Ladies and gentlemen, thank you for standing by, and welcome to Qifu Technology's First Quarter 2023 Earnings Conference Call. [Operator Instructions]. At this time, I would like to turn the conference call over to , Senior Director of Capital Markets. Please go ahead, Karen.

Thank you, operator. Hello, everyone, and welcome to Qifu Technologies' First Quarter 2023 Earnings Conference Call. Our earnings release was distributed. Joining me today is Mr. Wu Haisheng, our CEO; Mr. Alex Xu, our CFO; and Mr. Zheng Yan, our CRO. Before we start, I would like to refer you to our safe harbor statements in the earnings press release, which applies to this call as we will make certain forward-looking statements. Also, this call includes discussions on certain non-GAAP financial measures. Please refer to our earnings release, which contains a reconciliation of the non-GAAP measures to GAAP measures. Also, please note that unless otherwise stated, all figures [Technical Difficulty] to our CEO, Mr. Wu Haisheng. Please go ahead.

By the end of Q1, our platform cumulatively connected approximately 46 million users with approved credit lines and a total of 150 financial institutions. Total loan facilitation and origination volume on our platform reached RMB 109.5 billion, up by 10.7% year-over-year and 4.7% quarter-over-quarter. Cumulative users with approved credit lines increased 15.6% year-over-year. The growth was better than our initial expectation against the back [Technical Difficulty] of the rate recovery in China's macro economy. Since the beginning of this year, we have seen positive overall trends in our business, including credit demand from users, steadily increasing and a continuous improvement across multiple risk indicators. Pricing remained stable during the quarter with our user base having been substantially optimized. At the same time, we continue to upgrade our risk management models, including the release of more than 1,000 version updates, an iteration of over 30,000 strategic rules to our credit assessment models over the past 12 months. We also further upgraded our PBOC credit data assessment models. Since Q3 last year, we have started to incorporate PBOC credit data into our ongoing post credit assessment process on a large scale. With over 20,000 derived data dimensions, we are able to unlock the value of our existing users with enhanced credit assessment capabilities. Supported by our constantly improving ability to accurately identify risks, our day 1 delinquency rate in April 2023, decreased by 37 basis points from December 2022, while our M1 production rate increased by 189 basis points for the same period. With our risk performance having substantially reached our target, we are more confident in gradually ramping up investments in customer acquisition in the near term. Liquidity in the financial system, we've made ample during the quarter with total social financing and M2 money supply increasing by 10% and 12.7%…

Thank you, Haisheng. Good morning and good evening. Welcome to our first quarter earnings call. First quarter set a positive recovery year [Technical Difficulty] the improvement in many aspects of our operations. User activity levels continued to improve in recent months, aside from normal seasonality. Although we still want to call the recovery a modest one, things are indeed trending a little bit better than we initially saw. As Haisheng discussed earlier, with macro conditions improving throughout 2023, we intend to focus on our efforts and deploy our resources to drive growth while maintaining desirable asset quality. In Q1, targets high-quality and low-risk user base and drive further improvement in risk performance. Key leading indicators in day 1 delinquency has been on a steady declining trend in recent quarters, was 4.1% in Q1 versus 4.3% in Q4 and further declined to approximately 4% in April. The continued improvement in day 1 delinquency mainly reflect the macro improvements as well as further optimization of our algorithm. 30-day collection rate was 86.2% in Q1 versus 84.7% in Q4. This sharp rebound from the COVID disrupted Q4 mainly reflect back to normal collection operations. As economic recovery continues, we see further improvement in these metrics. By late April, 30-day collection rate already at near 87%. Total net revenue for Q1 was CNY 3.6 billion versus CNY 3.9 billion in Q4 and CNY 4.3 billion a year ago. Revenue from [Technical Difficulty] as well as further adjustments [Technical Difficulty]. During the quarter, we see increase [Technical Difficulty] Chinese New Year, still already effect from mortgage early repayment momentum and oversupply of liquidity at the beginning of the year. Looking ahead, we expect early repayment level to stabilize in Q2 as above-mentioned matters or factors gradually easing. On balance sheet loans continue to grow at…

[Operator Instructions]. Our first question comes from the line of Frank Zheng from Credit Suisse.

This is Frank from Credit Suisse. I have two questions. The first one is on the strategic focus for the rest of the year. Could the management provide more color on measures, for instance, more aggressive client acquisition, optimization of existing clients and potentially further upgrading client segments? And second question is on operating expenses. What are some of the measures the company is taking to be operationally more efficient?

