MBA GSBA / SUNY former SOX Process Manager, Lead Auditor QMS certification, CIA, CISA
Minimizing non-performing loans (NPLs) using an effective AI-powered loan agent can be achieved through several strategies. These strategies revolve around leveraging AI's ability to process large amounts of data, predict future borrower behavior and take proactive measures to manage loan performance. 1. Enhanced Credit Scoring and Risk Assessment
There are several ways to mitigate asymmetries in banking caused by the use of Artificial Intelligence (AI). As AI is increasingly integrated into banking systems, it introduces risks of power imbalances, where large institutions and firms with access to more data and advanced technology gain an advantage over consumers and
Automatable Credit Scoring SIPOC process: Data flow: Input(x) > Process algorithm(m+b) > Output(y) 1. Establish supervised learning model 2. Train model using historical data (x) to predict creditworthiness (y) Basic equation: Credit Score (y) = m*(Financial History) + b for iterative optimization of the data fitting algorithm
Actuaries play a critical role in ensuring the financial stability and reliability of insurance companies by calculating reserves, assessing risks, and guiding financial strategies. However, several potential risks arise from their work, which can impact both insurance firms and policyholders. Potential actuarial risks to be mitigated: 1. Inaccurate Reserve Calculations
Warren Buffett’s investment strategy, often referred to as value investing, revolves around several core principles. Based on information in the book "Warren Buffett and the Interpretation of Financial Statements" by Mary Buffett and David Clark, the essence of Warren Buffett’s approach can be distilled into the
The SIPOC process notation is considered a "master process pattern" because it provides a comprehensive and structured AllinOne framework for understanding and modeling and last but not least analyzing processes. This approach is applicable universally to integral process & data contexts as exemplified by the sensory processes of
1. Transparency Explainable AI provides transparency into how decisions are made by algorithms. When users understand the rationale behind an AI system’s output, it demystifies the process and helps build confidence in its results. Transparency allows stakeholders to see the inner workings of AI models, making it easier to
[ AI = f(Comprehensive Financial Data, Advanced Machine Learning Algorithms) + Identification of Undervalued Assets, Market Trends Analysis - Risk Assessment and Mitigation Strategies ] Data: The comprehensive financial data utilized in the AI wealth management project includes historical market performance, individual investment preferences, risk tolerance levels, macroeconomic indicators, real-time market updates and
Opportunities & risks associated with AI projects arise from the project's process context and its impact on stakeholders. 1. Process Context * Data Quality and Availability: The process context, including data sources, preprocessing methods, feature engineering and model selection, significantly impacts the quality and performance of AI algorithms. * Algorithm
A cobot or collaborative robot, also known as a companion robot, is a robot intended for direct human-robot interaction within a shared space or where humans and robots are in close proximity. My design thinking example is about a cobot assisting in the real-time visual inspection in manufacturing done for
SIPOC process mapping can play a crucial role in helping stakeholders identify conceivable risks of non-compliance of AI projects with the EU AI Act by providing a structured framework to analyze the process flow, dependencies and data handling practices. 1. Supplier Identification (S) * Risk Identification: Stakeholders can identify potential risks
What’s the relevance and value of data without process context ? Does data need process context to be relevant and of value to stakeholders ? Do risks depend on how processes are deployed ? Are process maps a prerequisite for any risk assessment of AI systems, product & services development & deployment