360數科 (QFIN) 2023 Q1 法說會逐字稿

Ladies and gentlemen, thank you for standing by, and welcome to Qifu Technology's First Quarter 2023 Earnings Conference Call. (Operator Instructions) Please also note, today's event is being recorded.

At this time, I would like to turn the conference call over to Ms. Karen Ji, Senior Director of Capital Markets. Please go ahead, Karen.

Thank you, operator. Hello, everyone, and welcome to Qifu Technology's First Quarter 2023 Earnings Conference Call. Our earnings release was distributed earlier today and is available on our IR website. Joining me today are 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 mentioned in this call are in RMB terms. Now I will turn the call over to our CEO, Mr. Wu Haisheng. Please go ahead.

[Interpreted]

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 number of users with approved credit lines increased 15.6% year-over-year. The growth was better than our initial expectation against the backdrop of a moderate 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 continuous improvements 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 and 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 collection 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 remained ample during the quarter, with total social financing and the M2 money supply increasing by 10% and 12.7% year-over-year, respectively. This enables us to further diversify our funding sources and reduce our funding costs by 30 basis points sequentially. In addition, with our solid risk performance in the loan facilitation model, we have obtained more allocation from our financial institution partners for ABS issuance, thereby accelerating our pace of issuance. In Q1, we issued RMB 2.3 billion of ABSs, up 77% year-over-year, far outpacing the growth of overall consumer loan ABS issuances in China. Our funding costs associated with ABS issuance also decreased by 17 basis points sequentially, contributing to a further reduction in overall funding costs. Going forward, we will continue to deepen our partnerships with financial institutions and strengthen our competitive edge in terms of funding costs. We expect overall funding costs to remain generally stable over the next two quarters.

In terms of customer acquisition in Q1, we entered into a cooperation agreement with Bilibili during the quarter, becoming one of its first batch of fintech marketing partners. We also launched precision marketing with the RTA model in the app stores of major mobile phone manufacturers, including Xiaomi, OPPO, VIVO, and Huawei. The upgraded marketing model will help us improve our marketing efficiency on the relevant app store platforms. For example, our customer acquisition cost per credit line user under the precision marketing model on one of the app stores was approximately 20% lower than it had been under the traditional model. Within our embedded finance business, we connected with additional traffic platforms, which resulted in a 14.5% sequential increase in the number of new users receiving credit lines. In addition, we have been exploring innovative ways to acquire new customers. The official live-streaming account for our 360 Jietiao product was launched on a short-form video platform in April. The marketing effect is better than our expectations.

We further expanded our outreach to existing users and optimized operational strategies to boost user conversion and retention. In Q1, we introduced the enterprise WeChat as a new channel for engaging with our existing users, which drives user conversion by offering a step-by-step guide through the loan application, drawdown and the submission process. As of today, we have engaged with more than 200,000 existing users. Furthermore, we also improved the user retention by optimizing our product offerings and user experience based on extensive surveys of our existing users.

As the macro economy gradually recovered, we have noticed that demand has been rebounding fast among broadly defined SME borrowers. To cater to this demand, we strategically strengthened our ability to identify and manage the specific segment. We enhanced our customer profiling capabilities by improving data dimensions such as credit history, invoice, e-commerce transactions, billings, industry data, which allow us to accurately identify customers within the broadly defined SME segment, which accounts for more than 40% of our current user base. Our next step is to conduct pilot tests on a selected group of users, aimed at developing differentiated products and fine-tuning risk management strategies for the broadly defined SME segment, which we believe has the potential to drive meaningful growth for our business in the future.

For our technology solution business, our focus in Q1 was on enhancing our product capabilities. Specifically, we extended the scope of our solutions beyond personal consumer loans to include individual business loans. Our diversified deployment methods allow us to better serve the needs of each financial institution we work with. So far, we have developed standardized products based on our core technological capabilities and created 18 standardized modules covering every aspect of the credit business, including customer acquisition, risk management, operations and accounting, et cetera. We also incorporated our extensive credit industry know-how into the modules, which empowers financial institutions with industry-leading expertise and algorithms from the moment they onboard.

