The era of large language model (LLM) has arrived and is currently transitioning from technological transformation to industrial transformation, exerting a profound influence on industries such as finance, healthcare, and education, as well as interdisciplinary or emerging professional fields. For instance, in the financial sector, LLMs possess an extremely strong ability to integrate and process data, capable of breaking down the data barriers within financial institutions and aggregating multi-source data scattered across various business systems, departments, and branches in different regions. LLMs can also integrate external data resources, such as regulatory policy dynamics and industry public opinion information, to grasp the external influencing factors of operational risks from a macro perspective. By learning from a vast amount of historical operational risk event data, LLMs can construct highly realistic risk scenario models, simulating scenarios such as bank loans, insurance claims, and securities trading under different fraud methods. Such as changing the purpose of the loan, misappropriating loan funds, monitoring loan defaults, fabricating insurance incidents, exaggerating the extent of losses, impersonating others to claim compensation, stock trading fraud, credit risk analysis, etc. Analyze the characteristic patterns of fraudulent behaviors in each scenario and formulate targeted risk identification strategies accordingly. In the field of emergency management, LLMs, by deeply integrating the vast amount of professional and application knowledge in emergency management, will play a significant role in multiple scenarios such as intelligent monitoring and early warning, empowering grassroots law enforcement, assisting command and decision-making, and supporting emergency rescue. Intelligent monitoring and early warning can detect potential risks in advance, thus buying precious time for emergency response. Empowering grassroots law enforcement can enhance the efficiency and accuracy of law enforcement, and reduce misjudgments and missed judgments. Auxiliary command and decision-making can provide scientific decision-making basis based on big data analysis, improving the pertinence and effectiveness of emergency response. Supporting emergency rescue can provide crucial information at critical moments and facilitate the rapid launch of rescue operations.

For this purpose, Shanghai Maritime University, Guangdong Banahe Big Data Technology Co., LTD., and the Guangdong public network security risk assessment and early warning emergency technology research center jointly host The 14th Emergency Management Scientists Forum and Financial Risk Management Forum (2025), jointly organized by the School of Economics and Management of Shanghai Maritime University, the School of Emergency Management of Jinan University, the School of National Security of Shanghai Maritime University, the Institute of Financial Engineering of Jinan University, the Asian Cruise College of Shanghai Maritime University and the Shanghai Advanced International Shipping College, It will be held at Shanghai Maritime University (Shanghai) on November 15-16, 2025 (registration on November 14). We sincerely invite scientists, top experts, entrepreneurs, renowned scholars, young talents, postdoctoral researchers, master's and doctoral students, etc. from home and abroad in the fields of emergency management, financial engineering, risk management, medical and health care, computer application, artificial intelligence, big data and cloud computing to participate in the forum and jointly discuss "Emergency Management and Risk Management in the Era of Large Language Models".

Forum official website address: http://www.emsffrmf.com/cn/ (the Chinese version is under construction)
http://www.emsffrmf.com/ (the English version is under construction)
Forum submission email: emsffrmf@163.com
Contact: Mr. Yang：15970369475；Mr. Pan：13353057393；Mis. Pan：15623655129
Supporting media:

The 9th emergency management scientists forum and Financial Risk Management Forum (2020)) and the 10th Young Scholars Forum (2021) in competitions 1, 2 and 3, A total of 76 papers were received and of which 24 excellent papers were successfully recommended for publication (or in the process of being revised) in the following 6 international top journals:

(1) Information processing & Management (SCI and SSCI, TOP，If=7.466) (1 published)
(2) Neural computing and Applications (SCI, If=5.120) (5 published)
(3) Environmental Technology & Innovation (SCI, If=7.758) (1 published)
(4) Sustainable cities and Society (SCI and SSCI, TOP，IF=10.696) (1 published)
(5) Journal of ambient intelligence and humanized computing (SCI, If=7.104) (2 published)

(6) Computational Intelligence and Neuroscience (SCI，IF=3.120) (8 published)