Management Presentation August 2019 Disclaimer This presentation - PowerPoint PPT Presentation

Management Presentation August 2019

Disclaimer This presentation contains forward-looking statements within the meaning of Section 21E of the Securities Exchange Act of 1934, as amended, and as defined in the U.S. Private Securities Litigation Reform Act of 1995. These forward-looking statements can be identified by terminology such as “may,” “will,” “expect,” “anticipate,” “aim,” “estimate,” “intend,” “plan,” “believe,” “pot ent ial,” “continue,” “is/are likely to” or other similar expressions. Such statements are based upon management’s current expectations and current market and operating conditions, and relate to events that involve known or unknown risks, uncertainties and other factors, all of which are difficult to predict and many of which are beyond the Company’s control, which may cause the Company’s actual results, performance or achievements to differ materially from those in the forward-looking statements. Further information regarding these and other risks, uncertainties or factors is included in the Company’s filings with the U.S. Securities and Exchange Commission. The Company does not undertake any obligation to update any forward-looking statement as a result of new information, future events or otherwise, except as required under law. In addition to U.S. GAAP financials, this presentation includes adjusted net income, a Non-GAAP financial measure. This Non-GAAP financial measure is not defined under U.S. GAAP and is not presented in accordance with U.S. GAAP. The Non-GAAP measure has limitations as an analytical tool and you should not consider it in isolation or as a substitute for an analysis of the Compa ny’ s results under U.S. GAAP. There are a number of limitations related to the use of the Non-GAAP financial measure versus its nearest GAAP equivalent. First, adjusted net income is not a substitute for net income or other consolidated statements of operations data prepared in accordance with U.S. GAAP. Second, other companies may calculate such Non-GAAP financial measure differently or may use other measures to evaluate their performance, all of which could reduce the usefulness of the Non-GAAP financial measure as a tool for comparison. Finally, the Non-GAAP financial measure does not reflect the impact of share-based compensation expenses, which have been and may continue to be incurred in the Company’s business. See the Appendix for reconciliation between adjusted net income to the mos t directly comparable financial measures calculated and presented in accordance with U.S. GAAP , which is net income. 2

Focused on Serving Large Population of Underbanked Large credit transactions Mainly served by  PBOC credit record  Banks ~150mn  Traditional mortgage and auto loans  Leading technology conglomerates  APR 3.5-18% people Mid credit transactions ~230mn  Key contributors of banks’ credit card  Banks & consumer finance overdrafts interest income people  Leading technology conglomerates  APR 18-25% Micro credit transactions  Lack of traditional credit metrics ~430mn  Other online lending APR ≤ 36%  platforms people High risk loans Source: Public information and Company‘s estimation as of the end of 2018 3

Our Tech-driven Business Model Leading Innovations in Fintech Monetization of Technology Open Platform Distributed Clearing Technology Credit Solution- Full suite tech Zero credit risk as-a-Service infrastructure & low support operational cost Small Credit Facilitation Big-data Credit-tech Credit Trained and Credit facilitation tested tech underwriting App & H5 based services infrastructure services Seamless User Interface 4

Massive, Under-penetrated User Base Unlocks Huge Opportunities User base (mn) User base (mn) Industry credit cycle Aug 2017: Aug 2018: Industry credit down cycle: stabilized: Payment ecosystem Payment ecosystem Conservative risk-taking on new users More aggressive new front-page icon front-page icon access user growth strategy access initiated terminated 76.0 73.3 71.8 70.0 67.9 (1) 65.3 6.1 62.4 56.6 47.9 70 mn 39.4 Huge potential 5.4 to activate 5.3 33.0 31.4 30.5 dormant 29.1 5.2 28.3 27.5 26.2 registered 5.1 23.6 users 17.6 4.9 14.1 (1) 6.3 6.1 5.8 5.2 5.3 5.4 5.0 5.1 4.9 3.9 1Q17 2Q17 3Q17 4Q17 1Q18 2Q18 3Q18 4Q18 1Q19 2Q19 1Q18 2Q18 3Q18 4Q18 1Q19 2Q19 Outstanding Borrowers Registered Users Approved Users Outstanding Borrowers Note: (1) Include outstanding borrowers from both loan book business and transaction referral business. 5

