• Data Driven Compliance

  • De: Tom Fox
  • Podcast

Data Driven Compliance

De: Tom Fox
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  • Resumen

  • Welcome to Data Driven Compliance, the newest edition to the Compliance Podcast Network. In this podcast we will discuss how to use data to improve and enhance the effectiveness of your compliance program, creating greater business efficiency, all leading to more return on the investment for your compliance regime. Join host Tom Fox as he explores how data will drive your compliance program to the next level.
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Episodios
  • Data Driven Risk Management and Fraud Prevention

    Data Driven Risk Management and Fraud Prevention
    Feb 27 2024
    Are you struggling to keep up with the ever-changing compliance programs in your business? Look no further than Tom Fox’s award-winning podcast, Data-Driven Compliance. This podcast features an in-depth conversation about the uses of data and data analytics in compliance programs. Data-Driven Compliance is back with another exciting episode. Today, I take a solo turn to explore how data-driven compliance has moved from cutting-edge compliance to part of a best practices compliance program to becoming table stakes to do business in a multi-national world. AI and data-driven compliance solutions are revolutionizing risk management and fraud prevention practices, offering advanced analytics, machine learning, and automation to enhance decision-making processes, improve efficiency, and proactively address compliance risks. These tools are essential for companies to navigate the complex regulatory landscape. Understanding the transformative power of data-driven approaches in compliance and risk management is critical for every compliance professional. User adoption is critical, for even the most advanced technology will not be effective if not embraced and utilized by compliance professionals. There is also a need to balance automation and human judgment to ensure the effectiveness of these tools in risk management. Key Highlights: Data-Driven Compliance Tools for Risk Management Optimizing Decision-Making with AI-Driven Compliance Tools Transformative Data-Driven Fraud Detection and Prevention Tom Fox Connect with me on the following sites: Instagram Facebook YouTube Twitter LinkedIn
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    12 m
  • Data Driven Compliance: From Cutting Edge to Best Practices to Table Stakes

    Data Driven Compliance: From Cutting Edge to Best Practices to Table Stakes
    Feb 20 2024
    Are you struggling to keep up with the ever-changing compliance programs in your business? Look no further than Tom Fox’s award-winning podcast, Data-Driven Compliance. This podcast features an in-depth conversation about the uses of data and data analytics in compliance programs. Data-Driven Compliance is back with another exciting episode. Today, I take a solo turn to explore how data-driven compliance has moved from cutting-edge compliance to part of a best practices compliance program to becoming table stakes to do business in a multi-national world. Data-Driven Compliance Programs are revolutionizing companies’ identification of potential compliance issues, mitigating risks, and maintaining integrity. The Department of Justice has given these programs, which use data analytics and AI, the go-ahead because of their capacity to improve business efficiency and stop improper payments. The bottom line is that these programs have transitioned from being cutting-edge to being considered best practices. Active data monitoring and analysis, inter-departmental collaboration, and formal risk assessments are cornerstones of a robust compliance program. This is because of the power of data analytics and AI in compliance monitoring and the need for compliance professionals to adapt continuously to the evolving landscape of data-driven compliance. Implementing a data-driven compliance program avoids trouble and enhances business efficiency in today’s regulatory environment.  Tom Fox  Connect with me on the following sites: Instagram Facebook YouTube Twitter LinkedIn
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    13 m
  • Sherlock Holmes on Pattern Recognition in Data-Driven Compliance

    Sherlock Holmes on Pattern Recognition in Data-Driven Compliance
    Feb 13 2024
    Are you struggling to keep up with the ever-changing compliance programs in your business? Look no further than the award-winning Data-Driven Compliance podcast, hosted by Tom Fox. This podcast features an in-depth conversation around the uses of data and data analytics in compliance programs. Data-Driven Compliance is back with another exciting episode. Today, I take a solo turn to talk about data analytics and pattern recognition for the compliance professional in the context of the Sherlock Holmes short story, The Adventures of the Dancing Men. For a deep dive into the story, check out the episode on my Sherlock Holmes pod, Adventures in Compliance. In this story, Holmes decodes stick figures to solve the mystery. One of the tools he uses is pattern recognition, which plays a pivotal role in data-driven compliance programs, serving as a tool to identify anomalies and potential compliance issues. It involves the systematic observation of data to identify recurring elements or trends, even in seemingly random data, and interpreting these patterns within the appropriate context to provide meaningful insights. The importance of this process for the compliance professional cannot be overstated. Pattern recognition requires both creativity and flexibility, and it can help predict future outcomes, optimize processes, and inform decision-making in compliance programs. I also discuss the significance of an iterative approach, which involves continuous improvement based on new information and collaboration with others to enhance analytic capabilities and gain deeper insights. Check out this most unique and interesting episode of the Data-Driven Compliance podcast, where Sherlock Holmes instructs the modern compliance professional on Data-Driven Compliance.  Resources: The New Annotated Sherlock Holmes Sherlock Holmes FAQ  Tom Fox  Connect with me on the following sites: Instagram Facebook YouTube Twitter LinkedIn
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    20 m
Economía
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