Over the past few months, ChatGPT has been having a significant impact on various industries since its release. We believe that generative AI technology has many natural applications in the financial sector. For example, in areas such as intelligent customer service, telemarketing and loan collection, ChatGPT can better understand user emotions and facilitate natural, smooth and personalized interaction with users. In risk management, it can derive useful information from credit reports and identify relevant factors for our risk management models. In April, we established a Large Language Model Department. This new strategic division is dedicated to developing various deep learning algorithms and generative AI technologies, specifically for applications in the financial sector. We have already launched the first version of GPT for internal use, which is designed to perform semantic analysis in our loan collection and telemarketing processes. Through GPT analysis of user intention and labeling, we can see that users of different labels have clear variations in the effectiveness of collection and telemarketing. Apart from applying AIGC to enhance the user experience and our operational efficiency, we also plan to gradually export such capabilities to our financial partners.

On the regulatory front, we continued to make steady progress in gaining compliant with "Duan Zhi Lian" credit agency reform in Q1. We have substantially completed the required integration of systems with our financial partners according to the plan we submitted to the regulator. So far, our loan facilitation progress through the "Duan Zhi Lian" model has been very smooth. Given the current regulatory focus on promoting economic development, we believe the industry will be able to deliver healthy growth in a stable regulatory environment.

Looking ahead, while the economic recovery is still in its early stage, but the trend for recovery is clear. We believe that the macroeconomic environment will gradually improve throughout the remainder of the year. We are also confident in our ability to capitalize on the recovery momentum and delivery on our goals effectively.

Finally, I have some news to share with you all. Our Board of Directors has just passed a resolution to increase our dividend payout ratio. As we continue to drive quality growth and create shareholder value, we believe it is also important to listen to the market and share the benefits of growth with our shareholders. We believe this will enable us to enhance the value of our company. Alex will share more about this later.

With that, I will now turn the call over to our CFO, Alex, who will walk you through our financial results for this quarter.

Thank you, Haisheng. Good morning and good evening. Welcome to our first quarter earnings call. First quarter set a positive tone for a recovery year. We are pleased to see 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 extra growth while maintaining desirable asset quality.

In Q1, we continued to targets higher quality and lower risk user base and drive further improvement in risk performance. Key leading indicator Day-1 delinquency has been on a steady declining trend in recent quarters. It 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 reflected 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 reflected back to normal collection operations. As economic recovery continues, we see further improvement in these metrics. By late April, 30-day collection rate was 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 credit driven services (capital-heavy) was CNY 2.6 billion in Q1, compared to CNY 2.8 billion in Q4 and CNY 2.9 billion a year ago. The year-on-year and sequential decline was mainly due to overall decline in expected average tenor of the new loans as well as further adjustments to existing loans' expected tenor. During the quarter, we see increased early repayment activities from borrowers likely driven by a few factors, including: seasonal spike prior to Chinese New Year, spill-over 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 the above-mentioned factors gradually easing. On balance sheet loans continued to grow at a faster pace and accounted for nearly 20% of the total loan volume, as we continued to drive for better utilization of our capital as well as our micro lending license.

Revenue from platform service (capital light) was CNY 969 million in Q1 compared to CNY 1.1 billion in Q4 and CNY 1.4 billion a year ago. The year-on-year and sequential decline was also mainly due to overall decline in expected average tenor of new loans as well as further adjustment to existing loans' expected tenor. For Q1, capital-light loan facilitation, ICE and other technology solutions combined accounted for roughly 56% of the total loan volume, roughly flat versus the prior quarter. We expect the risk ratio to be relatively stable throughout this year. In the long run, we'll continue to pursue tech-driven business model while seeking a balance among various forms of non risk-bearing solutions based on macro environment and operational conditions.

During the quarter, average IRR prices of loans we originated and/or facilitated remained stable Q-on-Q, well within the regulatory rate cap requirement. Looking forward, we expect pricing to be relatively stable for the coming quarters.

Sales and marketing expenses increased marginally Q-on-Q as recovery in our user activity was offset by seasonal impact of Chinese New Year. We added approximately 1.5 million new credit line users in Q1, flat versus Q4. Unit cost to acquire a new credit line user also increased marginally. While we will continue to drive for efficiency in our operations, we may adjust the pace of new user acquisition as economic recovery continues throughout 2023. Meanwhile, we will continue to focus on reenergizing existing user base as repeat borrowers historically contributed a vast majority of our growth.

Although we will continue to take a prudent approach to book provisions against potential credit losses, we should expect increasing write-backs from provisions from prior periods as overall risk profile of our loan portfolio gradually improves along with macro conditions. Total new provisions for risk-bearing loans in Q1 were approximately CNY 1.7 billion, and the write-backs of the previous provisions were approximately CNY 411 million. Provision coverage ratio, which is defined as total outstanding provisions provided by total outstanding delinquent loan balance between 90 and 180 days, were 432% in Q1 compared to 456% in Q4.