Our Traffic Acquisition Strategy Distributed Traffic 3.0 Ecosystem Independent Traffic- 2.0 Generation App E-sports Competition Transportation Laifenqi App 1.0 Plug-in to one Gaming Delivery Leisure Payment Ecosystem Social Entertainment Travel Live- streaming Music News Offline Stores E-commerce • Create massive ecosystem with third party app partnerships • Users access easily via service window • Attracted >76mn registered users with • Initiate credit trial program targeting at on 3 rd party ecosystem word of mouth credibility 70mn dormant registered users 6

Unique Business Model Based Upon Proprietary Technology 10 TB Massive Amount of Data New data Technology Infrastructure /day 315 37K Credit & Anti-fraud Databases Assessment Model Transactions 1,591 800+ decision rules per hour 6,000+ data variables Cloud-based servers QD score Centralized Fund Matching System 100+ licensed FI integrated 195mm+ 118TB Tech-driven Actual transaction Data processed Business Transaction Clearing backed analytics /day System Model T+0 bank-level clearance and settlement Full Cloud-based Technologies External Scalable, efficient, stable 220bn+ data sources and secure Accumulated transactions 7

Tech-enabled Credit Analysis and Serving Process Artificial intelligence QD Score User Credit Assessment Deposit Cash Information Data Analysis & Collection • Complex network • Behavior patterns • Address clustering • LBS (1) information Machine learning algorithms for • Biometric recognition… collection resource allocation External Data Self-reinforcing model Note: (1) Location-based services 8

Credit Solution-as-a-Service for App Partners Real-time customer intelligence and Fully in-app user experiences analytics Better monetization of user traffic New partner applications, accounting, Increase user engagement and ARPU etc. supporting functions Dedicated App Developed for App Partners Enhanced User Experience Loan Application 9

1. Overview of Small Credit Facilitation

2Q19 Segment Highlights 76.0mn registered users RMB19.7bn total amount of transactions 33.0mn users with approved credit RMB1,158.6mn Non-GAAP net income As of June 30, 2019 During 2Q19 RMB28.7bn outstanding loans (2) 6.0mn outstanding borrowers (1) As of June 30, 2019 As of June 30, 2019 <4.3% M1+ delinquency rate (3) 8.4 Months loan tenure <2.6% M6+ charge-off rate (4) RMB1,931 average ticket size For loans generated in 2017 through June 30, During 2Q19 2019 Massive amount Serve the “ credit Superior efficiency Consumption of high frequency underserved ” enabled by technology scenarios data Notes: (1) Borrowers who have loans outstanding as of June 30, 2019, including outstanding borrowers from loan book business only. (2) Includes off and on balance sheet loans directly or indirectly funded by our institutional funding partners or our own capital, net of cumulative write-offs and it does not include auto loans from Dabai Auto business and loans from transaction referral business. (3) M1+ delinquency rate by vintage is defined as the total balance of outstanding principal of a vintage for which any installment payment is over 30 calendar days past due as of a particular date 11 (adjusted to reflect total amount of recovered past due payments for principal, before charge-offs), divided by the total initial principal in such vintage. (4) M6+ charge-off rate is defined as the total off + on outstanding principal balance of the loans that are charged off during a specified period, divided by the total initial principal of the loans originated in such vintage.

Optimized Risk Model to Quickly React to Credit Cycle and De-risk Our Balance Monetization via increase in loan balance New regulation issued caused an + User activation via credit trial programs industry wide credit crunch and downcycle 40 12 35 10 Daily Outstanding Loan Balance (RMB bn) 30 Overall D1 8 delinquency rate 25 D1 Delinquency Rate (%) 20 6 15 4 10 2 5 D1 delinquency rate for new transactions after rule implementation 0 0 2017/10/27 2018/2/7 2018/5/21 2018/9/1 2018/12/13 2019/3/26 2019/7/7 (1) Daily Outstanding Loan Balance 12 Note: (1) Doesn’t take into account for accumulative charge -offs.