# AI Data Extraction

**Data Collection:**

***Transaction History:*** Users upload their previous transaction data, which includes details such as payment amounts, frequency, types of transactions, and associated vendors.

***User Information:*** Alongside transaction history, relevant user data such as demographics, location, and any other provided personal information is gathered.

**AI Processing:**

**Data Preprocessing*****:***  The AI cleans and organizes the uploaded transaction history and user information, identifying and rectifying any inconsistencies or missing data.

**Feature Extraction:** The AI algorithm extracts key features from the transaction data, such as spending patterns, regular payment recipients, frequency of transactions, and consistency in payments.

**Profile Generation:** Using machine learning algorithms, the system creates user profiles based on the analyzed transaction data. It identifies behavioral patterns, creditworthiness indicators, and risk factors.

**Credit Scoring:** The AI model generates an immediate credit score based on the derived profile, assessing the user’s reliability for accessing credited services.

**Important Information for AI Profiling:**

**Transaction Details*****:*** Payment amounts, frequency, types of transactions (one-time, recurring), and categories (utilities, rent, subscriptions, etc.).

**Consistency and Timeliness*****:*** Regularity and timeliness of payments as indicators of reliability.

**Spending Patterns:** Identification of consistent patterns in spending, understanding financial habits, and behavioral traits.

**Geographical Data*****:*** Location-based spending and transaction patterns.

**Privacy and Security Measures:**

**Data Encryption**: All customer data, including transaction history and soulbound tokens, are encrypted to ensure privacy and security, especially during AI analysis for credit scoring.