honi05/FraudDetector

Fraud Detector Dataset

Synthetic multi-modal dataset created for the Reply / ReplyMirror Fraud Detection Challenge. It simulates real-world banking activity across five data types — transactions, users, locations, communications, and audio calls — designed to support fraud detection research combining tabular, textual, geospatial, and audio signals.

The companion system that consumes this data is available at Honi05/Fraud-Detector.

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Dataset Structure

FraudDetector/ ├── transactions.csv ├── users.json ├── locations.json ├── sms.json ├── mails.json └── audio/ └── *.mp3 (48 files)

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File Descriptions

ransactions.csv

The core file. Contains individual financial transactions with fields for transaction ID, amount, timestamp, merchant, and other contextual attributes. This is the primary target file — fraud predictions are made at the transaction level.

users.json

User profile records. Includes demographic and account information for each customer. Used to build behavioral baselines and detect deviations from a user's normal activity patterns.

locations.json

Geolocation records linked to transactions or user activity. Used to flag geographical inconsistencies such as impossible travel sequences or transactions in unusual regions.

sms.json

SMS message logs associated with users. Analyzed for phishing indicators, suspicious links, and fraud-related language patterns using LLM-based text scoring.

mails.json

Email logs per user. Similar to SMS — processed for social engineering cues, fraud language, and anomalous communication behavior.

udio/ (48 MP3 files)

Recorded phone call segments named by timestamp and participant (e.g., 20870117_010505-jolanda_orsini.mp3). Intended for voice-based fraud signal extraction.

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Usage

This dataset is used as input to a multi-agent fraud detection pipeline:

ash python main.py --data ./data --output ./output/predictions.txt

The pipeline ingests all five modalities and produces a ranked list of flagged transaction IDs.

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Team

Masala Techii — Owais Mehboob, Sanya Khan, Honi Arora