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摘要
当前大数据时代，商业银行业务面临着新的发展机遇，同时也迎来重大挑战。
大数据本身具备数据量大、渠道来源广、数据类型多以及信息多维度等特征，需要
通过数据处理和挖掘获取其隐藏的价值。商业银行信贷业务中，批发业务相较传统
零售业务占有更大的比重。因此，在现行的金融体系当中，银行批发信贷风险管理
则是最重要的风险管理之一。随着社会的发展，渤海银行所面临的批发信贷风险管
理也在日渐复杂化。传统风险管理方案及技术无法有效实现对借款人数据的信息收
集、数据信息的整合、挖掘分析，也容易产生批发信贷业务过程中信息不对称、审
批效率低、管理成本高等问题。因此，需要找到并借助新的办法和技术手段来应对
银行批发信贷风险。利用大数据来优化银行批发信贷风险管理，不仅仅是一个单纯
的技术问题，还涉及到其他多方面问题。所以基于大数据应用背景，研究分析渤海
银行批发信贷业务风险管理优化问题，也就具备了重大理论价值与现实意义。
基于以上背景，本文旨在对渤海银行批发信贷业务风险管理中的大数据应用开
展分析研究。首先，对大数据、信贷业务风险管理及银行业大数据应用情况进行分
析，为后文的分析提供理论及数据参考。其次，对渤海银行的基本情况和批发信贷
业务现有风险管理措施进行分析，研究得出其存在的问题包括：顶层设计影响风险
控制目标实现、风险信息数据挖掘难度大、客户信息数据割裂、风险信息的共享不
全面、缺乏贷后风险预警信号处置机制和缺乏大数据和风险管理的复合型人才。随
后，进一步对大数据应用于渤海银行批发信贷业务的必要性进行阐述。最后，提出
大数据应用背景下渤海银行批发信贷业务风险管理的优化对策：一是强化数据的获
取和挖掘，二是强化客户信息数据的结合，三是强化部门和分行之间的数据联系的
紧密性，四是强化贷后风险预警信号处置机制，五是强化人才队伍建设。
与传统银行的信贷业务风险管理相比，本文着重突出大数据对于优化风险管理
全流程的作用，并希望借此达到降低不良贷款率的目的。依照批发信贷业务的大数
据风险管理，可以将可能成功的模式复制到其他的业务开展中，从而真正达成改善
银行优化全流程风险管理、降低风险的目标。
关键词：大数据；渤海银行；批发信贷业务；风险管理
I
Abstract
In the current big data era, Commercial Bank business is facing new development
opportunities, but also major challenges. Big Data itself has the characteristics of large
amount of data, wide sources, multi-types of data and multi-dimensions of information,
and needs to acquire its hidden value through data processing and mining. In the credit
business of commercial banks, the wholesale business occupies a larger proportion than
the traditional retail business. Therefore, in the current financial system, the bank
wholesale credit risk management is one of the most important risk management. With
the development of society, the Wholesale Credit Risk Management faced by Bohai Sea
banks is becoming more and more complicated. The traditional risk management scheme
and technology can not effectively achieve the information collection, data integration,
mining and analysis of the borrower's data, also prone to wholesale credit business
process in the Information asymmetry, approval efficiency, high management costs and
other issues. Therefore, it is necessary to find and use new methods and technical means
to deal with the wholesale credit risk of banks. Using big data to optimize bank wholesale
credit risk management is not only a technical problem, but also involves many other
aspects. Therefore, based on the background of big dataapplication, it has great
theoretical value and practical significance to study and analyze the risk management
optimization of wholesale credit business of Bohai Sea banks.
Against this background, this paper aims to conduct an analytical study on the
application of big data in risk management of Wholesale Credit Operations of Bohai Sea
banks. First of all, the application of big data, credit business risk management and
banking big data is analyzed to provide theory and data reference for the following
analysis. Secondly, an analysis of the basic situation of banks in Bohai Sea and the
existing risk management measures in their wholesale credit business, the existing
problems include: the top-level design affects the realization of risk control objectives,
the difficulty of risk information data mining, the fragmentation of customer information
data, the incomplete sharing of risk information, the lack of risk early warning signal
disposal mechanism after loan, and the lack of large data and compound talents of risk
management. This was followed by a further elaboration of the need for big data to be
applied to Bohai Sea banks'wholesale credit operations. Finally, the paper puts forward
the countermeasures to optimize the risk management of wholesale credit business of
II