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ai-anomaly-detection

ai-anomaly-detection

github.com/gajetosoData & databases4348 installsUpdated 4w ago
Repository

About ai-anomaly-detection

A skill for designing machine-learning anomaly detection workflows for financial data. It guides feature engineering, model selection, risk scoring, and audit reporting, while distinguishing LLM reasoning from analysis of large datasets.

Key features

  1. 01Assess data scale and anomaly types
  2. 02Engineer transaction features
  3. 03Apply Isolation Forest and LOF
  4. 04Scale input features
  5. 05Produce risk scores and audit reports

Use cases

  1. 01Explore unusual transactions in financial data
  2. 02Build a Python detection script for large datasets
  3. 03Summarize high-risk transactions for audit review

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/gajetoso/financeskills --skill ai-anomaly-detection

After installing, type/ai-anomaly-detectionin Claude Code to confirm it loaded.

Bundled files

References and scripts installed along with SKILL.md.

EXAMPLE.md
evals/
evals.json
evals/files/
expense_ledger.csv
references/
feature-engineering.md
ml-algorithms.md
scripts/
calculate.py

Details

From the project README

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

Related Skills

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

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