With solid operating results and stable contribution from capital-light model, our leverage ratio, which is defined as risk-bearing loan balance divided by shareholders' equity was at a historical low of 3.4x in Q1 compared to 4.2x a year ago. We expect to see rather stable leverage ratios for the time being until loan risk-bearing contribution resume growth in the future.

We generate approximately CNY 1.8 billion cash from operations in Q1, roughly flat Q-on-Q. Total cash and cash equivalents was CNY 9 billion in Q1 compared to CNY 10.9 billion in Q4. Nonrestricted cash was approximately CNY 5.1 billion in Q1 compared to CNY 7.2 billion in Q4. The sequential decline in cash position was mainly due to increased cash usage in our balance sheet lending. As we discussed earlier, with economic conditions improving, we may look for opportunities to deploy resources to launch new initiatives and develop new technologies and expand our service offerings.

Non-GAAP net profit was CNY 976 million in Q1 compared to CNY 919 million in Q4.

As we continue to generate healthy cash flow from operations, we believe our current cash position is sufficient to support our business development and to return to our shareholders. Since Q3 of 2021, we have paid out a total of CNY 1.34 billion cash dividend to our shareholders in 6 consecutive quarters. To generate high returns to our investors and solidify and expand our long-term investor base, the company's Board of Directors approved a new dividend plan yesterday. The new plan increases our dividend payout ratio to 20% to 30% from previous 15% to 20% of net profit. Also, to reduce the transaction cost for our shareholders, the new plan approved a semiannual dividend distribution schedule to replace the quarterly dividend schedule of the old plan. The first semiannual dividend payout will be declared in our Q2 earnings release.

Finally, regarding our outlook for 2023.

While we start to see a gradual recovery of macro economy and our business activities are also trending a bit better than previously thought, it may still take extra time for consumers' confidence and behavior to return to normal. At this junction, we still see a modest recovery in consumer credit demand with growth rate potentially accelerating throughout the year. As such, we would like to maintain our full year total loan volume target for 2023 at between RMB 455 billion and RMB 495 billion, representing year-on-year growth of 10% to 20%. As always, this forecast reflects the company's current and preliminary view, which is subject to material change.

With that, I would like to conclude our prepared remarks. Operator, we can now take some questions.

(Operator Instructions) Our first question comes from the line of Frank Zheng from Credit Suisse.

(foreign language)

This is Frank from Credit Suisse. I have 2 questions. The first 1 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?

[Interpreted]

Thanks, Frank. And I will answer your first question, and I will pass the second question to Alex. First, for this year, we will say the new customers and existing customers are equally important to us. 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 RTA model into our app store marketing. The result is better than our initial expectation.

And the second part, we used the RTA 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 acquisition part.

Regarding our existing users, actually, we have a very significant base of existing users. So it's very important 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 profiles of our users. For example, we enhanced our customer profiling capabilities by improving data dimensions and increased our percentage of accurately identifiable customers within the broadly defined SME segment, which accounts for more than 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, with entered a moderate macro economy recovery environment, we believe it's very important for us to increase our efficiency at this stage. We will increase our coverage in terms of the channel and partnership, so we can better improve our marketing efficiency. So currently, we are actually enjoying our leading position in this part and we believe we will further increase our user base when the macro further improves.

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 use to get credit scores cost and the one when we do the transaction or sending the message through the SMS. For 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 user 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 in the following quarters with the kind of increased pace of customer acquisition, you may see some increase in the marketing spending. But overall, on a full year basis, we will see a lower unit cost per credit line user acquisition cost. Other back-office-related fixed cost, we have a pretty tight internal control, including headcount and also the IT spending there.

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

(foreign language)

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?

[Interpreted]

Thanks, Alex. From the competition perspective, we think that for our industry, the recovery of the credit demand is more important than the competition. The impact from the recovery of user demand is more important than 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 theoretically, as long as our model is accurate enough, we can have very strong competitiveness.

And on pricing perspective, actually, we have been trying 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 add incremental value from this sort of strategy. So from the overall pricing perspective, we think the future pricing will maintain at a stable level compared to the current level.

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

(foreign language)

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.

[Interpreted] 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, Jiangsu, Shandong, Shanghai, Hebei, 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.

[Portions of this transcript that are marked [Interpreted] were spoken by an interpreter present on the live call.]