Risks-assessments-LLMs: An Intelligent Risk Assessment System for Negotiations Based on Large Language Models

This project is supported by the German Federal Institute for Risk Assessment (BfR). It explores the application of large language models (LLMs) to risk assessment in negotiation scenarios, aiming to use AI to assist in identifying and analyzing potential risks, provide intelligent support for decision-making, and address the issues of traditional methods relying on expert experience and struggling to capture dynamic and complex risks.

In scenarios such as business negotiations, diplomatic consultations, and legal mediation, risk assessment is a critical link in decision-making. Traditional methods rely on expert experience and static frameworks, making it difficult to fully capture complex risk factors in the dynamics of negotiations. With the breakthroughs of LLMs in natural language understanding and generation, new possibilities are brought to risk assessment. This project explores the use of LLMs to assist in negotiation-centered risk assessment work.

The project is supported by the German Federal Institute for Risk Assessment (BfR). Its goal is to integrate AI (especially LLMs) technology into risk assessment processes, create intelligent auxiliary tools, and help analysts and decision-makers comprehensively identify, analyze, and evaluate multi-dimensional risks (such as legal, financial, reputation, and strategic risks) in negotiations.

Core Role of Large Language Models

LLMs act as a knowledge integration and reasoning engine:

• Parse complex documents: Automatically extract key risk points from contracts, agreements, etc.
• Identify potential risks: Discover implicit risks and unfavorable clauses based on context
• Generate assessment reports: Structured output of risk levels, impact scope, and recommendations

Targeted Design for Negotiation Scenarios

Optimized for the dynamic, adversarial, and information-asymmetric characteristics of negotiations, the system understands the positions of all parties, identifies trap clauses, and evaluates the risk-benefit ratio of concession strategies to enhance assessment practicality.

• Improve efficiency: Shorten the assessment cycle, allowing analysts to focus on high-value judgment and strategy formulation
• Enhance comprehensiveness: Compensate for limitations in human experience and cognitive biases, and discover risk "blind spots"
• Support decision-making: Provide quantitative indicators and scenario simulations to assist in multi-variable trade-off decisions in high-risk negotiations

• Reliability and interpretability: Ensure LLM risk judgments are based on reasonable reasoning and avoid "hallucinations"
• Domain knowledge integration: Inject professional knowledge such as legal and financial expertise to make outputs meet professional standards
• Human-machine collaboration: Design reasonable processes so that AI serves as an assistant rather than a replacement, requiring practical optimization

Risks-assessments-LLMs represents a positive exploration of AI in professional fields. As model capabilities improve and domain adaptation matures, it can be expanded to broader scenarios such as negotiation strategy optimization and scenario simulation in the future, to deliver greater value.