Financial Technology, also known as fintech, is a fast-evolving field that has reshaped the financial industry. Ant Financial has redefined digital financial services, specifically mobile payment and microloan services, and Ping An Technology has developed innovative fintech to reshape the insurance, investment, and banking businesses.
Computing technologies play an important role in the transformation of modern financial services. Ant Financial, an affiliate of Alibaba and a leading Chinese fintech company, has participated in this transformation by using technology to bring financial services to hundreds of millions of individuals and small businesses in China and throughout the world. Two examples demonstrate its impact.
When an accident happens, the customer only needs to take a few pictures of the damaged car to file a claim from the accident site.
Ci Ren Ge Dan (Figure 1) runs a tent store at the foot of Mount Everest, 5,200 meters above sea level. He used to take half a day to go to the nearest bank. Carrying and keeping a lot of cash was inconvenient. Now he is one of many small merchants served by Ant Financial. Last year, Ci Ren Ge Dan added QR codes to items in his store, which allows tourists to pay him using their cellphones. He can also use his phone to pay electricity bills, deposit money, and acquire funds without leaving his store.
Zhang Yousheng (Figure 2) is a herdsman who has raised cattle for decades. In the past, he worried about having the funds he needed to buy calves and fodder, and about selling his cattle. After Ant Financial partnered with a cattle industry company to provide low cost microloans, Zhang said he no longer worries about funds and sales. His life as a herdsman is easier. Ant Financial's services have helped tens of millions of small and micro merchants in China, from prosperous cities to remote rural areas. The technologies behind these stories are a series of innovations that make financial services more accessible and affordable to everyone.
The innovations behind Ci Ren Ge Dan's story are payment technologies. Ant Financial started an escrow payment service 14 years ago, and held shoppers' payments until merchants delivered purchased items, providing a needed level of trust to e-commerce users. A second innovation came in 2010 when Ant Financial designed an express payment system that gave both users and banks a trusted payment platform. Although the initial technological challenge was expected to be connecting all banks, it turned out that the real challenge was controlling risk given a rapid increase in transaction volume. As a result, Ant Financial developed realtime risk management technologies that used rules and algorithms to analyze hundreds of thousands of transactions per second, improving transaction security dramatically. The express payment method has become a standard for Web and mobile payment.
In 2017, Ant Financial launched the Smile-to-Pay service based on computer vision technology. Instead of using a cellphone, a user smiles to a vending machine to complete a payment. As the first commercial facial recognition payment system, Smile-to-Pay took security and the user experience to a new level. The AI-driven product is based on imaging and vision analysis technology developed internally by Ant Financial.
Microloans are another area of Ant Financial innovation. When the service was launched in 2010, the first loan was for only 1,300 RMB ($180 USD). Ant Financial built a credit model based on data of merchants' previous sales and transactions. The combination of computing power and past behavior extended microloan services to more merchants. However, the operational costs were quite high, the user experience needed improvement, and it took three days for a merchant to get a loan.
A leap forward occurred when the system adopted advanced machine learning methodsincluding boosting, deep learning, and graph-based machine learningfor accurate credit modeling. The new system is characterized by three digits: 3, 1, and 0; a merchant takes less than 3 minutes to complete a loan application, obtains the decision in 1 second, with zero human intervention. The integration of systems engineering and algorithmic advances into the microloan operation makes the cost of each loan less than 2 RMB ($0.31 USD). Combining a convenient user experience with low operational costs, Ant Financial now serves tens of millions of merchants in China with accessible and affordable loans.
Financial service providers face three challenges when digitizing service for the future economyconnection: how to link users, merchants, and service partners in a low-cost, fast, and intelligent way; risk: how to control for aspects of financial risk; and trust: how to grant equal opportunity for all to be trusted, and trustworthy, in the digital space. To address these challenges, Ant Financial focuses on five technologies: blockchain, AI, security, IoT, and computing (BASIC). Blockchain helps to build a trusted global inter-connected system capable of storing, exchanging, and processing values; AI enables companies to build intelligent systems that better serve customers and business partners, and drive new product design; security is a pillar that makes digital systems safe and stable; IoT (Internet of Things) is a bridge that connects the physical and digital realms to transformative effect; and computing engines provide the digital space with computational power. The following paragraphs give more thoughts from Ant Financial on two of them: blockchain and AI.
