4Paradigm lowering the bar for AI products to push scale adoption
The moat of Netflix, ByteDance and other tech giants comes from their AI-powered recommendation systems which are difficult to replicate. 4Paradigm claimed it enabled 70% of its employees to create such systems.
Machine learning and AI algorithms have brought significant business values to Netflix and Spotify in the United States as well as to Baidu and ByteDance in China. The moat of those tech giants comes from their AI-powered personalization and recommendation systems that are difficult for their competitors to replicate so far. Founded in 2015 by the principal IT architect who created Baidu’s deep-learning search ad platform, 4Paradigm convinced that if such AI technologies are only mastered by a few scientists and machine learning experts, it cannot be promoted to go mainstream and will fail to unleash its full potential.
Hence, the company strived to lower the bar for creation of AI applications, transforming AI from expert-only to everywhere. It introduced an end-to-end machine learning PaaS platform, “Prophet 3.0”, to enable companies to independently and quickly develop their AI applications. Before the platform was released, the founder and the CEO of 4Paradigm, DAI Wenyuan (戴文渊), let his non-IT employees use “Prophet 3.0”, and after a month of training, more than 70% of employees from the marketing team, human resources and other functions obtained a modeling score of 0.8/1.0 in the AUC test, a strong result comparable to experienced data scientists. In other words, workers with non-technical backgrounds were able to leverage such a low threshold application to build a newsfeed recommendation system similar to ByteDance’s, or design AI applications that are tailored to their work.
Even though making everyone an AI expert is a distant goal in the short term, the interpretable algorithms are much easier to sell to client companies who are skeptical of machine learning or AI and will become 4Paradigm’s competitive advantage, from our perspective, as the company continues to roll out new versions of Prophet.
Leave the job of developing industry-specific products to clients
According to Deloitte Insights, two major avenues for companies to access the AI technology are1) enterprise software with AI embedded; 2) cloud-based development platforms. While Sensor Data (神策数据) and Mininglamp (明略数据) focus on delivering the first type of applications, 4Paradigm sees the market not as large as the one that constructs AI infrastructure for clients. At the end of the day, companies hope to gain a competitive advantage from AI will need to develop their own solutions. Indeed, the global AI infrastructure market is huge: spendings on AI and machine learning platforms are estimated to grow from USD 12 billion in 2017 to USD 58 billion by 2021 according to IDC.
DAI pointed out that his company used to be asked to build AI applications which did not appear cost-effective to him: while it would take considerable amount of time to collect data sets and gain related-industry experience, such software may not apply to other use cases due to the limited customization. At the end of the day, companies hope to gain a competitive advantage from AI will need to develop their own solutions. From a vendor’s point of view, since different companies have different operation procedures that they want to optimize, the AI-powered SaaS solutions developed by one service provider may only serve a niche market rather than cover bigger needs out there. Although some emerging AI vendors have brought standardized products particularly to the public security industry in China, the third-party AI-infused SaaS solutions add little value to other sectors thus far. This is the primary factor that 4Paradigm chose to take a different route from other well-known AI startups and to establish the AI PaaS platforms. The platforms do not focus on industry-specific solutions, but the solutions developed by clients based on 4Paradigm’s platforms will be customized to the industries they belong to.
Strategic investments with major Chinese state-owned banks
The adoption of AI in financial services is still in its infancy; per our Research Intelligence team, China’s fintech markets will be valued at RMB 24.5 billion in 2020, 31% of which will come from AI-driven risk management applications. Citigroup estimates that expenses incurred at the risk management and compliance divisions account for 10% of the operating costs or more at large-sized banks. Those divisions are expecting to integrate AI into the overall risk management framework and their current workflows as soon as possible, since reducing costs is no longer about hiring cheap labor but automating the processes.
In 2018, 90% of 4Paradigm’s revenue came from its financial clients. Putting the financial sector as its primary focus, 4Paradigm has announced strategic investments with five major Chinese state-owned banks, Industrial and Commercial Bank of China (ICBC), Bank of China (BOC), China Construction Bank (CCB), Agricultural Bank of China (ABC) and Bank of Communications, giving it a valuation of over USD 1 billion. It is a milestone for the company because banks at such scales are generally reluctant to delegate their core businesses to a startup. 4Paradigm has been helping clients build customized AI applications for their businesses, including ICBC’s anti-fraud applications, China Merchants Bank’s anti-laundering money applications, Shanghai Bank’s targeted marketing applications, China Everbright Bank’s bill collection reminding systems and so on. We note that involving clients in the development phase will help close the potential trust gaps and skill gaps.
From one of 4Paradigm’s clients in the financial service industry, we learned that 4Paradigm’s AI platforms have saved their significant amount of costs and time on creating internal risk-control models. For example, the company used to have around 1,000 rules to identify fraudulent activities; the number increased to 2.5 billion and the accuracy of prediction rate saw a sharply rise with the help of 4Paradigm’s automated machine learning technologies. While building, evaluating and revising models used to be labor-intensive for the client, automated machine learning helped it identify the most robust model and let the company “test and learn” iteratively over time. As a result, it took 2 hours for its employees to build the risk analytics models compared to 2 months previously, at an accuracy rate of 3% higher than before.
Besides servicing state-owned banks, other use cases in the financial services industry include insurance fraud detection. 4Paradigm is serving PICC, China’s largest property insurer, to identify fraudulent claims.
Expanding the business map to healthcare
Despite the fact that client companies outside the financial service industry are contributing lower than 10% of revenue to the company, 4Paradigm strives to lead major breakthroughs in healthcare, education and technology. Particularly, with an aging population and understaffed hospitals, the healthcare industry will greatly benefit from AI in areas including pre-emptive warning systems, early disease detection, personalized treatment, surgical simulation, and so on.
In this sector, 4Paradigm has made strategic cooperation with Shanghai Ruijin Hospital, a leading hospital with the highest rating in Chinese medical system, and together both of them have been working on the auxiliary treatment and health management. For example, “Runing Knows Sugar”, the machine learning system built by 4Paragidm, collected the hospital’s data from 2010 to 2013, including gender, height, weight, blood sugar levels, drinking and smoking history in order to predict whether patients would develop diabetes within the next three years. It also gave risk forecasts for the next 9 and 15 years as a reference. The average accuracy rate reached 88%, exceeding professional doctors, according to South China Morning Post.