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    ShineTech Decision-Making Platform Boosts Rural Financial Services

Interest rate liberalization and the rise of Internet finance have resulted in fierce competition with and between banks. Small banks, such as rural commercial banks, rural cooperative banks, and rural credit cooperatives, are less able to manage and reduce new risks than the ‘Big Four’ state-owned banks or various joint-stock banks. In addition, these small financial institutions face fierce competition for customers, competition that has been driving them to move their places of business closer to rural areas. This type of relocation is risky, and it introduces new challenges.

However, wherever there is risk, there is also opportunity. By the end of 2017, of the USD 4.62 trillion (CNY 31 trillion) in rural loans issued by Chinese banks, only 20 percent came from rural commercial banks. Opening up the rural financial market will create new business opportunities for rural banks struggling against fierce competition.

Faced with both risks and opportunities, small rural banks and credit unions must explore innovative ways to mitigate risk and new business models. They need to deploy advanced information technologies to improve efficiency and customer experience.

Technology Improves Competitiveness

ShineTech has a deep pool of respected technical and business experts from across the banking, finance, and technology industries. The company’s real-time decision-making platform, which is based on the Huawei FusionInsight RTD engine, provides user-friendly interfaces, a wealth of policy management functions, high concurrent big data processing capabilities, and millisecond-level responsiveness. With this platform and years of experience in the financial field, ShineTech can improve the competitiveness of financial institutions by strengthening their risk management and marketing capabilities.

  • Technologically, the decision-making platform complies with distributed big data architecture, enables real-time processing of massive quantities of data, and supports horizontal platform expansion. The event-based pipe model and service load-based dynamic scheduling ensure high concurrency and low latency. Memory query and incremental computing accelerates event flow processing, making it possible to invoke hundreds of models or computing rules. A rule compiler compatible with the SQL syntax allows service and technical personnel to use SQL for application development.
  • Functionally, this platform provides a graphical user interface, comprehensive operations, and management functions. This greatly simplifies technical requirements for service users, and frees service personnel from policy code development.

A Full-Featured Decision-Making Platform

The lack of a formal credit reporting system for rural residents is a major obstacle for rural financial institutions. If small banks use the traditional methods adopted by large banks, they may lose potential customers or take on uncontrollable risks. To compete effectively, they must provide precisely targeted services. Services that are precisely targeted at local markets are difficult to replicate on a large scale. Small, rural commercial banks, cooperative banks, and credit unions are far better positioned to innovate the rural credit industry, as their services are widely available in urban areas and they are well aware of the details specific to local markets.

The real-time decision-making platform can maximize this advantage, allowing rural credit organizations to improve their customer profiles and risk control models by adapting to changes in data and service in real time. Take the lack of a robust credit reporting system for rural areas as an example. To handle this problem, financial organizations can explain financial details to local residents on site and collect farmers’ financial requirements, basic details about them and their family members, agricultural acreage, as well as assets and liabilities. They can obtain additional details from local agricultural and trade organizations, and collect farmers’ mobile communications data and online behavior data. Then, a linked archive of rural economic details can be established, which can be mined for additional details. If a local resident lacks sufficient collateral, their creditworthiness can be evaluated based on associated partners, guarantors, and various enterprises. What’s more, agriculture is greatly affected by market fluctuations and natural disasters. Relevant data about national policies, the state of various markets, and meteorological events can be introduced to improve the risk control model.

Data, models, and business rules determined during service improvement can be directly exported to the ShineTech platform and configured immediately. Data is modified and maintained on the platform, and the latest data is used by specific services in real time. As massive numbers of account transactions, transfers, and payments are processed, historical data association analysis, event flow association analysis, time series analysis, in-depth learning model calculation, consumption group analysis for consumers of the same type, and transaction network relationship analysis can all be performed in real time, using various analytical methods, to determine whether to allow each current transaction. When users file a loan application or payment, or scan a QR code, the real-time decision-making platform leverages powerful computing capabilities, ultra-low latency, and ultra-high concurrency to serve financial institutions and consumers alike.

As the risk control system is improved, businesses will continue to grow. As businesses grow, more and more customer data will become available, which can then, in turn, be used to further improve the risk control model and better capitalize on marketing opportunities. As marketing policies are adjusted in real time based on customer behavior, preferences, and habits, services can be better tailored. What’s more, real-time and precision marketing is achieved when customers participate in marketing events. The real-time decision-making platform provides a Graphical User Interface (GUI) for quick modification and rollout of marketing policies without code development. In addition, it provides auxiliary functions, such as virtual testing, version management, operational monitoring, and data analysis, all of which support better iterative development. With big data-related technologies, response can be achieved on the platform within just milliseconds. Customer information is obtained the moment customers enter the marketing scenarios, preventing valuable marketing opportunities from being wasted. Based on long-term cooperative projects with various banks, the conversion rate can be increased by 20 percent just by converting legacy batch precision marketing into real-time marketing. If you can allow the customer to purchase something the moment they want it, conversion will increase. It’s that simple. Scenario-based, real-time precision marketing is the key to a significantly improved conversion rate.

Rural Credit Financial Institutions Grow with Huawei

Rural commercial banks, cooperative banks, and credit unions are expensive to operate and manage. These rural institutions must support numerous local branches and employees, yet offer no real advantages in regards to loan terms. Driven by big data, cloud computing, and artificial intelligence, rural banks need to develop online technologies, set up a real-time decision-making platform, and build intelligent big data risk control or marketing models and strategies to achieve the breakthroughs they need to thrive.

Using Southern China as a reference, ShineTech has been looking to rural credit providers throughout China. The company promotes advanced science, technology, and business models. Working together with Huawei, ShineTech has built a decision-making platform solution based on the FusionInsight big data platform. The ShineTech platform offers small, rural banks and credit unions professional financial system implementation and consulting services to drive growth in the rural credit sector.