Popular GitHub Repositories for Systemic Risk

The Python Risk Identification Tool for generative AI (PyRIT) is an open source framework built to empower security professionals and engineers to proactively identify risks in generative AI systems.

The code for the paper 'Heterogeneous Risk Minimization' of ICML2021.

ARCH models in Python

A wechat robot based on ChatGPT with no risk, very stable! ?

AI-Native Risk Intelligence Systems, OpenDeRisk——Your application system risk intelligent manager provides 7* 24-hour comprehensive and in-depth protection.

Personal notes about scientific and research works on "Decision-Making for Autonomous Driving"

Security and Privacy Risk Simulator for Machine Learning (arXiv:2312.17667)

?This repo contains detailed notes and multiple projects implemented in Python related to AI and Finance. Follow the blog here: https://purvasingh.medium.com

This repository contains Cursor Security Rules designed to improve the security of both development workflows and AI agent usage within the Cursor environment. These rules aim to enforce safe coding practices, control sensitive operations, and reduce risk in AI-assisted development.

An Ethical Trajectory Planning Algorithm for Autonomous Vehicles

Smart Algorithms to predict buying and selling of stocks on the basis of Mutual Funds Analysis, Stock Trends Analysis and Prediction, Portfolio Risk Factor, Stock and Finance Market News Sentiment Analysis and Selling profit ratio. Project developed as a part of NSE-FutureTech-Hackathon 2018, Mumbai. Team : Semicolon

Credit Risk analysis by using Python and ML

RedFlag uses AI to identify high-risk code changes. Run it in batch mode for release candidate testing or in CI pipelines to flag PRs and add reviewers. RedFlag's flexible configuration makes it valuable for any team.

SCOPE-RL: A python library for offline reinforcement learning, off-policy evaluation, and selection

Build your autonomous hedge fund in minutes. AutoHedge harnesses the power of swarm intelligence and AI agents to automate market analysis, risk management, and trade execution.

Classifying people as high risk and low risk based on their distance to other people.

Official code repo for the O'Reilly Book - Machine Learning for High-Risk Applications

Using Bidirectional Generative Adversarial Networks to estimate Value-at-Risk for Market Risk Management using TensorFlow.

Optimize and improve the Label propagation algorithm

Driving risk assessment with deep learning using a monocular camera. Related paper: https://arxiv.org/abs/1906.02859