Thanks, Frank. And I will answer your first question, and I will pass the second question to Alex. First, about the -- sorry. For this year, we will say the new customer and existing customers are equally important trends. So in terms of the new customer acquisition, our efforts will be focused on 2 aspects. First, we will continue to expand our partnership with different channels and increase the depth of the partnership. For example, we have been trying the live streaming on some short-form video platforms, and we innovatively utilize the IT model into our App Store marketing. The result is better than our initial expectation. And the second part, we used the ICE model to increase our recognition about the users' willingness to borrow. So we can optimize our offer and increase our users' conversion ratio. Therefore, we can increase the LTV of our users. So we will increase our competitiveness in terms of customer acquisition. This is about the new customer -- new customer acquisition part. Regarding our existing users, actually, we have a very significant user base of our existing users. So it's very important for our -- for us to increase the efforts on existing users to improve the conversion effectiveness. For example, we have tried to use enterprise WeChat to cover our existing users to increase effectiveness for us to outreach our existing users. And we refine our risk management models to better understand our users and identify different profile of our users. For example, [Technical Difficulty] 40%. So later, we can optimize our offer and improve our user engagement process to better engage the existing users to improve their long-term value. So at this point moderate macro economy recovery environment, we believe it's very important for us to increase our efficiency at this stage. So we will increase our coverage in terms of the channel and partnership, so we can better improve our marketing efficiency. So testing, we are -- actually enjoyed the [Technical Difficulty] and we believe we will further increase our user base when to -- and macro further [Technical Difficulty].

So basically, for the operating expenses trend going forward, there are a few aspects. One, some of the operating expenses are variable costs, for example, the one we used to get credit scores cost and the -- when we do the transaction or sending the message or the SMS, those variable costs -- we have a long-term relationship with those suppliers. And every year you can always squeeze a little bit from the cost base on a unit basis, but the room for that is not really that much. And then the other big part of the variable cost is really customer acquisition. I think we had this kind of discussion earlier or before. Last year, our unit or per credit line users customer acquisition cost was about RMB 370 or RMB 360 per user. This year we intend to lower that unit cost number to somewhere around RMB 330. The first quarter was only about RMB 280, less than RMB 290. So the following quarters with the kind of increased pace of customer acquisition, you may see some increase in the -- towards marketing spending. But overall, on a full year basis, we will see a lower unit cost per credit line users acquisition cost. Other back-office-related fixed cost, we have a pretty tight internal control, including headcount and also the IT spendings there.

Our next question comes from the line of Alex Ye from UBS.

So my question is mainly on the loan pricing outlook. So firstly, in terms of the competitive landscape, we have seen the overall consumption recovery and consumer credit data has been quite modest in April. So could you share some color in terms of what's the current trend you have seen regarding the capacity pressure from different players. And secondly, would you consider lowering your loan pricing in order to stimulate some loan drawdown demand from your existing customer base.

Thanks, Alex. From the competition perspective, we think that for our industry, the recovery of the credit demand is more important for the competition. The impact from the recovery of user demand is more important from the impact of the competition. On the other hand, we believe the segmentation of this industry is very clear at this stage. We are actually quite differentiated from the large banks and also the smaller players. We're targeting different target customers and the pricing segments. So there is limited overlapping among the competitors. And on the other hand, for our service capability, we think we can have a wide coverage of different kind of users. We can cover the lower pricing users and also the higher pricing users. So we can further refine our risk management models to provide differentiated products and offerings to better serve our users. So periodically, as long as our model is accurate enough, we can have very strong competitiveness. And on pricing perspective, actually, we can try some reduced pricing to better activate and engage some of our users as long as our model is accurate, we can better serve them and increase -- as increased value from this sort of strategy. So from the overall pricing perspective, we think the future pricing will maintain at current level -- will maintain a stable level compared to the current level.

Our next question comes from the line of Richard Xu from Morgan Stanley.

So essentially my question is on the loan demand, particularly on the sequential change from the potential borrowers in recent months and any divergent trends among different region groups borrowers in terms of loan demand, income growth and credit quality.

In terms of credit demand recovery, we have seen 2 trends: one is moderate recovery, the other is divergent recovery. So in terms of the diversified recovery, we would look at this problem in several aspects. From a region perspective, , Shanghai, those regions are recovering faster than other regions, which is in line with the incremental social financing by region published by the government in Q1. From a customer segment perspective, we have seen higher-quality users recovering relatively faster with increasing credit size. And on the other hand, we have seen the broadly defined SME group recovering relatively faster, especially from the service industries.

We have reached the end of the question-and-answer session. Thank you very much for all your questions. I'll now turn the conference back to the management team for closing remarks.

Okay. Thank you. Thanks to everyone for joining us. If you have additional questions, we can discuss offline. Thank you. Have a good day. Bye, bye.

Thank you. This concludes today's conference call. Thank you for participating. You may now disconnect.