The hottest big data artificial intelligence cuts

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Big data artificial intelligence "cuts into" the collection and disposal of non-performing assets

how to quickly and properly dispose of non-performing loan assets through advanced technologies such as artificial intelligence and big data has become one of the main ways to resolve financial risks

Cheng Fei, chairman of Shenzhen United Financial Holdings risk asset management Co., Ltd. (hereinafter referred to as "United Financial Holdings"), said in an interview with this newspaper on the sidelines of the recently held money20/20 Hangzhou summit that based on the rapid development of big data, artificial intelligence, cloud computing and other technologies, the company launched an intelligent platform for the management of non-performing asset cases of financial institutions called "case awareness", creating seven module functions such as creditor's rights management and loan management, It provides financial institutions with a data-based creditor's rights evaluation system, accurate data statistics, intelligent submission, follow-up and reminder of all case tasks, a complete management system for outsourcing institutions, synchronous collaboration functions, and a wealth of information about all cases? The knowledge safety belt impact testing machine is used for the impact test content of safety belt for high-altitude fence, suspension and climbing effect

Cheng Fei told that by the end of last year, the scale of non-performing assets of Chinese commercial banks had reached about 2trillion yuan, but the degree of informatization and intelligence in the non-performing assets business was far from matching its scale, and even far lower than the informatization level of other business departments of the bank. The reason is that the non-performing assets business process is long and involves a wide range of fields. As a result, many banks do not dare to use big data, artificial intelligence, cloud computing and other technologies to establish new process management and risk management and control systems, so as to avoid omissions in the risk control process and business disposal

he said, "at present, technologies such as artificial intelligence and big data are becoming more and more mature, which have been able to solve the worries of financial institutions such as banks." For example, the "case awareness" platform helps financial institutions build a series of new non-performing asset disposal business operation processes, such as automatic data capture, OCR identification, distributed cloud computing, AI machine learning, text search engine, etc., based on the actual scenarios of non-performing asset collection business, so as to form its own big data system of risk assets and realize intelligent risk control

he bluntly said that due to the constant changes in the business conditions of enterprises, it used to take about 3 months for the staff of relevant departments of banks to complete the collection and updating of enterprise information. As a result, a large number of staff of relevant departments of banks had to spend about 3/4 of their time updating the business data and verifying the business conditions of enterprises in the process of clearing and receiving non-performing assets, and only 1/4 of their time focused on the design of non-performing assets disposal schemes and negotiating with customers, Therefore, the overall efficiency of the collection and disposal of non-performing assets is quite low; If we can continuously reduce the energy and time consumption of these employees in collecting and updating enterprise operation data through the constant data update and big data analysis of enterprise operation status of the intelligent platform for non-performing asset case management of financial institutions, they can spend more energy to design a more optimized non-performing asset collection and disposal scheme, so as to improve the disposal efficiency of non-performing assets

it is learned that there are usually three ways to dispose of large non-performing assets at present. One is litigation. The process cycle of this way will be very long, which generally takes two to three years; (2) Once the material of the cold-formed support is crushed (if necessary), pull it apart from both sides equidistantly. Second, negotiate and settle with the customer; The third is to sell asset claims directly to asset management companies. Many insiders therefore believe that the current application of big data, artificial intelligence and other technologies in the field of non-performing assets disposal and risk control mainly focuses on the online and intelligent disposal process, but the key factor determining the execution efficiency of non-performing assets disposal - the execution efficiency of the court and the negotiation efficiency of customers - still highly depends on manual operation

"we hope that the data accumulation and deep learning ability of artificial intelligence can quickly fill this gap." Cheng Fei said. However, if artificial intelligence technology does not have the ability to accumulate and learn data for many years, it is difficult to truly replace artificial decision-making. The reason is that non-performing assets involve a wide range of industries. On the basis of a large number of research and Analysis on the industry, artificial intelligence technology can effectively help improve the operation efficiency of artificial decision-making

in addition, whether artificial intelligence can further improve the intelligent risk control efficiency of non-performing assets and the asset disposal decision-making mechanism is also closely related to whether courts at all levels can open ports for direct data connection, whether asset management companies can connect ports to carry out risk pricing exploration on similar non-performing assets, and continue to undertake the research on the newly increased capital. Whether the debtor's multi borrowing information can be shared to improve the judgment and decision-making efficiency

therefore, based on the successful intelligent operation experience and analysis of the disposal of non-performing assets after loan, United financial holding is planning to extend the intelligent risk control from the post loan link to the pre loan link, and further cover the service scope to non bank financial institutions,

"the complexity of NPA disposal determines that only when big data, artificial intelligence and other financial technology technologies are combined with application scenarios, can advanced technologies directly serve business scenarios, accurately grasp customer needs, provide targeted technologies, and effectively solve various specific problems of customers in the daily work of NPA disposal." Cheng Fei said frankly

(source: 21st Century Business Herald)

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