Bingjian Group Completes Pre-B Round to Keep Exploring AI Applications Across Financial Scenarios | FreeS Fund Deal News
As the online lending industry matures, risk control capabilities have become the key to profitability.
On April 8, 2018, IceKredit announced the completion of a Pre-B funding round exceeding 100 million yuan. IceKredit is a credit assessment service provider built on big data technology, which started by targeting small and micro enterprise credit evaluation and now offers products covering both personal and corporate credit assessment.
When IceKredit was founded in 2015, it secured an angel round led by FreeS Fund. Over the past two-plus years, FreeS Fund watched IceKredit gain repeated recognition from investors and win notable clients including UnionPay, Taizhou Bank, Bank of Nanjing, Suning Bank, MaShang Consumer Finance, BOC Consumer Finance, NetEase Microloans, Fullerton Microloans, ZhongAn Insurance, and Wacai.
From the Investor
Zhao Zhiyuan, Vice President, FreeS Fund
Email: zzy@freesvc.com
We remain bullish on the IceKredit team's extensive experience in finance, data, artificial intelligence, and risk control, as well as their deep understanding of the industry. In a rapidly shifting financial market over the past year, the IceKredit team seized opportunities and earned high recognition from partners including banks, non-bank financial institutions, and internet finance companies. We believe that in the vast market for independent credit reporting, IceKredit can continue to deliver superior service to its clients.
(From 36Kr)
On April 8, AI-powered big data risk control platform IceKredit announced the completion of a Pre-B funding round exceeding 100 million yuan. Prior to this, IceKredit had secured a Series A led by CCV, and an angel round led by FreeS Fund with participation from Yunqi Capital and Weilie Capital.
IceKredit is a credit assessment service provider built on big data technology, which started by targeting small and micro enterprise credit evaluation and now offers products covering both personal and corporate credit assessment. IceKredit founder Lingyun Gu said the new funding would primarily go toward talent acquisition, market expansion, R&D investment, and strategic mergers and acquisitions across the industry value chain. He added that IceKredit, which built its reputation on corporate credit reporting, would continue refining its corporate credit products following this round.
As Online Lending Matures, Risk Control Becomes the Key to Profitability
Late last year, new regulations on cash loans were introduced; this year, online lending platforms face a mid-year registration deadline. With regulatory documents coming out frequently, compliance requirements for financial and related institutions have grown stricter. Previously, when regulations were absent, many cash loan platforms operated with virtually no risk control — they simply covered bad debts with high interest rates.
But once strict caps on interest rates were imposed, platforms had to rigorously screen users and price risk precisely. This likely means that the risk control work once ignored by certain cash loan platforms will become a central focus of the financial industry going forward.
Lingyun Gu divides the development of China's online lending market and its risk control into three stages:
The first stage, before 2014, saw mobile internet mature and mobile-based online lending emerge. To meet borrowing demand, lending institutions of all kinds proliferated, and banks began paying more attention to consumer loans. At this point regulation had not caught up, competition within the industry was insufficient, and lending demand was not fully met.
The second stage, from 2014 to 2017, saw regulators gradually strengthen oversight with requirements such as fund custody. Land-grabbing and wild growth were no longer viable, and players turned to organic growth and the hunt for unique data sources. During this period, privacy violations ran rampant, as firms sought to crush competitors through proprietary data advantages. This phase ended in June 2017 with the formal implementation of the Personal Information Protection Law.
The third stage, after 2017, saw data disparities within the industry gradually flatten. Eventually, as data trading became marketized and standardized, "either everyone has a certain data point, or no one does — so you can only compete on technology." Gu believes that after industry consolidation and regulatory standardization, the sector has now entered deep waters. With data sources and prices converging, algorithmic and modeling capabilities have become the key differentiators.
Moreover, Gu views the new cash loan regulations as broadly positive for the big data risk control industry — and especially positive for teams that prioritize risk control and compliance.
Gu notes that IceKredit has adhered to independent third-party principles from day one, neither selling data nor issuing loans. This strategy put them at a slight disadvantage during the early land-grab phase, but as the industry matured and regulations tightened, the group's technical strengths have become apparent. He said IceKredit's business volume began rising rapidly starting in June 2017.
Similarly, in the lending industry, companies that have quietly focused on consumer finance are now beginning to show their strength. With high-interest cash loans banned and rate caps in place, platforms can no longer simply raise interest rates to compensate — they must rely on more precise pricing. Thus the cash loan regulations have sparked a second wave of demand for technology.
Contextualizing Technology and Models: Where Risk Control Differentiation Shows
Against this backdrop, where will risk control differentiation manifest? Gu believes it lies in understanding algorithms themselves, engineering those algorithms, and combining that engineering with specific business scenarios.
He sees cash loans trending toward online, large-ticket, low-interest, long-term products — the only way to ensure profitability through business logic. Consumer finance, meanwhile, will enter a stage similar to the US market, with business logic becoming contextualized and verticalized. Therefore, risk control must go deeper in combination with specific business scenarios and population segments, forming specialized moats.
Currently, IceKredit has developed two product lines: standardized products and customized products.
Standardized products include Huomou, Huimou, Tanzhen, and Fuzheng — consumer loan solutions delivered via API. Clients provide user triangulated data (name, mobile number, ID number), and IceKredit returns an assessment through AI algorithms. These are currently applied in precision marketing and anti-fraud.
Customized products involve systematic output for banks and other financial institutions. IceKredit collaborates with these institutions to jointly define risk control dimensions, build models, and construct IT systems tailored to each institution's specific requirements.
Gu revealed that IceKredit currently has 200–300 paying clients, all through model-based partnerships. Paying clients include UnionPay, Taizhou Bank, Bank of Nanjing, Suning Bank, MaShang Consumer Finance, BOC Consumer Finance, NetEase Microloans, Fullerton Microloans, ZhongAn Insurance, and Wacai.
Earlier this year, IceKredit won a competitive bid for a project with Chengdu Rural Commercial Bank, which ranks among the top four rural commercial banks nationally by asset size.
On specific proprietary technical advantages, Gu highlighted several capabilities in IceKredit's business scenarios.
First is its public sentiment system. After crawling social media, online news, and web keywords, it uses AI algorithms to aggregate and judge sentiment, generating an IceKredit sentiment score for real-time public opinion monitoring of small and micro enterprises.
Next is its super report repair function. Gu explained that in risk control there's a saying: "garbage in, garbage out" — if raw data is fake or low-quality, the output is meaningless. IceKredit's system, combined with IPC techniques, filters data quality through on-site interviews, cross-validation, and other methods to ensure usability.
IceKredit also applies knowledge graphs and graph database technology to enterprise borrowing and guarantee relationship networks, uncovering hidden complex webs of connections between companies to enable early risk warnings.
(Adapted from 36Kr. Feel free to share to your Moments.)
The Man-Machine Battle Is Over; AI Investing Is Just Beginning
Li Feng: We've Finally Made It to Value Returning — The Era Where Good People Make Money Is Here