Deep learning and natural language processing technologies helped intelligent customer service robots achieve higher customer satisfaction rates than live service staffs.
Two of the BASIC technologies merit a closer look. Blockchain provides a new trust mechanism to transactions. Over the past two years, Ant Financial has used it to improve the transparency of charities, strengthen the trust of insurance contracts, ensure the authenticity of house rental contracts, and improve the traceability of e-commerce supply chains. Ant Financial's applications are based on a consortium blockchain. However, current blockchain technologies face several key challenges in large-scale financial applications. Take a global e-commerce supply chain as an example. To support a global supply chain, blockchain nodes should be deployed in different continents, which affect the fairness of the consensus algorithm used by the blockchain system. If all supply, distribution, and sales records are stored in the same chain, the chain must be able to support hundreds of thousands of transactions per second. Not all records should be transparent to all participants, so a comprehensive mechanism is needed to protect the privacy and ownership of the data on the chain. All these are serious hurdles to a blockchain system.
Thus Ant Financial has developed an industrial-grade blockchain system to address these challenges. The company plans to share the system's value and open its blockchain technologies to the public in 2018.
Ant Financial uses AI to create a financial brain for the digital world. Recent years have witnessed the huge success of machine learning and deep learning in machine perception areas such as speech recognition and image analysis, but financial services need more, including prediction and decision-making. These capabilities, combined with a comprehensive financial knowledge graph, are the foundation of the financial brain at the core of Ant Financial's risk, credit, and customer service engines. The brain enabled Ant Financial to reduce its payment loss rate to less than one in a million, automatically answer millions of customer inquiries a day, automatically assess car damages based on computer vision and a vehicle knowledge base, and improve other services. In particular, deep learning and natural language processing (NLP) technologies helped intelligent customer service robots achieve higher customer satisfaction rates than live service staffs. During the popular Singles' Day 2016 shopping occasion, 97% of customer service inquiries on Ant Financial's Alipay service were handled by the intelligent customer service robots.
The surge of fintech in the past few decades has revolutionized the way financial industry personnel work, think, and live. Ping An has developed numerous technologies to advance the industry. Its areas of fintech concentration can be summarized as ABCDS: artificial intelligence, blockchain, cloud, big data, and security. AI is the core engine that drives industry automation and intelligence. Blockchain provides a revolutionary trust mechanism. Cloud computing lays the foundation for processing massive amounts of online transactions. Big data aids knowledge mining and decision making. Security is the essential element for safe and stable systems.
The core of Ping An's AI platform is the Ping An Brain engine. Covering a broad range of data analytics and AI techniques such as biometrics, NLP, image recognition, and more, Ping An Brain can provide full-stack AI solutions to enhance financial services scenarios such as marketing, customer service, and decision support. It has been successfully deployed across Ping An's insurance, investment, and banking businesses, greatly improving their effectiveness, efficiency, and costs.
For blockchain, Ping An was an early adopter, and has deployed a blockchain-based production system since 2016. By the end of 2017, it had over 12 blockchain-based platforms, covering fixed income trading, asset-backed securities, post trade reconciliation, and other transactions. By March 2018, its blockchain network had over 20,000 nodes across China and handled transactions valued at over one-trillion RMBs, including over 90% of those for Ping An OneConnect, the Ping An Group's fintech subsidiary.
A series of applications showcases its use of AI, big data, and blockchain.
As the capacity and scope of the insurance industry expands, the number of claims increases and leads to issues such as processing latency, high risk, potential misjudgment, and possibly fraud. To resolve such issues, Ping An has leveraged AI techniques across all insurance industry scenarios, including fraud detection, customer acquisition, and claims processing.
