As Financial Technology (FinTech) continues to revolutionize how people and businesses manage their finances, the industry faces an escalating challenge: fraud. With the increasing sophistication of check forgery and alteration, traditional fraud detection methods are no longer sufficient. Artificial intelligence (AI) has emerged as a game-changer, and at VALID Systems, we are enabling financial institutions to detect and prevent fraudulent activities in real-time through advanced machine learning models, pattern recognition, and predictive analytics. Our AI-driven fraud prevention systems analyze vast amounts of transaction data, behavioral anomalies, and adapt to evolving threats, offering unprecedented efficiency and accuracy. 

Figure 1: VALID Systems’ 2024 processed transactions, RTLA

(Real-time Loss Alerts) processed, and fraud attempt dollars identified with RTLA. 

However, as AI takes center stage in fraud prevention, ensuring its ethical, efficient, and effective use is paramount. A robust data governance program plays a critical role in mitigating risks associated with biased algorithms, data privacy breaches, and regulatory non-compliance. Establishing clear policies around data quality, security, and transparency is essential for maintaining our clients’ trust and meeting legal requirements. By integrating strong governance frameworks with AI-powered fraud detection, VALID Systems strives to maintain the right balance between innovation and responsibility, safeguarding both our clients’ data and our reputation. 

We recently had the opportunity to attend the DGIQ + AIGOV Conference, hosted by DATAVERSITY in Washington, D.C.. Our participation reinforced the critical role of data governance in ensuring ethical AI implementation. 

At VALID Systems, we have been using AI within our Machine Learning risk models since 2017, which is why we were especially interested in learning more about best practices and guidelines to ensure our data governance program covers the ethical use of AI. 

Without a robust data governance framework, organizations risk poor quality data, biased decision making, sociotechnical issues (human AI interactions), and regulatory violations. At VALID, ethical AI hinges on the foundation of sound data management practices, and governance ensures that data is clean, accurate, representative, and compliant. This white paper outlines the importance of integrating governance with AI to achieve ethical, transparent, and trustworthy outcomes. 

The Importance of Data Governance in Ethical AI 

 In a world increasingly driven by data, the December 2024 DGIQ and AIGOV Conference, hosted by DATAVERSITY, served as a powerful reminder of both the responsibility and opportunity organizations have to harness the full potential of their information assets. At its core, data governance is more than a set of rules; it is the backbone of trust in the digital age. It defines who can act on what data, under what circumstances, and by what methods, ensuring that every decision is grounded in reliability, consistency, and integrity. 

By establishing robust policies, procedures, and standards, data governance transforms data from a raw material into a strategic enabler, aligning organizational practices with compliance requirements, risk management priorities, and transparent decision-making. As enterprises navigate the evolving landscape of artificial intelligence and digital transformation, the conference underscored the urgent need to embrace governance as a tool for empowerment, accountability, and innovation. 

Data governance is not just about managing information; it is about unlocking its true potential to shape a brighter and more secure future. 

Data governance is highly adaptable, conforming to the unique needs of each organization. It is a continuous process of learning, adapting, and improving—not a static definition. A useful way to conceptualize data governance is:

• Empowerment, Not Control: Data governance is often misunderstood as a restrictive framework designed to limit data use, but in reality, its primary aim is empowerment. It fosters responsible and effective data utilization by providing clear guidelines, processes, and tools that enhance decision-making and innovation. When it is effective, it enables employees and organizations to leverage data responsibly and effectively. By ensuring clear policies, access to quality data, and robust ethical guidelines, governance fosters innovation and informed decision-making.  
• A Shared Responsibility: Everyone in the organization, from the CEO to front-line employees, plays a role. Effective data governance is not confined to a single department but requires collaboration across all levels of an organization. From the CEO setting the vision for ethical AI use to data analysts ensuring proper data handling, every employee has a role to play. Organizations that integrate shared responsibility into their governance models foster a culture of accountability and compliance. 

