The Banker's Algorithm can be conceptually applied to managing a stable electricity grid, especially in a complex and decentralized system like Switzerland’s. The goal is to avoid 'grid deadlock' conditions like brownouts, blackouts, or frequency collapse, by pre-checking demand requests and ensuring safe operating margins.
The SIPOC model (Suppliers > Input > PROCESS > Output > Customers) maps Switzerland’s energy system from energy sources (supply side) to consumption channels (demand side). Assessment of Green Transition * Upstream (Supply Side) * Fossil-heavy sources (oil, gas) rely on foreign suppliers, vulnerable to price shocks * Renewable energy relies more
Algorithmic transparency refers to the degree to which the workings of an algorithm, i.e. how it processes data, makes decisions, and produces outcomes are understandable, explainable, and accessible to relevant stakeholders (developers, users, regulators, etc.). 1. Informed Users: Ensure applicants are clearly informed when an algorithm is being used
Ensure that the bank's AI-powered loan system can safely allocate loans to applicants without overcommitting funds or ending up in a state where it can't meet future obligations. 1. Customer Loan Request * A customer requests a loan of e.g. EUR 50,000 2. AI Checks
A superintelligent, general-purpose humanoid robot is powerful but potentially risky. Key Characteristics * Massive adaptability: can perform thousands of tasks without explicit reprogramming * Context-aware reasoning: understands goals, priorities, emotions & complex language * Autonomous decision-making: makes choices without constant human oversight * Self-learning: improves performance across environments over time * Physical dexterity & cognitive
As humanoid robots evolve, we might see tiered performance levels tailored to different use cases, each with distinct acquisition and operating costs. Just like cars, computers, or smartphones, robots will segment into market categories. Low-Performer Tier Use case: Environments requiring safe, predictable, low-speed interaction as e.g. senior homes, reception
Managing Total Quality Management (TQM) as part of Governance, Risk, and Compliance (GRC) brings alignment, efficiency and resilience to an organization. 1. Holistic Risk Management * TQM identifies quality risks (e.g. defects, process inefficiencies, customer dissatisfaction) that can jeopardize compliance, reputation and financial performance. * When part of GRC, quality risks
AI (Artificial Intelligence) has the potential to significantly support humanity in tackling global challenges, particularly those outlined in the United Nations Sustainable Development Goals as e.g. SDG 3 re Good Health & Well-being: * AI diagnostics to identify diseases (e.g., cancer, COVID-19, retinal disorders) early and accurately. * Telemedicine and
While AI models are basically functions that map inputs to outputs, the SIPOC model extends this model notion by adding stakeholders in AI applications. As one and the same model will produce different results in different contexts and under different circumstances, for different individuals and stakeholders, end-to-end SIPOC process mapping
Process Horizon Blog Post Title @ https://blog.processhorizon.com Date Published Blog Post Link Manage risks at a commercial bank 2023-03-17 https://blog.processhorizon.com/ghost/#/editor/post/6414c46d38c9865bc20e3c6b Manage M&A Due Diligence for a big bank takeover 2023-03-19 https://blog.processhorizon.com/ghost/#/editor/post/6416e1b238c9865bc20e3cd3 Retail
The key challenge for banking with Agentic AI is ensuring that autonomous decision-making systems act transparently, ethically, and in alignment with regulatory and risk-management frameworks in a highly complex and regulated environment. The market risks of an AI-driven banking ecosystem include model-driven volatility, systemic errors from correlated AI behaviors, lack
This process map provides an operational AI governance process framework that integrates key international AI governance standards: * ISO/IEC 42001 AI Management System (AIMS) * ISO/IEC 42005 AI System Impact Assessment (AIIA) * ISO/IEC 23894 AI Risk Management * ISO/IEC 38507 AI Governance Guidance for Boards * NIST AI RMF Risk