Monolith Monitor: AI-Driven Banking System Sanity Check

Inspired by the inscrutable monoliths of '2001: A Space Odyssey', this project aims to create a 'Monolith Monitor' for banking software – specifically addressing the 'monolithic' legacy systems common in the industry. Drawing on the AI workflow scraping idea, the project focuses on automatically gathering data from logs, performance metrics, security alerts, and code repositories related to the banking system (similar to how Hyperion's Shrike gathers information). This data is then fed into an AI model (likely a combination of time series analysis, anomaly detection, and potentially NLP for analyzing log messages).

Story/Concept: Banks rely on complex, often decades-old systems that are critical to their operations. Like the HAL 9000 going haywire in '2001', unforeseen glitches can have catastrophic consequences. This 'Monolith Monitor' acts as a vigilant observer, constantly learning the normal behavior of the system and proactively identifying deviations that might indicate impending problems. It goes beyond simple threshold alerts by understanding the complex relationships between different system components and the context of events. It's not about replacing existing monitoring but adding an intelligent layer for early warning and root cause analysis.

How it works:
1. Data Acquisition: Uses APIs (if available) or targeted scraping techniques (like the 'AI Workflow for Companies' project) to gather data from existing monitoring tools (e.g., Splunk, Prometheus), system logs, database performance metrics, and even commit history from version control systems (e.g., Git). Emphasis is placed on non-intrusive data collection to avoid impacting the live system.
2. Data Preprocessing: Cleans and transforms the collected data into a suitable format for AI model training. This includes time series alignment, feature engineering (creating relevant metrics from raw data), and handling missing values.
3. AI Model Training: Employs a combination of AI techniques. Time series analysis (e.g., ARIMA, Prophet) for forecasting key performance indicators. Anomaly detection algorithms (e.g., Isolation Forest, One-Class SVM) to identify unusual patterns in the data. NLP models (e.g., BERT-based classifiers) to analyze log messages and identify potential errors.
4. Anomaly Detection and Prediction: The trained AI model continuously monitors the incoming data and flags anomalies that deviate significantly from the learned normal behavior. It also attempts to predict future issues based on current trends.
5. Explainable AI (XAI): Crucially, the system provides explanations for its alerts. Instead of just saying "System X is failing," it explains -why- it believes a failure is imminent, highlighting the specific data points that triggered the alert. This increases trust and allows IT teams to quickly investigate and address the problem.
6. Alerting and Reporting: Generates alerts through standard channels (e.g., email, Slack, PagerDuty) and provides detailed reports on system health, identified anomalies, and potential risks.

Niche, Low-Cost, High Earning Potential:
- Niche: Focuses specifically on legacy banking systems, a market often underserved by modern monitoring solutions.
- Low-Cost: Uses open-source AI frameworks (e.g., TensorFlow, PyTorch, Scikit-learn) and focuses on efficient data collection and processing to minimize infrastructure costs.
- High Earning Potential: Banks are highly motivated to prevent system failures, making them willing to invest in solutions that can improve reliability and reduce downtime. The explainable AI component further increases its value proposition by providing actionable insights.