With the continuous development of financial technology and credit markets, credit accessibility is increasing, and groups of credit products are becoming larger which inevitably brings in more uncertainty and thus greatly intensifies the need of market participants to identify the dynamic risk. In response to these challenges, there has been a rapid adoption of artificial intelligence (AI) in recent years, particularly impacting the credit sector. The integration of AI into credit systems not only streamlines and accelerates the risk assessment process but also significantly improves predictive performance. AI-enhanced credit analytical tools offer the potential to transform traditional creditprocesses by providing deeper insights into borrower behaviour, predicting financial distress and default more accurately, and enabling proactive risk management strategies.
We are now organising the third Credit Scoring and Credit Rating Conference (CSCR III) which will be held on 18-20 Oct 2024 in hybrid formats: online and onsite in the city of Yangling, Shaanxi, China. The conference will be jointly organised by the Northwest A&F University (NWAFU), Southwestern University of Finance and Economics (SWUFE) and the University of Edinburgh (UoE). CSCR III aims to bring together academics, financial economists, practitioners, and policymakers to debate the changing landscape of AI applications in (credit) risk management over the coming decade and beyond. Along with the conference, we would like to proposea special issue or section on the theme of 'Artificial Intelligence and Credit Analytics'. The CSCR III conference and the associated special issue will share new perspectives on innovative AI applications in credit analysis, ethical considerations in automated decision-making, and the future potential of AI to enhance financial analysis, stability and accessibility.
Artificial Intelligence and Credit Analytics
人工智能与信用分析
2024年7月，党的二十届三中全会提出，要加快建立民营企业信用状况综合评价体系，健全社会信用体系和监管制度。国务院印发《新一代人工智能发展规划》提出面向2030年我国新一代人工智能发展目标，部署构筑人工智能发展的先发优势，加快建设创新型国家和世界科技强国。在当今数据驱动的时代，人工智能正以其强大的数据处理和预测能力彻底变革着各行各业。信用分析承担着评估个人或企业信用状况、预测违约风险和防范欺诈行为的重要职责。人工智能在信用分析中的应用，不仅提升了信用评估的准确性和效率，还促进了风险管理的创新和决策的公平性。人工智能技术的不断进步将为信用分析领域带来更多的可能性和变革，推动金融行业向更加智能化、精准化和普惠化的方向发展。
西南财经大学金融学院于2020年发起举办首届“信用评分与信用评级会议”（CSCR I），2022年与宁波诺丁汉大学联合举办第二届“信用评分与信用评级会议”（CSCR II），旨在为学术界和实业界搭建交流沟通的平台，深入探讨信用评分领域的前沿问题。经组委会商议，第三届“信用评分与信用评级会议”（CSCR III）将于2024年10月18-20日在陕西杨凌举行，由西北农林科技大学经济管理学院与西南财经大学金融学院联合主办，爱丁堡大学商学院协办，会议主题为“Artificial Intelligence and Credit Analytics”，将深入探讨AI技术在信用分析、风险评估等领域的应用。会议欢迎来自银行业、公司金融、消费金融、债券和信贷衍生品市场、金融科技、征信机构、评级机构、信用信息服务机构等方面的专家和学者一起讨论交流，分享与生成式人工智能、大模型、信用评分、信用评级和信贷资产风险管理相关的研究成果和技术进展。
Keynote Speeches
主旨演讲
Lucia Alessi
露西娅·阿莱西
Team Leader, Joint Research Centre at European Commission
Cuiqing Jiang
蒋翠清
Professor, Hefei University of Technology, China
Raghav Rao
拉加夫·拉奥
Professor, University of Texas at San Antonio, USA
Shantanu Dutta
沙坦努·杜塔
Professor, University of Ottawa, Canada
Jianping Li
李建平
Professor, University of Chinese Academy of Sciences, China
Publication Opportunity
期刊发表机会
British Accounting Review
International Journal of Financial Engineering
Information Systems Frontiers
Journal of Chinese Economic and Business Studies
Financial Innovation
China Finance Review International
Best Paper Awards
最佳论文奖
Best Paper Awards
Sponsored by Dapu Credit Rating Ltd.
1. Uncovering financial distress with textual risk disclosures in financial reports: Insights from large language models
Xiaoqian Zhu, University of Chinese Academy of Sciences
Hanlin Jin, University of Chinese Academy of Sciences
Jianping Li, University of Chinese Academy of Sciences
2. Tax Revenue Target and the Quality of Taxpaying Credit Rating
Yunxia Bai, Tongji University
Yewei Wu, Guangxi University
Best Student Paper Awards
Sponsored by SINOFIN Ltd.
1. Prompting is all you need: Predicting credit defaults using diverse text data
Zongxiao Wu, University of Edinburgh
Yizhe Dong, University of Edinburgh
Yaoyiran Li, University of Cambridge
Baofeng Shi, Northwest A&F University
2. From Words to Woes: Harnessing multi-source text for financial distress prediction
Chaochao Qiu, Northeastern University
Gang Li, Northeastern University
Zhipeng Zhang, Shanghai Jiao Tong University
Organisers
主办单位
Supporting Organisations
支持单位
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