The financial services sector is highly challenging, with participants seeking to gain a competitive advantage through technologies, business practices and the incorporation of more efficient operational methods. Artificial intelligence (AI) is becoming one of the essential tools that financial institutions possess that can assist in automating processes, improving the accuracy of predictions and forecasts, and improving customer service. However, financial institutions must establish a robust AI governance framework to drive AI implementations that encompass operational safety while remaining effective.
Where AI governance comes into play
AI governance collectively incorporates the rules, practices and processes by which artificial intelligence is directed and controlled. AI governance aims to enable organizations to take full advantage of AI while minimizing costs and risks.
In particular, because of the highly regulated nature of the sector, financial institutions must implement a robust AI governance framework to better oversee an AI strategy. The framework should include a clear strategy for using AI and guidelines for collecting and managing data. Also essential is the need to identify and mitigate risks in conjunction with actions to better maintain stringent data security as well as compliance with legal requirements.
Key benefits of investing in a practical AI governance framework include:
- Improved decision-making capabilities — better access to data and more accurate predictions
- Increased efficiency and lower costs — automation of routine tasks and processes
- Improved customer service and engagement — chatbots or other AI-enabled tools enhance customer interactions.
Investments to realize these benefits may include hiring dedicated staff members responsible for overseeing the framework, establishing clear guidelines and protocols and acquiring the tools to monitor and analyze data.
AI governance needs to stem from existing and future demands
Artificial intelligence capabilities are powerful but introduce new challenges that financial institutions must manage in a transformed operational environment. Organizations should embed controls to help measure and manage the models' objectives, data needs, desired performance levels and trustworthiness in alignment with the company's risk appetite.
By adapting to a more formalized, comprehensive and holistic governance approach, financial institutions can better develop improved and more controlled methods for managing the risks associated with AI models. Ultimately, they can better protect their organizations and customers from potential harm while attaining broader customer-centric benefits.
The vital factors where financial institutions need to implement formalized and strong AI governance practices center around their reliability, operational resilience and security, including data privacy.
- Reliability — is imperative especially as it relates to AI models' fairness and ethical components because wrong decisions can inadvertently penalize certain groups. Accountability and transparency can justify how information is used, how it impacts the decision-making process to reach outcomes, and provides channels for inquiry or challenge if needed.
- Flexibility— AI models must be constantly updated and revised to account for new risks, regulatory changes and other challenges in order to optimize performance and consistency. A robust governance framework can aid financial institutions in their goal to effectively assess, monitor and respond to model-related risks.
- Security— AI models can potentially expose financial institutions to a wide range of operational and regulatory vulnerabilities, including risks associated with disruptive incidents such as IT systems failure, cyber threats (e.g., data poisoning attacks) and regulatory compliance issues.
Challenges in implementing AI governance frameworks
To attain the majority of long-term benefits associated with an AI governance framework, critical challenges that need to be addressed include:
- Maintaining appropriate data collection, cleaning and analysis to produce more accurate, reliable and consistent results — including the validity of the data input function.
- Addressing the issue of bias in AI models which can lead to discriminatory and unfair outcomes.
- Adherence to accountability and transparency in AI systems allows understanding of how the organization uses data to make decisions, facilitating the ability to challenge or appeal decisions if necessary.
- Compliance with regulatory requirements, organizational policies, standards and industry best practices, including external and internal mandates. This challenge is especially vital for highly-regulated industries, such as finance, to avoid fines, penalties and reputational harm.
- Proactively managing and monitoring AI systems that identify and diagnose issues and allow for corrective remediation actions.