THE AI/ML INDUSTRY LEADERS FORUM ™

Background: In the rapidly evolving landscape of community financial institutions, the integration of Artificial Intelligence (AI) is no longer a futuristic concept but a present necessity. Boards are asking their Executive Leadership on their plans to incorporate AI to obtain strategic and transformative benefits. They realize that embracing AI can drive significant efficiencies and enhance outcomes by automating routine tasks, analyzing vast amounts of data for better decision-making, and personalizing customer experiences. As your institution navigates the challenges of modern banking, AI stands as a powerful tool to streamline operations, reduce costs, and deliver superior services that meet the rising expectations of your clients. Now is the time to leverage AI to not only stay competitive but to lead the way in financial innovation.

Two distinct approaches in using AI within Financial Institutions

Deterministic and probabilistic models represent two distinct approaches in using AI within financial institutions. Deterministic models operate on precise data inputs and predefined rules, yielding exact outcomes based on fixed conditions. These models are best suited for scenarios where accuracy and exactness are paramount, such as calculating interest on loans or processing transactions with fixed rates. This is also needed where decisions need to be explainable, auditable and predictable.

In contrast, probabilistic models handle uncertainty by incorporating probabilities and likelihoods into their calculations. These models are valuable when dealing with dynamic variables and situations where outcomes are influenced by multiple factors. For instance, they can provide responses based on being trained on vast volumes of member financial and sentiment data.

In today's digital landscape, advanced tools like ChatGPT and other large language models (LLMs) complement these models by assisting in decision support, customer interaction, and data analysis. ChatGPT, as a probabilistic model, excels in natural language understanding and generating responses based on probabilities.

However, it is not ideal for use cases requiring deterministic outcomes, where exact precision is crucial. Instead, deterministic models are better suited for tasks like financial calculations and transaction processing that demand precise, rule-based decisions. By integrating deterministic and probabilistic models alongside AI technologies like ChatGPT and LLMs, financial institutions can achieve a balanced approach to decision-making, leveraging each model's strengths to enhance operational efficiency, risk management, and customer satisfaction through personalized services and data-driven insights.

AI Use Case Landscape:  For community financial institutions, focusing on use cases and outcomes is the most effective way to evaluate the implementation of AI. This approach ensures that AI investments are directly aligned with strategic objectives and tangible benefits. By identifying specific use cases—such as automating loan approvals, enhancing fraud detection, or personalizing customer interactions—institutions can clearly see how AI addresses their unique challenges and opportunities. Evaluating outcomes, such as improved efficiency, reduced operational costs, increased customer satisfaction, and enhanced risk management, provides concrete evidence of AI's value. This results-oriented evaluation helps prioritize AI initiatives that deliver the most significant impact, ensuring resources are wisely allocated and fostering a culture of innovation and continuous improvement. The following is a list of potential use cases within a Financial Institution:

1. Automation and Efficiency: AI can automate routine tasks such as rules or policy based decisioning, fraud detection from multiple sources, and document collection verification, freeing up human resources for more complex activities.

2. Personalization: AI can enable personalized customer experiences through tailored personalized onboarding, product recommendations, and targeted marketing campaigns based on individual preferences and behaviors.

3. Expanding the Relationship with members and customers: AI can be used to expand relationships with existing customers and members by providing them with the appropriate consumer or business product recommendation based on deep analysis of structured and unstructured data.

These AI categories collectively empower community financial institutions to streamline operations, mitigate risks, deliver personalized services, and stay competitive in the digital age.

Call to Action: 4 steps to go from ‘Analysis Paralysis’ to driving outcomes through AI.

1. Identify Relevant Use Cases: Start by identifying the most critical issues or opportunities where AI can make a significant impact for your organization. This could involve improving operational efficiency, reducing turnaround times, or enhancing customer/member experience.

2. Define Desired Outcomes: Clearly define what success looks like in measurable terms. This includes objectives such as increased efficiency percentages, reduced processing times, or improved satisfaction scores among members or customers.

3. Calculate Potential ROI: Develop an ROI calculator to quantify the potential return on investment from implementing AI solutions. This involves estimating cost savings, revenue increases, or other financial benefits compared to the investment required.

4. Execute a Pilot Project: Implement a pilot project within a defined timeframe, typically around 120 days, to test the hypothesis and validate the expected ROI. This pilot provides real-world data and feedback to refine the AI solution and demonstrate its effectiveness before full-scale deployment.

Click here to request sample use cases, outcomes and an ROI calculator. By thoughtfully selecting use cases and focusing on clear outcomes, community financial institutions can effectively leverage AI’s potential, drive meaningful improvements, and ensure sustainable success.