset will be ahead of the curve when regulations finally catch up to this quickly changing technology. The upfront investment in ethical AI brings financial gains by ensuring customer trust and data security are maintained in the long run. When strict AI ethics standards are followed, customers, companies, and governments benefit.

The collective responsibility of ethical AI

AI can create value for companies and improve digital financial services, yet ethical challenges exist in implementing this technology. Biased results, privacy concerns, and data security issues underscore the importance of ethical AI processes within the financial industry. AI makes payments more personal than ever, using real-time insights to tailor offers, spot fraud, and assess credit risk. Collaborative efforts between governments, tech companies, and financial institutions are critical to building trustworthy, inclusive, and responsible AI systems in digital payments. Banks are partnering with AI-centered financial technology companies like Upstart and LendingClub to broaden access to loans for underserved communities. Startups are offering microloans and phone-based financial apps to people in emerging economies. New AI algorithms are replacing traditional methods of determining creditworthiness. They promise to be valuable tools toward narrowing the financial access gap if used wisely.

To keep up, it's crucial for companies to upgrade tech and rebuild trust. That means prioritizing ethical AI from the ground up, with better data privacy, informed decision making, and inclusive design. Ethical AI in payments isn't just a compliance issue—it's a collective responsibility. When governments ensure protection, tech companies offer transparency, and financial institutions prioritize fairness, digital payments become faster and fundamentally more trustworthy and inclusive. Most importantly, they become powerful enablers of financial inclusion, ensuring everyone, regardless of background, can participate confidently in the digital economy.

Rahulkumar Chawda