AI & Data Innovation Colab
Innovating with AI & Data Science
Welcome to the AI & Data Innovation Colab—a place for experimentation, collaboration, and practical applications of AI in finance and data science.
Predictive analytics and machine learning are improving how we assess risk, uncover insights, and support decision-making.
This section features AI-driven models focused on automation, efficiency, and problem-solving in finance, underwriting, and strategic decision-making. Some projects are fully developed, while others are in progress—but all aim to encourage discussion and exploration.
If you're interested in AI, automation, or data-driven solutions, take a look at the projects, share your insights, and explore new ideas with us.
Exploring AI, Machine Learning, & Predictive Analytics
How AI is Transforming Mortgage & Finance
Artificial Intelligence (AI) is changing the way industries operate by enabling machines to analyze data, identify patterns, and support decision-making.
A key component of AI, Machine Learning (ML), allows systems to learn from historical data and improve predictions over time. Predictive Analytics takes this further by using AI and statistical models to anticipate future trends, helping businesses manage risk, identify opportunities, and optimize financial performance.
In finance and mortgage lending, these technologies are already enhancing underwriting efficiency, improving risk assessment, and streamlining decision-making. AI-driven models help lenders make faster, more accurate loan approvals while reducing risk, while predictive analytics identifies early warning signs of financial distress, enabling lenders and investors to take proactive steps.
Colab #1
AI in Action: Predicting Bankruptcy with Machine Learning
This project applies classification-based machine learning to predict bankruptcy risk, helping lenders, investors, and businesses assess financial health and mitigate risk. Using historical financial data and predictive analytics, the model identifies key financial signals that indicate the likelihood of bankruptcy.
See the Model in Action: This project uses supervised machine learning techniques, including Calibrated XGBoost, which achieved 96.28% accuracy in predicting bankruptcies. You can explore the model, test it on financial data, or clone it from GitHub.
Why is this useful?
- Lenders can improve loan underwriting and adjust terms for high-risk borrowers.
- Investors can identify unstable companies before making investment decisions.
- Businesses can benchmark financial health and proactively manage risk.
How Does This Model Work?
To predict bankruptcy risk, the model analyzes key financial indicators—similar to how a bank evaluates a borrower’s creditworthiness before approving a loan.
Step 1: Understanding Financial Signals → The model looks at financial ratios, trends, and risk indicators to assess corporate health.
Step 2: Learning from the Data → Using advanced machine learning algorithms, the model studies historical bankruptcies to recognize risk patterns.
Step 3: Making Smarter Predictions → The best-performing model, Calibrated XGBoost, achieved 96.28% accuracy, balancing precision and recall to ensure reliability.
Explore the Model on GitHub:
koso9/Bankruptcy-ML-Prediction: Machine Learning Model to Predict Bankruptcy
- Click the link above to access the project.
- Read the README file for an overview of how the model works.
- If you're a developer, clone the repository and run the model on your computer.