Data governance is not a siloed initiative owned by a single department, it’s a collective effort that involves every individual in the organization, regardless of their role.

• Data Management: Provides the infrastructure and processes that make shared responsibility feasible. It ensures that data is cataloged, documented, and accessible so that all stakeholders can contribute meaningfully. 

• Data Stewardship: Assigns accountability to specific individuals or roles, ensuring that governance policies are actively applied. Stewards advocate for governance, mediate between departments, and promote best practices across the organization. 

• Executive Leadership: Sets the vision for data governance and ensures alignment with strategic goals. 

• Data Teams: Implement governance frameworks, maintain data quality, and monitor compliance. 

• Front-Line Employees: Adhere to governance standards are embedded in their daily operations and provide feedback on challenges and opportunities. 

By clearly defining roles and responsibilities, organizations can ensure governance is uniformly applied, reducing silos, enhancing collaboration, and strengthening accountability. 

• Consistency: Shared responsibility ensures that data governance is applied uniformly across all departments, reducing silos. 

• Accountability: With everyone playing a role, no single point of failure exists, and issues can be quickly identified and resolved. 

• Collaboration: Encourages departments to work together, aligning data usage with organizational goals. 

• Define clear roles and responsibilities for data governance at all levels. 

• Provide training and resources to educate employees on governance principles. 

• Establish cross-functional governance team to oversee policies and initiatives. 

A Journey, Not a Destination: Data governance continuously evolves through learning and refinement. It is an ongoing process that requires continuous learning, adaptation, and refinement. Organizations must consistently assess their governance policies, respond to emerging risks, and incorporate new technologies and regulatory changes. 

• Start Small: Begin with focused initiatives that address immediate challenges, such as improving data quality or ensuring compliance. 

• Iterate and Expand: Build on initial successes, gradually expanding the scope of governance to include advanced practices like data lineage and ethical AI. 

• Monitor and Refine: Regularly review governance policies and processes, incorporating feedback and lessons learned to drive improvement. 

• Establish a feedback loop where employees can report issues and suggest improvements. 

• Use metrics and KPIs to measure the effectiveness of governance initiatives. 

• Celebrate milestones and successes to reinforce the value of governance efforts. 

The key to successful data governance lies in the intersection of people, processes, and technology—with people and processes taking the lead. It fosters a culture of data stewardship, collaboration, and strategic data utilization. 

Data Governance and Ethical AI: Key Considerations 

Data governance and ethical AI are intrinsically linked. A strong governance framework serves as the foundation for ethical AI by ensuring data is high quality, unbiased, transparent, and compliant with regulations. Below are key areas where data governance directly impacts ethical AI development: 

1. Data Quality and Bias Mitigation

AI systems are only as reliable as the data they are trained on. Poor-quality data leads to poor AI performance and biased outcomes. A strong governance framework ensures that data undergoes cleansing, validation, and standardization, addressing inconsistencies and promoting diversity within datasets. Bias mitigation strategies within governance prevent discriminatory practices and ensure AI models are fair across all demographics. 

• • Garbage In, Garbage Out: AI models rely on training data. Poor data quality leads to biased or inaccurate outputs. A strong governance framework ensures data cleansing, validation, and standardization. 

• • Identifying and Addressing Bias: Governance frameworks analyze data for biases, ensure diversity in datasets, and implement bias-mitigation techniques. For example, training data for a loan approval AI model must be balanced and representative to prevent discriminatory outcomes. 

2. Transparency and Explainability

Transparency is vital to building trust in AI systems. Governance frameworks help document the data pipeline and AI processes, ensuring that data origins, transformations, and decisions made by AI models are clearly communicated. This transparency fosters trust among stakeholders and makes AI models more understandable and explainable. 

• • Data Lineage and Documentation: Governance frameworks track data origins and transformations, allowing stakeholders to understand how AI models reach decisions. 

• • Auditing and Monitoring: Governance enables auditing and ongoing monitoring, ensuring transparency, bias detection, and compliance with ethical standards. 

