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Leader in supervisory technology, governmental and corporate service think tank BBD

Previously known as BBD Anti-Fraud Department, since 2015 the renamed BBD Regtech has been dedicated to providing acute early-warning supervision in "illegal fund-raising" and "abnormal corporate behavior" risks to financial supervisory government bodies. BBD Regtech's big data risk monitoring and early-warning platform utilizes big data technology and creates a special risk identification model that is based on multi-dimensional and fully quantified data and achieved via breakthroughs in information tracking and event trends, which will facilitate commercial operating entities and government supervisory bodies in pinpointing and getting early-warnings to major financial, accounting and legal risks. Since 2016, BBD Regtech has been bolstered with an overhaul to its big data risk monitoring and early-warning platform and optimization to its risk early-warning model, while expanding its supervision businesses to the China Insurance Regulatory Commission, Ministry of Housing and Urban-Rural Development, Customs and other governmental bodies and financial organizations other than the municipal finance office. An industry- wide service chain for governmental and corporate supervisory businesses has thus emerged.

Main products and services

Main products and services

Data service system

Government affairs data collection government affairs data cleansing government affairs data mining supplementary corresponding data service modules

Big data risk monitoring and early-warning platform

Illegal fund-raising monitoring and early-warning platform Customized monitoring technology solution

...

new finance big data monitoring and early-warning platform

Full-process smart management solution for cases Insurance industry risk monitoring big data solution Real estate industry credit appraisal big data solution

consulting service

High-risk company list corporate risk early-warning industry risk assessment report analysis reports for wide range of other industries

Core products

Core products

big data monitoring and early-warning platfor

Platform background

Rampant illegal fund-raising
Financial stability a priority of the state

Platform highlights

National new finance health appraisal
associated party topography in-depth data mining
coordinated regional and industry monitorin
LFR financial risk indicator quantification
smart real-time risk report
screening of companies potentially engaged in illegal fund-raising
in-depth investigation and analysis report
precise pinpointing of potential risks
industry features risk assessment model
7x24 customized tracking and monitoring system

Platform objectives

Pre-event identification, real-time tracking, earlywarning monitoring
Preemptive discovery, rapid response, timely processing
Lower monitoring costs, improve monitoring efficiency, ensure financial safety

Platform business logic

Deploying distributed and high-performance Internet crawlers to perform collection and noise reduction of non-structuralized big data in real-time, the data is then converted into a standardized and structuralized result. Machine learning methods such as convolutional neural network, LDA clustering and support vector machine are used to locate and extract risk factors with high relevance among the multi-source heterogeneous data, so as to construct a full-coverage, reasonably weighted and quantifiable industry feature risks model that can simulate trends in risks of illegal fund-raising, thoroughly profile a company's DNA and accurately locate a company's risks associated with illegal fund-raising.

Risk pre-warning model

BBD new finance big data monitoring and early-warning platform's risk early-warning model is comprised of inherent logics in products, organizations and supervision of sub-industries as foundation, big data monitoring as the core, and machine learning and artificial intelligence as methods, enabling dynamic, up-to-the-minute, interactive and deeper mining of corporate data and the behaviors represented behind such data. Coupled with effective presentation tools and formats, events that have happened and are concurrently taking place can be rapidly discovered, and predictions of potential risks and their probability of occurrence become achievable, thus permitting com prehensive, standardized and far-sighted monitoring of enterprise actions and potential risks within a financial system.

The various risk dimensions are calculated comprehensively using a machine learning-based quantified appraisal model, and the LFR financial risk indicator score of each targeted object is confirmed, allowing collation from a sea of data and the identification of high-value targets that are flagged for high risk potential, have great degree of impact and demand attention from supervisory bodies.