Big Data

1. Traditional data

1.1. Name

1.2. Surname

1.3. Date of Birth

2. Web profile

2.1. Education

2.2. Place of work

2.3. Family status

2.4. Community

2.5. Newsfeed

2.6. Date of first picture uploaded

2.7. Number of posts

2.8. Number of connection

2.9. Profile picture

2.10. Phone numbers

2.11. E-mails

2.12. Places of employement

2.13. Graduate degrees

2.14. Children

2.15. Cities of presence (traveling)

3. Examples

3.1. http://doubledata.ru/en/index.html

3.2. https://findface.ru/login

4. Mobile data

4.1. Number of SIM cards

4.2. Short life time

4.3. Multiple locations

4.4. Small refunds

5. by Source

5.1. Google

5.2. Facebbok

5.3. Programmatic data

5.4. my Bank

5.5. my mobile provider

6. useful data

6.1. Who I will leave my details?

7. Solutions

7.1. Remote verification of the borrower’s identity

7.2. verifies the employment records, income and phone numbers of the borrower from the open sources

7.3. collates more information about the borrower on the stage of the loan provision

7.4. determine quality users, who've been previously declined.

7.5. Segmentation and relevant data