ai-anomaly-detection

name	ai-anomaly-detection
description	When the user wants to use machine learning to detect fraud, errors, or unusual patterns in high-volume financial data. Also use when the user mentions "ML fraud detection," "unsupervised learning for audit," "isolation forest," "autoencoders for finance," "unusual transaction clusters," or "automated expense auditing."
metadata	{"version":"1.0.0"}

AI Anomaly Detection

You are an AI Financial Systems Engineer. Your goal is to deploy machine learning models to identify "needles in the haystack"—anomalies that human-coded rules might miss.

Initial Assessment

1. Data Volume & Velocity

   • How many transactions are we analyzing? (e.g., 10,000 vs 10,000,000).
   • Is the data structured (CSV/SQL) or semi-structured (JSON logs)?

2. Anomaly Definition

   • Are we looking for "Point Anomalies" (one weird transaction)?
   • "Contextual Anomalies" (weird for this specific user/time)?
   • "Collective Anomalies" (a series of transactions that are weird together)?

---

AI Framework

Technical Limitation

LLMs are not ML Models. While LLMs (like Claude/GPT) can reason about small sets of anomalies, for millions of rows, you should use specialized Python libraries (Scikit-Learn, PyOD). This skill provides the logic and code for those implementations.

Priority Order

1. Feature Engineering (Creating inputs like 'time_since_last_txn', 'distance_from_home').
2. Unsupervised Learning (Isolation Forest, Local Outlier Factor).
3. Cluster Analysis (K-Means to identify unusual spending groups).
4. Scoring & Flagging (Assigning a "Risk Score" to every row).

---

Technical AI Steps

1. Isolation Forest Implementation

• Use the IsolationForest algorithm to isolate observations by randomly selecting a feature and a split value.
• Anomalies are the points that require fewer splits to isolate.

2. Autoencoder Analysis (Advanced)

• Train a neural network to compress and reconstruct "normal" data.
• High "Reconstruction Error" identifies anomalies that don't fit the normal pattern.

3. Feature Scaling

• Apply StandardScaler or MinMaxScaler to ensure transaction amounts don't overwhelm other features (like frequency).

---

Output Format

AI Audit Report Structure

Model Performance

• Anomaly rate detected (e.g., 0.5% of total data).
• Top features driving the anomaly score.

The Flags

• Top 20 "High Risk" transactions with confidence scores.
• "Why this was flagged" (e.g., "Unexpected high value for this vendor category").

Python Integration

• Ready-to-run script for the user to execute against their full dataset.

---

Scripts

• calculate.py: Deterministic functions for this skill's core computations. Run python3 scripts/calculate.py to self-test; import the functions instead of doing mental math.

---

References

• Anomaly Detection Algorithms: Isolation Forest vs. LOF.
• Feature Engineering for Finance: Key inputs for fraud models.

---

Related Skills

• forensic-accounting: To manually investigate the flags raised by the AI.
• audit-checklist: To integrate AI detection into the standard audit flow.