3. Regulatory Compliance and Ethical Standards

Data governance frameworks ensure compliance with data privacy laws such as GDPR, CCPA, and others, ensuring AI systems uphold user rights and meet regulatory requirements. Moreover, governance promotes ethical standards by embedding fairness, privacy, and accountability principles into AI systems, protecting both users and organizations from ethical lapses. 

• • Meeting Regulatory Requirements: Data governance aligns AI practices with legal frameworks such as GDPR and CCPA, ensuring compliance with data privacy laws. 

• • Enforcing Ethical Principles: Governance policies embed ethical considerations, ensuring AI systems promote fairness and respect human rights. 

4. Building Trust and Accountability:

• • Transparency and Trust: Responsible data governance fosters trust in AI technologies, which is crucial for public adoption. 

• • Accountability: Clearly defining roles and responsibilities ensures ethical AI practices are upheld. 

Bridging Innovation and Responsibility

Two Sides of the Same Coin: What’s Different? 

• AI Governance: Focuses on ensuring AI systems are ethical, transparent, and accountable. It governs how AI systems behave and make decisions. 

• Data Governance: Ensures the data feeding AI systems are reliable, secure, and free from bias. It manages the “fuel” that powers AI. 

Despite these differences, AI relies on high-quality, well-governed data. Without robust data governance, AI systems risk producing unreliable or unethical outcomes. 

Deep Interconnections: Why They Need Each Other 

The interconnectedness of AI governance and data governance is evident in the following key areas: 

• Data Quality: Data governance ensures clean, unbiased data for AI models, while AI governance ensures the models built on that data deliver equitable outcomes. 

• Bias Mitigation: Data governance promotes diverse datasets, and AI governance applies frameworks to prevent discriminatory AI behavior. 

• Transparency and Explainability: Data governance documents data lineage, enabling AI governance to explain AI decisions clearly and credibly. 

• Compliance: Data governance aligns with laws like GDPR1 or CCPA2, while AI governance ensures AI systems adhere to these regulations in their operations. 

Together, these processes ensure AI systems are trustworthy, ethical, and effective. 

Should They Be Managed Separately? 

The approach depends on organizational priorities: 

• Integrated Approach: Organizations heavily reliant on AI may benefit from combining AI and data governance into a unified framework for seamless alignment. 

• Separate but Aligned: Companies with mature data governance practices may opt to manage AI governance separately, building on existing processes while maintaining collaboration. 

Ultimately, the success of ethical AI relies on a balanced and strategic approach to both governance types. 

Analogy: Ingredients and Cooking 

• Data Governance: Think of it as managing the ingredients in a recipe—it ensures the ingredients are fresh, measured correctly, and stored properly. 

• AI Governance: Think of it as overseeing the cooking process—it ensures the recipe is followed ethically, the cooking is done responsibly, and the final dish meets quality standards. 

Real-world example of successful Data Governance in Ethical AI 

Hospitals leveraging AI for predictive analytics, such as assessing patient readmission risks, face ethical challenges related to privacy, bias, and accuracy. To address these issues, leading institutions have implemented robust data governance frameworks. These frameworks ensure data quality standards, privacy controls, bias detection mechanisms, and transparent documentation of decision-making processes. As a result, AI models enhance patient outcomes while maintaining ethical integrity and regulatory compliance. 

According to a 2018 Health Catalyst article on patient readmissions, "the specific rates vary by institution, [but] nearly 20 percent of all Medicare discharges nationwide led to a readmission within 30 days. Preventing even 10 percent of these readmissions could save Medicare $1 billion."

Mayo Clinic exemplifies this shift by transitioning from diagnosing and treating diseases to preventing and curing them. This strategic vision focuses on eliminating diseases or preventing their onset rather than managing symptoms and disease progression. In alignment with this goal, Mayo Clinic has prioritized AI investments in monitoring and diagnostics, dedicating 47% of its AI funding to this area as of early 2023. By leveraging extensive patient data, these AI tools enable faster and more accurate analysis, facilitating earlier detection, more precise and less invasive diagnoses, and quicker interventions.4 

To address ethical concerns, leading hospitals have adopted comprehensive data governance strategies: 

• Data Quality and Standardization: Strict data entry protocols, standardized medical terminologies, and validation processes ensure reliable AI model training. 

