AI can deliver immediate benefits, but generating deeper insights requires companies to buy into its iterative process. Finance teams should not expect a simple “plug and play” model, but rather commit to continued AI monitoring and retraining to strengthen performance and compound value over time. Successful companies will invest time in the long term to consistently tweak and redeploy the AI.
The 5-Step Cycle of AI Monitoring & Model Improvement
Just like any financial strategy, AI models in finance are not static entities; they require ongoing adjustments and refinements to remain effective.
The process typically involves several stages:
- Initial deployment: When an AI model is first introduced, it’s built on existing data and preconceived algorithms tailored to finance-specific tasks, such as risk assessment, forecasts, fraud detection or investment predictions.
- Performance monitoring: Once deployed, the model’s performance is closely monitored. KPIs specific to financial outcomes are tracked to assess the model’s effectiveness.
- Feedback integration: Feedback, both from the system’s results and from human oversight, plays a pivotal role. When discrepancies or areas of improvement are identified, they feed into the next phase.
- Model retraining and updating: With new data and feedback, the AI model undergoes retraining. This might involve tweaking algorithms, introducing new data sets or refining the model’s focus.
- Redeployment and continuous AI monitoring: Post-retraining, the updated model is redeployed. The cycle of monitoring, feedback, and updating continues, forming an ongoing process of refinement. This requires a close partnership with data scientists and members of a technology team to ensure a system built in a way to support constant redeployment.
Leveraging Data and Feedback Loops
A critical aspect of this iterative process is the establishment of robust feedback loops. These loops are essential for two reasons:
- Data-driven refinements: Continual data flow from market trends, customer behaviors and financial results allows the AI models to adapt and evolve with changing financial landscapes.
- Human-AI collaboration: Human oversight ensures that AI models align with strategic objectives and regulatory requirements. Expert input helps in contextualizing AI decisions, especially in complex financial scenarios.
Enhancing Performance and Reliability of AI-Driven Solutions
The continuous improvement of AI models in finance is not just about maintaining their relevance, it’s about enhancing their performance and reliability in a high-stakes world.
Model Retraining Adapts the Model to Changing Realities
AI models, especially in finance, risk becoming obsolete if they are not regularly updated with new data and insights. Regular retraining ensures that these models can adapt to new market conditions, regulatory changes and evolving business strategies.
Continuous Learning Heightens Financial Decision Making
Continuous learning is the cornerstone of AI evolution in finance. It involves:
- Expanding data horizons: Incorporating diverse and comprehensive datasets ensures that AI models can make more nuanced and informed decisions.
- Advanced algorithms: Adopting newer and more sophisticated algorithms allows models to analyze complex patterns and make more accurate predictions.
- Cross-functional integration: AI models that interact with different business functions can provide a more holistic view, leading to better-informed financial decisions.
Building Reliable and Efficient AI Solutions for Finance
The ultimate goal of AI monitoring and refinement is to build AI solutions that are not only efficient but also reliable and trustworthy. AI models in finance must adhere to regulatory standards and ethical considerations, especially regarding data privacy and bias mitigation.
While AI can handle a significant amount of analytical work, human expertise remains invaluable for enhancing the performance and reliability of their AI-driven solutions. This ongoing refinement is not just a technical necessity but a strategic imperative to stay ahead in the competitive and dynamic field of finance.
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About the Author
Eli Gill is the Vice President of Engineering, Product, and AI at Paro, an AI-powered marketplace that delivers finance and accounting solutions to businesses through a combination of expert fractional talent, data-driven tools, and guiding insights. Eli has worked in the AI and machine learning field for over 10 years and served for five years as a limited term lecturer in the subjects of machine learning, data science, and AI at Purdue University.
