亚信科技

01 Participant

Champion: China Mobile, China Telecom, Agricultural Bank of China Suzhou Branch, China Guangfa Bank, Bank of Ningbo, Sichuan XW Bank

Participant: Huawei Technologies Co., Ltd., AsiaInfo, Sudo Privacy

02 Project Overview

In 2020, we have carried out innovations and practices in COVID-19 prevention and control and emergency management based on a trusted sharing platform, which has been widely praised by government leaders and the public. With the development of privacy protection computing technology and the surge in demands of vertical industries, this year we continue to apply privacy protection computing technology to the network systems of telecom operators and financial institutions with the trusted sharing platform as the basis, and strengthen cooperation with the financial industry to maximize the value of data and accelerate the digital transformation of the industry.

In recent years, under the impact of mobile Internet and mobile payment, the financial industry has encountered unprecedented challenges. In order to lower financial risks and promote inclusive financial services for benefiting people’s livelihood, financial institutions hope to improve the operational capabilities of financial products from the perspectives of credit investigation, risk control, marketing, and retention.

At the same time, the opportunities for data realization are also unveiled to telecom operators: On the premise of the compliance of data usage, based on the trusted sharing platform, financial institutions can be aided to improve their operational capabilities through the correlation analysis of financial and telecom data, such as reducing credit costs, increasing marketing success rates, improving customer service quality, and enhancing anti-fraud efficiency, so as to build a safer barrier for the business environment of financial industry.

03 Industry Challenge

With the rapid development of mobile Internet and mobile payment, financial institutions are facing tremendous pressure in the fields of credit investigation, risk control, marketing and retention. In the process of digital economy development, telecom operators hope to maximize the value of telecom data and gradually transform into data-driven enterprises to empower industry development and benefit people’s livelihood. Therefore, the key challenges faced by financial institutions and telecom operators mainly include:

• Credit investigation data of financial institutions mainly comes from banking, securities, insurance, and social security systems. Although the data is highly authoritative and valuable, it is largely homogenous and less effective, which causes difficulties for financial institutions to achieve efficient and accurate credit assessment of the public and corporate customers, thus affecting the efficiency of credit crediting and leading to the high costs;

• Traditional financial platforms have problems such as limited data elements, untimely data updates, and high platform access thresholds, resulting in insufficient risk control over the public and corporate customers by financial institutions. For example, in the anti-telecom fraud, since it is often difficult to accurately identify abnormal accounts in the first time, the security of account funds is thereby affected;

• Under the impact of mobile Internet and mobile payment, financial institutions are facing fiercer market competition. In the face of the ever-changing market, they seem to be powerless as their data elements have difficulties in achieving precise marketing and providing differentiated customer services;

• For telecom operators, they often suffer from the inability to put massive telecom data into safe and effective interaction with data in other fields, which leads to poor display and utilization of the value of data, and affects the confidence and progress of digital transformation.

04 Project Solution

By introducing privacy protection computing technology, the trusted sharing platform solves the problem of collaborative computing between parties that do not trust each other under the premise of ensuring data security. Its technical characteristics and application prospects have attracted extensive attention of large financial institutions, telecom operators, government departments and other industries. Under the current vast data volume, the industry represented by the communication industry is boasted with unique high-value information strongly related to the financial market, so the privacy protection computing technology can help financial institutions find risk-free excess earnings and take the lead in the market. The key scenarios for joint incubation and practice of the project are as follows:

Credit investigation and risk control: Based on the federated learning feature of privacy protection computing technology, financial institutions can conduct joint credit modeling for public or corporate customers to form an effective credit score. Generally, when the public or enterprises need financial services, financial institutions will conduct credit investigation and risk control based on consumption or income information, which often requires large manpower and system integration costs with a low efficiency of credit investigation. Nowadays, under the premise of ensuring that private information is not leaked and the use of data is safe and compliant, financial institutions establish a proprietary database in combination with telecom data, and conduct joint credit modeling and credit scoring for public or corporate customers, which can build low-cost and efficient credit investigation and risk control channels.

Customer acquisition and marketing: Based on the secure intersection feature of privacy protection computing technology, financial institutions can tap high-quality and potential users from the low-end and mid-end customer groups by integrating channels and business data in the telecom field, and link the high-value customer groups of telecom operators to implement precision marketing at the same time. In many cases, the same customer group presents different values in financial institutions and telecom operators. Financial institutions have only a small amount of consumption or income information, while operators have a large amount of online consumption and network income information, resulting in the ineffective exploration and utilization of the value of data. Nowadays, under the premise of ensuring that private information is not leaked and the use of data is safe and compliant, financial institutions can identify high-value and potential customer groups through modeling in association with telecom data, and accurately push financial products. In this way, the marketing success rate will be significantly increased, and the customer experience will also be improved.

Customer service and retention: Based on the secure multi-party computing and hidden query features of privacy protection computing technology, financial institutions can perceive customer feedback on financial products and services in a timely manner. On the one hand, customers seldom notify financial institutions the customer information changes to update the information immediately, which may affect subsequent financial services; on the other hand, customer feedback to financial institutions often comes from the mobile Internet or telecom operator platforms, which means that it is difficult for financial institutions to obtain customer feedback. Therefore, through the correlation analysis of financial and telecom data, financial institutions can learn about customer information changes and feedback in a timely manner in order to provide better customer service and increase customer stickiness and retention.

Anti-telecom fraud: Based on the secure intersection feature of privacy protection computing technology, financial institutions can effectively screen abnormal transactions and accurately identify telecom frauds. The occurrence of abnormal transactions occur in a financial account often means suspicious telecom frauds, but it is difficult for financial institutions to judge whether there must be a fraud through traditional methods. Now, under the premise of ensuring that private information is not leaked and the use of data is safe and compliant, financial institutions can quickly and effectively identify the possible telecom frauds and notify customers in a timely manner through trusted correlation analysis of financial and communication accounts to protect customers’ funds and ensure transaction security.

05 Project Value and Highlights

The trusted sharing platform provides a safe and efficient capability base for financial services, guarantees the safe, efficient, legal and compliant data service transactions, and protects the public sharing of data assets and the realization of data value. In the project, the data elements of telecom operators and financial institutions are efficiently exchanged through the trusted sharing platform, enabling financial institutions to greatly improve their operating capabilities in the fields of credit investigation, risk control, marketing, and retention, facilitating the steady development of financial services, and realizing the goal of value preservation and appreciation of data from telecom operators.

Through joint innovation and practice of project members, remarkable commercial results have been achieved: With comprehensive improvement of operating capabilities, financial institutions have reduced the bad debt rate by 1%, increased marketing success rate by 200%, improved satisfaction rate by 1%, with an additional income of approximately X0,000,000 yuan; with the improvement of the level of anti-telecom fraud, it is expected that 3.8 billion yuan of economic loss can be recovered in one year; telecom operators have achieved nearly 10 million yuan of additional revenue through data value realization.

In the future, telecom operators will further empower the financial industry, promote the sustainable and healthy development of inclusive finance, and energize the development of social production and people’s livelihood.

1. If you are interested in this project, please pay attention to the project space:

https://myaccount.tmforum.org/networks/21-0-203/CatTeam.html

2. Welcome to visit the project page at LinkedIn:

https://www.linkedin.com/posts/tm-forum_tm-forum-catalyst-trailer-trusted-data-activity-6813752629596827648-4Cab