1. BaiRong Financial Information Services, Ltd. (100Credit.com)
Mr. Hong Zhao， CFO, 100Creidt.com
May 2016
100Credit.com

2. Company Profile（www.100credit.com）
BaiRong Financial Services, Ltd.（100Credit.com）, uses big-data technology to provide precision marketing, credit check, credit monitoring, anti-fraud, and other risk management services for banks, insurance companies and other financial enterprises.
Vast amount of data with rich dimensions, allowing precise credit profile building for individual loan applicants:
610 million individuals with real-name identifier, 1.08 billion devices with unique equipment ID.
Diverse data sources, constant update:
online and offline retail, financial institutions, Telecom operators, social & media network, airlines, educational organizations, major brands, etc.
daily data update 1.8 TB
Objective third-party platform:
do not lend money, focus on building an infrastructure for lending

3. Challenges faced by lending institutions
Demand
Supply
Vs.
Lending institutions’ Wish-List
Want to reach more users, especially internet users with spending potential
Want to expand credit offerings to individuals in all regions, especially regions with not-so- well developed off-line banking infrastructure
Want to know their potential customers better as well as capabilities to make instant decisions, requires large amount of data and rich details of individual profile
People’s Bank of China (PBOC)
Financial activities data for approx. 300 million individuals , less than 25% of China’s population
Sporadic coverage outside economically developed regions
Information source limited to financial institutions that are authorized to submit data to PBOC, very limited data dimensions

4. Evolution: Traditional credit model to big-data credit model
PBOC data has always been the foundation of traditional credit models.
However, more lenders are adopting big-data credit model, which allows then to extend credit to individuals who would otherwise be rejected under the traditional model due to lack of data.
Traditional Model
Big-Data Model
Pros
Traditional model uses mostly “strong” variables that directly resulting from financial transactions.
Application
Pass
Rejected
ID Verification
Reject
Pass
No record
Rejected
Credit Scoring
External Source
Cons
Pass
Data Match
May incorrectly reject credit worthy applicants due to lack of data.
Credit Evaluation
Pass
Approval

5. Illustration of trends revealed by big-data analyses
Annex – examples
Illustration of trends revealed by big-data analyses

6. Goods Consumption Assessment-Local consumption
For a period of 12 months ,the more consumption on local categories (food, entertainment, sporting events, etc.), the lower the default rate
Note：Credit card sample

7. Goods Consumption Assessment-Online Gaming
For a period of 12 months , the higher amount of online gaming spending, the higher the risk of default.
来源：光大

8. Media Reading Assessment- Financial Media Visiting
For the last 12 months, the more days of visiting financial related media sites, the less the risk of default
来源：光大