agentsclimarketplace

Ai anomaly detection

Skill GAJETOso/financeskills/skills/ai-anomaly-detection

AI-powered skills for financial professionals. Comprehensive collection of finance, accounting, audit, and compliance skills for AI agents. IFRS/GAAP compliant with industry-specific applications.

Install
npx -y skills add GAJETOso/financeskills --skill ai-anomaly-detection

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What its author says it does

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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."

SKILL.md

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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.

Keep looking

Skills are one crate of 328,083. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.