Q&A Guide for Banking Professionals
Abstract
This document has been prepared to answer the most frequently asked questions from the perspective of risk, regulation, and operational efficiency of a financial institution, based on Flux Finance's fundamental philosophy and technological architecture. Our goal is to bridge the gap between the theoretical depth of our new risk modeling paradigm, which we call “The Physics of Trust,” and the practical and concrete needs of the banking sector.
The following questions and answers detail the added value that the Flux Engine offers compared to existing systems, the security and simplicity of integration processes, and our transparent approach to regulatory compliance.
This document serves as an invitation to lay the groundwork for potential collaboration and to establish a common language with our visionary partners with whom we can build the future of finance together.
The following questions are prepared for those who want to understand the project's essence and core value proposition.
Category 0: "What is the Project's Essence?" Questions (For Understanding Core Concepts and Value)
A) "I Don't Understand What You Do" (General Concepts):
The Most Basic Question: Your manifesto is quite philosophical; it includes concepts like 'Cosmic Constitution' and 'Trust Spinor.' Can you set aside all this theory and summarize what you do in its simplest form, in one or two sentences, as if you were explaining it to a banker?
Comparison Question: What is the fundamental difference between this 'Flux Trust Score' and the KKB score we already use, or our own internal risk scores? Why do we need yet another score?
Clarification Question: Your Lambda-F Monitor produces a risk score. Could you make this more concrete for us? How will this score be useful in our daily operations?
B) "What's in it for Me?" (Specific Value Proposition):
Problem-Oriented Question: As X Bank, one of our biggest problems right now is accurately assessing the risk of the new generation and 'thin-file' customers. What solution does your project offer for this specific problem?
Scale Question: The case studies on your site focus on major global crises. What's more important for us is the sudden distress of a single large SME in our portfolio or a specific sector. Can your model also detect these more 'micro' level risks?
C) "How Do We Start?" (Implementation and Cost):
Starting Question: As I understand it, this is a very comprehensive system. If we wanted to start, what would be the simplest, fastest, and most cost-effective first step? Do you have a small piece that can start generating value for us tomorrow?
The following questions assume that all materials on our website have been read.
Category 1: "What's in it for Us?" Questions (Practical Value and ROI-Focused)
We already have existing risk models (Credit Bureau scores, our own internal rating models, etc.). What specific risk does your Lambda-F or Flux Trust Score model capture that our current systems cannot see? Can you give us a concrete example?
Can you quantify the financial benefit this model would provide us? For example, by how many basis points do you foresee it reducing our non-performing loan (NPL) ratio in our SME loans portfolio? How much could it reduce our provision costs?
Your case studies (2008, Meme-stock) are global and US-focused. How do you think this model will perform within Turkey's unique economic dynamics (high inflation, currency fluctuations, etc.)?
Category 2: "Prove It" Questions (Accuracy and Reliability-Focused)
What is your model's false positive rate? A system that constantly generates 'risky' alerts will eventually be ignored. How did you calibrate the model to strike this balance?
What about the false negative rate? How accurately can your model detect a real risk when it arises?
Your case studies always focus on moments of crisis. How does your model behave during long periods when markets are stable and 'boring'? Does it produce meaningful signals under normal market conditions?
Category 3: "How Will It Work?" Questions (Integration, Data, and Security-Focused)
You mention 'On-Premise Setup' and 'Federated Learning' in your manifesto. What does this mean for us? Technically, what kind of setup will be done on our servers? How much workload will it create for our IT team?
For a pilot program, what specific internal data (e.g., anonymized customer transaction data, NPL data) will you need from us? How will you guarantee the security of this data sharing process and its compliance with KVKK/GDPR?
How will your model's outputs (alerts, scores) integrate with our existing risk monitoring dashboards and reporting systems?
Category 4: "How Do We Explain This?" Questions (Transparency and Regulatory Compliance-Focused)
The 'Trust Spinor' at the heart of your model is a highly theoretical concept. How do we explain this in a simple and understandable way to an auditor or a BRSA official?
You've mentioned your 'Explainable AI (XAI)' solution. Can you provide us with a sample report or screenshot showing the explanation for a 'Risky' score generated for an SME? How detailed and understandable is this explanation?
Is the model one that regulators would classify as a 'black box'? How transparent is the model's decision logic?
Category 5: "Pilot Program and the Future"
What will be the specific success criteria for the pilot program?
How will your post-pilot licensing model work?
What is on your long-term product roadmap?