Explore Pencil Sketch Drawing

Text Prompt

Data Collection and Preprocessing:

Aggregate data from multiple sources: transaction logs, user devices, and external data (news, social media).
Normalize and scale data for consistent analysis.
Behavioral and Transactional Analysis:

Implement rule-based methods for immediate flags (e.g., unusual IP addresses, high-value transactions).
Develop behavioral models using historical transaction patterns to set dynamic thresholds.
Machine Learning Integration:

Apply hybrid models:
LSTMs for time-series transaction forecasting.
Isolation Forest and Autoencoders for unsupervised anomaly detection.
Train models continuously to adapt to evolving fraud patterns.
External Contextual Analysis:

Use Knowledge Graphs to integrate macroeconomic factors and external events into risk assessment.
Leverage LLMs for summarizing relevant insights.
Real-Time Alerts and Verification:

Automatically flag anomalies for immediate review.
Employ automated or manual verification techniques, such as contacting users or analyzing flagged data.
Feedback Loop:

Use flagged transactions to improve detection models and minimize false positives over time.
Model

sketch-lora
Image Size

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Aspect Ratio

1:1