Take auto insurance as an example. When an accident happens, it often takes a long time to process a claim. Customers wait onsite for investigators to arrive and assess the damage, they wait as the claim is filed and processed, and they wait for a final decision. It is inconvenient for customers and costly to insurance companies. The process is also vulnerable to fraudulent claims. To address such problems, Ping An developed a system where the customer only needs to take a few pictures of the damaged car to file a claim from the accident site. The claim is processed within seconds and the customer given a precise payment calculation. The system involves a series of key modules: picture quality assessment, verification of insurance, car segmentation, identification of damage and related parts, payment calculation, and fraud detection. A number of AI techniques, such as image processing, image segmentation, and object recognition, were developed to support the functions. The system has been running in production at Ping An for over a year, successfully processing over 30,000 claims each day. It not only improves claim processing efficiency and thus customer experience, but also stops potential frauds on the order of multi-billion RMB. This system is now available to the insurance industry through the Ping An OneConnect platform.
Investment banks often need to assess the potential value and risk of targeted customersindividuals for retail banking or enterprises for corporate. In today's big data era, information comes from a broad range of resources with complex relationships between them. To make a precise assessment, it is crucial to organize and analyze such complex information in an efficient and effective manner. This is exactly what knowledge graph is designed for. Ping An has developed various knowledge graph techniques for retail and corporate businesses.
Take corporate risk assessment, for example. There are over 70-million registered enterprises, including households, in China. Their information comes from three major sources: commercial registration and daily operation; public news announcements and social posts; and business relationships including the supply chain, investments, and legal actions. To organize and analyze such rich and dynamic data, Ping An developed Euler Graph, an enterprise knowledge graph. The graph covers nearly all of China's 70-million enterprises, using data from all three sources. Millions of legal proceedings are automatically interpreted and over 40-million lawsuit relationships have been extracted and incorporated into the graph. Signals on enterprises are collected from over 300 news and social sites, totaling hundreds of thousands of articles daily, and updated every 10 minutes.
Information from these and other sources grows quickly. Deep graph analysis algorithms support business decisions on risk assessment and other matters. One advantage of Euler Graph is that business logic is directly integrated. For example, risks are assigned using different business logic for investments, bonds, or loans, and signals are extracted from an analysis of social and news data. Upstream and downstream relationships may also be encoded as risk indicators. When a risk event occurs upstream, the incident passes through the graph network and may influence an assessment. Through effective analysis by Euler Graph, risks such as defaults were successfully detected three to nine months ahead of occurrence. Euler Graph is also used for other applications, such as precision marketing and exploring investment opportunities.
Ping An OneConnect has identified various shortcoming impeding the wide scale adoption of blockchain. Performance and scalability bottlenecks have hindered its potential in building high volume financial transaction systems, and issues of data privacy and confidentiality have limited its usage in public service areas where few entities are willing to share data.
Ping An's blockchain research and cryptography team responded with the FiMAX platform. The architecture is designed to address all key problems hindering large scale blockchain adoption, with performance matching traditional databases systems and privacy protection enabled by advanced cryptology including various Ping An designed zero knowledge proof algorithms.
FiMAX has not only earned praise from Ping An's business partners, it has also gained recognition with its selection for some of the largest international blockchain networks being built for banks and regulators. For example, one cross border blockchain network to be launched later this year will comprise over 10 international banks and over 100 nodes.
Ant Financial made a series of innovations that led to key technologies behind mobile payment and microloan services in China. Ping An used innovative techniques to improve financial services for insurance, investment, and banking industries. Much progress has been made, but every problem solved opens the door to further questions and considerations. How should we model the transaction systems in a large-scale dynamic network, and implement intelligent inference and reasoning for better financial services? How can data be utilized and user privacy protected at the same time yet better than through current methods such as differential privacy? How can causal inference be applied in a complex system and when only observational data are available? Answering these questions will lead to tomorrow's breakthroughs.
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