CUNA Compliance And Risk Council Conference Insights: AI From a Risk & Compliance Perspective
AI adoption is not a matter of "if," but more of "when and how." Of the roughly 90 participants surveyed at the CUNA Compliance And Risk Council Conference, 76% said that they either don't know where to begin or are not clear on compliance & risk perspectives related to AI. That's why it's vital to understand AI's future, most importantly machine learning for predictive modeling and generative AI for content creation.
AI For predictive models: using explainability and data quality to build a strong foundation
AI For predictive models: using explainability and data quality to build a strong foundation
dQS: THE AUTOMATED CREDIT REPORTING COMPLIANCE SOLUTION
Ensuring data quality is crucial as illogical conditions from input data pose risks, and specialized tools, like Data Wrangler, can offer insights, but robust tools dedicated to identifying such conditions enable quicker issue resolution.
In data usage, Machine Learning relies on large datasets, emphasizing the importance of understanding both the training data, even if not yours, and how vendors utilize your data.
Here is a look at predicting funds availability:
Ensuring data quality is crucial as illogical conditions from input data pose risks, and specialized tools, like Data Wrangler, can offer insights, but robust tools dedicated to identifying such conditions enable quicker issue resolution.
In data usage, Machine Learning relies on large datasets, emphasizing the importance of understanding both the training data, even if not yours, and how vendors utilize your data.
Here is a look at predicting funds availability:
what should we do about generative ai?
To prepare for the evolving landscape of Generative AI, it’s vital to build familiarity by starting to use it now, especially for potential internal applications through limited pilots. Assessing AI applications, cleaning Metro 2 data, and understanding the data’s sources, volume, history, and update frequency are essential steps to ensure effective utilization and identify upstream data issues.
To prepare for the evolving landscape of Generative AI, it’s vital to build familiarity by starting to use it now, especially for potential internal applications through limited pilots. Assessing AI applications, cleaning Metro 2 data, and understanding the data’s sources, volume, history, and update frequency are essential steps to ensure effective utilization and identify upstream data issues.
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