Unleashing the Power of AI in Risk Management: A Step-by-Step Exploration
Education
Introduction
In today's fast-paced business environment, AI chatbots and AI assistants have become essential tools for risk management. These advanced technologies can perform various tasks typically assigned to junior quantitative risk managers or risk analysts. However, it is essential to remember that, like human analysts, AI systems can make mistakes, and final decision-making rests with the user.
Daily Applications of AI in Risk Management
For most of my daily tasks, I utilize one or more of three AI platforms: AIG GPT from OpenAI, ChatGPT, and a standalone risk awareness wisdom bot. I have created AIG GPT, which is publicly available, allowing users to access tailored risk management functionality. Unlike broader chatbots, this tool is equipped with a knowledge base curated by experienced risk managers, focusing specifically on risk management methodologies. While it cannot provide advice on constructing the perfect heat map, it excels in integrating risk analysis into contract reviews, project cost and schedule planning, budgeting, and investment decisions.
In addition to AIG GPT, I utilize a specialized risk awareness wisdom bot, trained on Risk Academy blogs, videos, and Risk Awareness Week materials. This bot often yields better results than ChatGPT's own risk awareness features, illustrating how training outside of the primary platform can produce effective outcomes. Google’s Gemini Advanced also offers capabilities for calculations and text manipulations, making it a valuable tool in this process.
In this demonstration, I will use OpenAI’s GPT for quantitative calculations. I will also validate the findings using Gemini. The example data I will work with today concerns magnesium sulfate—a raw material used as a reagent in various industries.
Data Management and Analysis
First, I will clean and structure the data accurately. Well-organized data with clear headings, dates, and values is crucial for effective analysis. After verifying that the data begins in January 2007 and checking for duplicates or missing values, I can proceed with the analysis.
I will begin by asking the AI to visualize the data and assess for trends or seasonality. The results demonstrate notable price volatility over the years and an overall increasing trend with historical fluctuations. These insights signify that utilizing this raw material may expose the business to market risk.
Next, I will request a calculation of price returns to explore how historical data can inform budgetary flexibility in purchasing magnesium sulfate. For instance, if we plan to budget for the raw material, we must account for expected price volatility in cash flow.
Throughout these interactions, it’s essential to approach the AI as if it were a junior risk analyst. The AI responds quickly, although its performance may vary. For example, it successfully calculated the distribution of returns, showing that monthly fluctuations can range significantly, which hints towards considerable market risk.
Investigating Distribution Types
To better understand the risks involved, I will explore whether the calculated returns fit a normal distribution or if another distribution might more accurately characterize them. Upon running the analysis, the AI indicates that fit for normal distribution is poor and suggests log-normal or T distributions instead.
The ability to ask follow-up questions allows for a deeper understanding, providing opportunities to clarify complex terms or request further explanations. This conversational model exemplifies the powerful interface that AI offers, creating a comfortable environment for interaction without the fear of seeming uninformed.
Having established more suitable distributions, I'll guide the chatbot in calculating key risk metrics, such as standard deviation, volatility, Value at Risk (VaR), and Conditional Value at Risk (CVaR). AI's capability to both perform calculations and offer advice on which metrics to apply is a significant asset in risk analysis.
Concluding Observations
As the analysis progresses, it becomes important to ensure that risk calculations are relevant to the business context. Price returns need to reflect the potential risks of price increases, prompting me to instruct the AI to adjust its calculations accordingly.
Ultimately, the AI successfully estimates that, at a 95% confidence level, the monthly price could increase by up to 47%, highlighting a substantial risk factor where, in one out of twenty months, prices may rise significantly. An understanding of such dynamics greatly enhances decision-making processes within a company.
By leveraging the power of AI in risk management, professionals can gather insights more efficiently and effectively while maintaining intelligent oversight of their analyses.
Keywords
- AI Chatbots
- Risk Management
- Quantitative Risk Analysis
- Data Visualization
- Price Volatility
- Value at Risk (VaR)
- Conditional Value at Risk (CVaR)
- Log-Normal Distribution
- T Distribution
- Business Decision Making
FAQ
Q1: How can AI assist in risk management?
A1: AI can perform tasks typical of junior risk analysts, such as data analysis, calculating risk metrics, and providing insights into market trends, thereby enhancing decision-making processes.
Q2: What types of data can be analyzed using AI tools?
A2: AI can analyze various data types relevant to risk management, including pricing data for raw materials, historical incidents, operational risk metrics, and market data.
Q3: Why is data cleaning important before using AI?
A3: Cleaning data ensures accurate results and helps avoid misleading analyses. Well-structured data allows AI to provide insightful outputs without the need for excessive troubleshooting.
Q4: What are common risk metrics that AI can calculate?
A4: Common risk metrics include standard deviation, volatility, Value at Risk (VaR), Conditional Value at Risk (CVaR), and expected shortfall, among others.
Q5: How does AI handle data distributions?
A5: AI can assess whether data fits a normal distribution and suggest alternatives like log-normal or T distributions, allowing for a more nuanced understanding of risk factors involved.