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

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

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

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

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

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

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

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