CRISC Notes.

This project provides a powerful tool for stock market analysis and portfolio management, combining multiple forecasting models, technical analysis, and risk management features. Built with modern Python libraries and Streamlit for an intuitive user interface.

Automated bond fund liquidity risk monitoring workflow with n8n + Flask + Wind API. Includes web dashboard, mock mode for demo, and configurable risk indicators.

This project aims at analyzing the risk appetite of banks. When the company receives a loan application, the company must decide for loan approval based on the applicant’s profile. Exploratory Data Analysis using excel was performed to draw insghts.

Governed AI workflow prototype for compliance-oriented decision support using RAG, human review, and audit logging.

Includes Source Code, PPT, Synopsis, Report, Documents, Base Research Paper & Video tutorials. AI-Based Financial Portfolio Management leverages AI to analyze market data, assess risks, and optimize investment portfolios, providing personalized strategies to enhance returns and reduce financial risks.

Sophos NIS2 Compliance Board to assess an institutions Cybersecurity Maturity Level

production-inspired fund liquidity risk monitoring and early warning system built with Flask

Interactive hands-on lab using Nessus Essentials for network risk assessments, including vulnerability scanning, analysis, and reporting.

GRC risk register project using Excel to identify, score, and mitigate security risks

AI-powered stock analysis engine using Random Forest to predict 5-day market movements with automated bias correction. Built with Python.

Developed a comprehensive Risk Mitigation Log identifying, assessing, and addressing potential project risks for the DIA Baggage Handling System. Categorized risks by probability and impact, defined mitigation strategies, assigned ownership,and allocated budget to ensure proactive management and minimize disruptions throughout project lifecycle.

High-dimensional loan transaction data. By reducing the dimensionality of the dataset, patterns were identified to help a financial institution mitigate risks such as loan defaults or early repayments. Key steps include data preprocessing, PCA implementation, and interpretation of principal components to uncover significant insights.

A summary dashboard of the Covid-19 cases in Italy (online version)

upcomingtrader's blog

CS6501 Information Security 2

An AI-powered healthcare application that predicts cardiovascular disease risk using H2O AutoML and Explainable AI. Built with Streamlit and deployed using Docker on Render for real-time, scalable, and interpretable health risk prediction.

Proactive communication regarding identified risks and potential impacts within the healthcare sector... | View more at LaravelMail.com