Episode 5: How to Build a Treasury Bot for Hedging Recommendations

Introduction

Managing FX risk efficiently is critical for any treasury department. A bot that provides hedging recommendations can help reduce risk exposure and optimize hedge strategies. This article will guide you through building an AI-powered Treasury Bot that assists in making hedging decisions based on real-time FX data, historical trends, machine learning-based risk assessments, and exposure calculations—all without requiring coding skills or IT department intervention.

What You Need

To build this bot, we will use:

• Microsoft Power Automate – to automate workflows
• Microsoft Excel / Power BI – for data storage and advanced predictive analytics
• Microsoft Copilot / OpenAI API – for AI-powered insights and natural language processing
• Azure Machine Learning (Optional) – for deeper predictive hedging analytics
• Power Apps – for a user-friendly interface
• External FX Data Sources – from APIs like Open Exchange Rates, Alpha Vantage, or Bloomberg Terminal
• SQL Database (Optional) – for structured data storage and advanced queries

Step 1: Set Up FX Data Import

First, ensure you have access to real-time FX rates. You can do this by:

1. Using a free FX API – Sign up for Open Exchange Rates, Alpha Vantage, or integrate with Bloomberg.
2. Power Automate Data Flow – Set up an automated flow to fetch data every hour and store it in an SQL database or an Excel file on SharePoint.
3. Store Data in a Structured Format – Ensure each row contains:
   • Currency pair (e.g., EUR/USD)
   • Timestamp
   • Bid/Ask rates
   • Volatility index (if available)
   • Historical Moving Averages (calculated automatically in SQL or Power BI)

Step 2: Define Hedging Criteria

Your bot needs to recommend hedges based on predefined and AI-driven rules:

• Hedging Threshold – Define a percentage deviation from the budgeted rate and adjust based on AI-driven volatility forecasts.
• Market Trends – Use historical volatility, standard deviation, and ML-based trend forecasting to adjust hedge recommendations.
• Exposure Level – Connect to your treasury cash flow forecast to assess FX exposures dynamically from SAP or an ERP system.
• Sentiment Analysis (Optional) – Use OpenAI or NLP models to analyze financial news and determine if external macroeconomic conditions suggest hedging.

Store these parameters in an SQL database or an Excel file, so they can be adjusted dynamically.

Step 3: Automate Hedge Recommendations

1. Set Up a Power Automate Flow to:
   • Compare real-time FX rates with the budgeted rate.
   • Assess volatility trends using Power BI or Azure ML models.
   • Retrieve current exposure levels from SQL or an Excel-based forecast.
   • Predict short-term FX rate movements using regression models or AI insights.
   • Calculate if a hedge is required and recommend hedge amount and instrument (e.g., forwards, options).
2. Apply AI Insights
   • Use Microsoft Copilot or OpenAI to analyze trends and suggest whether to hedge fully or partially.
   • Train an Azure ML model on historical hedging data to refine recommendations.
   • Example AI prompt: “Based on the last 30 days’ FX rate trends, news sentiment, and current exposure, should we hedge EUR/USD today?”
3. Send Multi-Channel Alerts
   • If a hedge is recommended, automate notifications via:
     • Email – Include details such as hedge amount, suggested forward rate, and justification.
     • Microsoft Teams or Slack – Use adaptive cards to enable quick decision-making.
     • Power Apps Interface – Enable real-time interaction with the bot.

Step 4: Build a Real-Time Monitoring Dashboard

Use Power BI or a web-based interface with SQL integration to create a live dashboard with:

• FX Trends – Display historical trends, standard deviations, and predictive analytics.
• Current Exposure – Show dynamically updated exposures per currency.
• Hedge Status – Monitor outstanding hedges vs. risk.
• Scenario Analysis – Simulate different hedge strategies and forecast their impact.
• Machine Learning Predictions – Show expected FX rates for the next 7/30/90 days.

Step 5: Deploy, Test, and Improve

• Implement in a test environment and analyze bot-generated recommendations against historical data.
• Adjust AI parameters and retrain models periodically for better accuracy.
• Expand functionality to incorporate commodity or interest rate hedging.
• Enable voice command support using Microsoft Copilot for ease of use.

This approach ensures accessibility for non-technical professionals while leveraging advanced analytics for smarter decision-making. Start small, automate key steps, integrate AI-driven insights, and let the bot refine your strategy over time!