Qfin (QFIN) Q2 2026 earnings summary
Event summary combining transcript, slides, and related documents.
Q2 2026 earnings summary
28 Aug, 2026Executive summary
The consumer finance industry in China faced significant pressure in Q2 2026, with a sharp decline in household consumer loans, tightening regulatory oversight, and a late-June liquidity crisis that triggered funding contraction and risk aversion among financial institutions.
The company prioritized compliance, risk management, and operational efficiency, optimizing user and business mix to enhance resilience, and maintained a leading AI-empowered credit-tech platform serving 168 financial institutions and 65.6 million users as of June 30, 2026.
Achieved 301.8 million cumulative consumer credit needs and 39.9 million cumulative borrowers with successful drawdown, up 8.5% year-over-year.
Appointed Prof. Dong Lou as independent director and declared a semi-annual dividend of $0.46 per ADS for H1 2026.
Financial highlights
Q2 2026 net revenue was RMB3,567 million, down from RMB3,909.3 million in Q1 and RMB5,216 million year-over-year.
Non-GAAP net income was RMB454.9 million, down from RMB945.9 million in Q1 and RMB1,849 million a year ago; net income was RMB401.4 million.
Total loan facilitation and origination volume reached RMB63.4 billion, down 2.5% sequentially and from RMB84.6 billion year-over-year.
Cash and short-term investments totaled approximately RMB10.6 billion at quarter end.
Outstanding loan balance at quarter-end was RMB107.6 billion.
Outlook and guidance
Q3 2026 non-GAAP net income is expected between RMB400 million and RMB500 million, a year-on-year decline of 67%-73%.
Management will maintain a cautious approach, focusing on risk control, efficiency, and capital preservation amid ongoing market volatility and regulatory changes.
Loan volume is expected to decline meaningfully in Q3 due to tightened risk controls and funding constraints.
Continued focus on expanding user base and deepening partnerships with financial institutions, emphasizing AI-driven credit assessment and risk management.
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