Artificial intelligence is rapidly transforming the financial landscape, offering powerful tools for forecasting, fraud detection, and risk assessment. However, introducing artificial intelligence into your financial workflows creates new challenges and risks. Controllers play a critical role in identifying and mitigating these risks so they can maximize the ROI of AI.
The Role of AI in Financial Decision Making
Controllers and CFOs are already using AI to support a wide range of financial activities and workflows. Machine learning algorithms analyze vast datasets and can reveal patterns that would otherwise go unnoticed.
AI analytics tools provide controllers with real-time insights and reliable predictions about potential market shifts. Some of the other finance functions being supported by AI include the following:
- Forecasting Cash Flow: Predicting future cash positions
- Conducting Risk Assessments: Evaluating credit and investment risks
- Detecting Fraud: Finding anomalies and flagging suspicious transactions
- Monitoring Operational Efficiency: Streamlining financial reporting and auditing processes
Artificial intelligence can reduce the workload on your finance team while promoting better efficiency and accuracy. However, AI systems are not infallible. You must adopt a structured risk management approach to address the potential challenges associated with the use of finance AI technology.
Key Financial Risks in AI-Driven Decision-Making
When integrating artificial intelligence into your financial workflows, you must be aware of the following risks:
Model Accuracy and Data Integrity
The adage “garbage in, garbage out” is a favorite among data analysts. This concept holds true when using artificial intelligence tools. AI models are only as good as the data they rely on. If a model is provided with inaccurate, incomplete, or biased data, its predictions will be flawed. You can address these concerns by:
- Implementing a robust data governance policy
- Regularly auditing AI-generated outputs for inconsistencies
- Establishing data validation procedures before feeding information into AI
Artificial intelligence tools must be properly trained and optimized to deliver a strong ROI. Even if your business has good data governance policies in place, regular audits are necessary.
Lack of Transparency and Explainability
Mini AI algorithms function as “black boxes,” making it difficult to understand how decisions are made. This lack of transparency can create compliance issues and hinder trust in financial forecasts. To mitigate this, you should:
- Prioritize AI solutions with explainable or transparent models
- Develop internal protocols for documenting AI-driven financial decisions
- Tasking your team with reviewing the same datasets and determining if they come to the same conclusion
The better you understand an AI platform’s logic and reasoning processes, the easier it will be to win the trust of the C-suite.
Cybersecurity and Privacy Concerns
Artificial intelligence platforms require huge volumes of data. While providing AI systems with plenty of high-quality information will lead to better decision-making, it also makes them prime targets for cyberattacks.
Make sure you are using AI tools that meet the cybersecurity standards of the finance industry. General use of third-party tools doesn’t provide the level of protection necessary to safeguard your financial data.
Concerns of Bias
AI models can inadvertently reinforce biases that are present in the data on which they are trained. This can lead to unfair financial decisions. Underlying bias can impact credit scoring, loan approvals, and investment strategies. If this sort of skewed decision-making goes unnoticed, it could negatively impact customers and damage an organization’s reputation.
How Controllers Can Mitigate Risk When Leveraging AI
To effectively manage AI-related risks, you should implement the following best practices:
Develop a Framework
Establish a structured approach to assess and monitor AI-related risks. Create a plan for addressing any concerns that emerge so you can maximize the ROI of your artificial intelligence investment.
Establish AI Governance Policies
A risk management framework is a critical step toward effective risk mitigation. However, you will also need detailed governance policies that:
- Define roles and responsibilities for AI oversight
- Outline how to report and resolve concerns
- Determine which teams will be involved in AI governance
Managing your artificial intelligence platform is a cross-functional process that must involve finance, IT, and compliance personnel.
Conduct Ongoing Audits
Regularly assess your AI models for accuracy and reliability. Compare AI-generated insights with traditional financial metrics and your team’s recommendations to detect discrepancies.
Your experienced finance team should be able to reach conclusions similar to those of the artificial intelligence platform when using the same data as a reference.
Train and Educate Staff
Equip your team with the skills to safely and ethically use artificial intelligence finance tools. Be transparent about your AI implementation initiative and focus on how the project will benefit your staff and the organization.
Reshape Your Organization With AI
Artificial intelligence provides a means of transforming financial operations. However, without proper oversight, AI can introduce significant risks to your business. As a financial leader, you are responsible for being proactive and mitigating the risks of artificial intelligence so you can ensure compliance while modernizing your financial workflows.
