5 Steps for Continuous AI Model Improvement: A Guide for Finance

By Eli Gill

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:

1. 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.
2. Performance monitoring: Once deployed, the model’s performance is closely monitored. KPIs specific to financial outcomes are tracked to assess the model’s effectiveness.
3. 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.
4. 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.
5. 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 is 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.

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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.

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