• Privacy and Security: Role-based access controls, data encryption, and anonymization techniques comply with regulations like HIPAA. 

• Bias Detection and Mitigation: Analyzing demographic disparities and implementing techniques to reduce bias ensures equitable AI outcomes. 

• Transparency and Explainability: Documenting data sources, model algorithms, and decision-making processes builds trust in AI predictions. 

The Outcome: 

• Improved patient care through accurate AI-driven predictions. 

• Ethical AI usage through privacy prioritization, bias reduction, and transparency. 

• Enhanced public trust in healthcare AI technologies. 

The World Health Organization (WHO) supports the responsible use of technologies like large language models (LLMs) to assist healthcare professionals, patients, researchers, and scientists. However, it has expressed concerns that the caution typically applied to new technologies is not consistently exercised with LLMs. This includes adherence to principles such as transparency, inclusion, public engagement, expert oversight, and rigorous evaluation.5 

VALID Systems’ Commitment to Ethical AI and Data Governance 

Through the evolvement of our data governance program, we have established a strong foundation in ethical AI over the past seven years by focusing on the following elements: 

• AI Monitoring and Bias Audits: 

• Conduct bias audits on our ML models. 

• Set up reports to monitor data drift. 

• Perform weekly audits of production features (by client and ML model). 

• Fostering a Culture of Ethical AI Awareness: 

• • Employees working with ML models do not have access to PII data and understand AI ethics and responsible data use.  

• Scalable and Evolving Governance: 

• • As our organization grows, we continuously refine and scale our governance program to meet evolving needs and industry standards. 

A key principle of data governance is shared responsibility. It is not confined to a single department but is a collective effort requiring contributions from all employees. Moreover, data governance is an ongoing journey, not a final destination. 

At VALID Systems, we prioritize fairness in AI, ensuring that our ML models avoid discrimination and deliver equitable outcomes. Transparency is another cornerstone our models must be explainable, allowing stakeholders to understand AI-driven decision-making processes. 

By integrating data governance with ethical AI principles, organizations can harness AI’s potential responsibly, ensuring fairness, transparency, and accountability. 

Conclusion 

The integration of data governance and ethical AI is not just a necessity, it is a strategic imperative. Organizations that embrace strong governance frameworks will be better positioned to harness AI's potential responsibly while ensuring compliance, fairness, and transparency. As AI continues to shape the future of FinTech, a commitment to ethical principles and rigorous governance will be essential in fostering trust, mitigating risks, and driving sustainable innovation. By prioritizing governance, organizations can responsibly unlock AI’s full potential while promoting fairness, transparency, and accountability. 

At VALID Systems, we recognize that AI’s transformative power must be balanced with responsibility and accountability. By integrating robust data governance practices with our advanced AI-driven fraud prevention systems, we uphold fairness, transparency, and compliance. This dual commitment allows us to deliver cutting-edge risk and data analytic solutions while safeguarding client trust and protecting their data. As we continue to innovate, we remain steadfast in our dedication to ethical AI practices, setting a benchmark for excellence in the FinTech industry. 

References 

1. GDPR: General Data Protection Regulation 

1. CCPA: California Consumer Privacy Act 

1. Retrieved from https://downloads.healthcatalyst.com/success-stories/Accuracy-of-Readmission-Risk-Assessment-Improved-by-Machine-Learning-1.pdf 

1. Retrieved from https://businessdevelopment.mayoclinic.org/wp-content/uploads/2024/11/The-future-according-to-Mayo-Clinic-How-AI-is-transforming-the-hospital-CB-Insights.pdf 

1. Retrieved from https://www.who.int/news/item/16-05-2023-who-calls-for-safe-and-ethical-ai-for-health