I’ve recently published a short research paper titled: " A Numerical Simulation of Spherical Tank Drainage Using the Modified Euler Method: A Physics-Based Approach " It presents a simplified yet insightful Modified Euler’s method for solving differential equations and an physical applications on it. Feel free to check it out, and if you find it useful, I’d really appreciate your recommendation on ResearchGate. [https://www.researchgate.net/publication/390947672_A_Numerical_Simulation_of_Spherical_Tank_Drainage_Using_the_Modified_Euler_Method_A_Physics-Based_Approach ]

Government/Authorities can help keep AI fair and honest by making sure credit systems clearly explain their choices. People can then understand why they were given or refused credit, which helps build trust. Also, these systems should be regularly checked by regulators to make sure they don't treat people poorly because of their race or gender. Companies that use AI should fix any bias that is found.

There should be clear rules about what kinds of data can be used. When it comes to big credit choices, there should always be a person involved who can review or double-check the AI's choice. They need to keep track of how AI works and let experts look it over. They should be able to find out if AI was used to decide their creditworthiness and be able to ask why. I hope this helps.

Fairness and transparency in AI-driven credit risk evaluation cannot rely on algorithms alone. Regulators should mandate explainability, require independent bias audits, and enforce strong data governance standards. Embedding human oversight and aligning with global frameworks (e.g., EU AI Act) are key to ensuring equity and accountability in financial AI systems.