Real transaction datasets arrive with broken amounts, duplicate columns, mixed timestamp formats, missing values, typo-heavy city names, and invalid IP addresses. If the data is not cleaned first, the fraud signals stay buried.
See the workflow →Flagged after cleaning and feature generation
94 / 100
From messy transaction files to interpretable fraud predictions, every stage is designed for practical analyst workflows.
Standardize raw CSV uploads by fixing amount formats, normalizing timestamps, merging duplicate columns, and correcting invalid records.
Surface missing values, duplicate rows, transaction_id conflicts, invalid IPs, and distribution shifts before modeling begins.
Derive average spend, deviation from normal behavior, transaction velocity, unusual transaction time, and location drift for each user.
Track new devices, device reuse, city standardization, and geo mismatches that often reveal account takeover or synthetic activity.
Use engineered features to power a fraud model that returns a prediction and a transaction-level risk score for every record.
Show why a transaction was flagged with feature contribution hints so analysts can trust the decision and act faster.
Accept raw transaction CSV files
Bring in datasets containing amount, timestamp, location, device, IP address, payment method, and user activity fields without changing your backend flow.
Standardize messy real-world records
Merge duplicate columns like amt into transaction_amount, normalize timestamps, standardize city names, remove duplicate records, handle missing values, and flag invalid IP addresses.
Run EDA on quality and behavior
Highlight nulls, duplicates, invalid entries, and key distributions across transaction amounts, payment methods, device usage, and time-of-day activity.
Engineer features for fraud detection
Create user-level behavioral signals such as spending deviation, transaction velocity, unusual devices, location drift, and odd transaction times before scoring.
Predict risk and show why
Return a fraud prediction, risk score, and explanation layer so users can inspect the model decision instead of treating it as a black box.
Flagged Transactions
TXN-104882
User U-219 | Visa
TXN-204771
User U-031 | Wallet
TXN-778420
User U-558 | UPI
TXN-116903
User U-074 | Card
TXN-440018
User U-902 | NetBanking
Relationship View
Risk score raised by amount deviation, unseen device, and late-night velocity burst
Transaction Detail
March 23, 2026 | 02:14 AM
User: U-219 | Payment: Visa
City: Bengaluru | Device: Android 14
IP status: Corrected and validated
0
Cleaning and analysis stages
0+
Behavioral fraud indicators
0
Outputs per transaction
0
Explainable risk score
PARKHI.ai | Predictive Anomaly Recognition & Knowledge-based Hazard Intelligence | Tech Sagar 2026