Machine Learning: The Backbone of AI
September 29, 2025
There is a considerable amount of uncertainty surrounding the future of Artificial Intelligence (AI) and Machine Learning. It can be difficult to understand how these tools function, especially when new discoveries seem to be developing with breakneck speed. New technology may create vulnerabilities, but also presents exciting new opportunities for improved speed and efficiency. As the use cases for AI increase, it becomes progressively more important and crucial for individuals and organizations to feel a greater comfort with the fundamentals of AI and their potential effects.
What is Machine Learning
Computer systems have performed tasks for humans since their invention but the increased computing power in the last decade has brought us the AI we all know and love (or hate) today. Large Language models or LLMs (eg. GPT, DeepSeek, Gemini) apply machine learning techniques to learn patterns and deliver responses. Machine Learning is a subcategory of AI that uses algorithms in order to interpret data in ways similar to humans. Much like a human student, these algorithms are designed in such a way that improvement comes with the experience of practice, testing and adjusting rather than receiving new instructions. Practice for a machine learning algorithm can be led by human developers where the algorithm is given the answers to the test or unsupervised where conclusions are reached independently. Through this process the algorithms produce a good approximation for the human brain using a neural network to recognize patterns, extract key insights, and make predictions. Computers can parse data not only faster but in a more sophisticated way than humans on their own.
Why Machine Learning Matters to Financial and Service Organizations
Good decisions are made with good information. Machine learning expands an organization’s ability to identify risks, recognize vulnerabilities, and ensures processes remain effective. Service providers generate and care for increasingly enormous quantities of data. Machine learning is designed to thrive in an environment drowning with data such that scalability, accuracy, and timeliness are prioritized
As professionals explore these opportunities they must consider whether data quality is maintained, the function of AI outputs are explainable, and security practices are in place.
Practical Use Cases for Organizational Managers
-Manage risk by identifying anomalous transactions for potential fraud
-Forecast client behavior, budget outcomes, or supply chain logistics based on organizational trends
-Perform cursory tests of managerial controls to check compliance with regulatory requirements
-Discover operational inefficiencies
-Analyze common client obstacles and explore strategies for improved service and solutions
-Streamline accounting and financial reporting processes by automating repetitive tasks related to data management and organization
-Write code for internal reports
-Verify the completeness of data provided
AI and Machine Learning are technologies not to be watched but understood and used starting now. There are great opportunities to be explored but there is also an obligation we all now have; to carefully govern the ways we use it to ensure trust and accountability remain in all we do. Individuals and organizations who embrace it provide themselves with a competitive advantage in fulfilling their missions.
For additional guidance, contact Larson & Company today. Find out more about our IT and SOC Audit services developed specifically to serve the needs of companies of all sizes in a wide range of industries.
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