This document discusses using unstructured data like social media profiles and online activity to assess fraud risk and determine credit scores. It describes how a social score can be generated based on personal details, relationships, interests, and location data extracted from online profiles and activity. This social score and other data insights can then be used by financial institutions and other organizations to streamline credit evaluation, identify potential investors or customers, and reduce fraud and risks.

1. Anti fraud and Credit scoring using unstructured data

2. 265.352.198-13
marcos.sc@gmail.com
SINGLE SOCIAL ID

3. Name
Birthday
Gender
Jobs
Study
Relashionship
Location
Likes
Anti fraud
Identity
Online activity
Register supplement
Social Score
Crédito

4. Managerial position
Debtors
Elected for credit
Retail
Potential investors
Contacts
Online buyer
Retail
Economical potential
Credit level
Economical potential
Clusters
Level 2: US 50,000
Social Score
Crédito

6. Daniel Mendes . CEO
UX . Strategy . inovation Data Scientist
AI . Statistic . Predictive
Felipe Dias . CTO
Development . Data science
Executive sales speciatists
Credit analisys

7. Market
INSTITUIÇÕES
FINANCIAL
ENTERPRISES DATA
BUREAUS
FINTECH
STARTUPS

8. Competitors

9. Best practices in security
management
Highest level of security for
financial transactions
Exam that ensures security compliance Evaluation of information security
in the cloud
Security Protocol for transaction
data via Webservice
Legal opinion of the most renowned
Office that certifies every operation
“The rules of the game must be part of the game itself.” Lawrence Lessig, creator of Creative Commons

10. Daniel Mendes
CEO
www.dataholics.io
daniel.mendes@dataholics.io
+ 55 11 995736666
Skype: Daniel.fluida