Learn Top 3 AI tools for Finance Domain in 2025 from Awon Learning

Predict the Future of Finance with AI | Transforming Forecasting, Sales & Investments

Are you ready to see how Artificial Intelligence is reshaping the world of finance?
In this video, we explore how finance professionals, investors, and analysts can use AI forecasting tools to predict market trends, optimize investment strategies, and make data-driven financial decisions with confidence.

You’ll learn:
1. What AI forecasting means in finance
2. Top tools used by financial professionals
3. How AI improves sales & investment forecasting
4. Real-world examples of predictive insights from data
5. Why AI is becoming essential for every finance role

If you're in finance, investment, or data analytics, this PDF will guide you the AI tools that you can learn and use for daily routine work as a finance analyst or finance manager!

Learn how to forecast smarter, reduce risks, and lead your organization into the future of finance.

1. AI FORECASTING
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how to use AI tools for
forecasting task

2. WHAT IS AI FORECASTING?
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AI forecasting uses machine
learning algorithms to predict
future outcomes based on
historical data.

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Examples: sales predictions, demand
forecasting, project completion
timelines, risk probability, financial
projections.
Unlike traditional forecasting, AI can
handle large datasets, identify hidden
patterns, and update predictions in real
time.

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Historical data is critical: sales
numbers, project timelines, weather,
costs, or resource usage.
Clean the data: handle missing values,
outliers, and ensure consistency.
How to Use AI Tools for
Forecasting
1. Collect & Prepare Data
Tools: Excel, Google Sheets, Python (pandas),
Power BI

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AI tools can use various methods:
Time Series Forecasting: ARIMA,
Prophet, LSTM (for sequential data
like sales trends).
Regression Models: Predict outcomes
based on multiple factors (e.g.,
revenue vs marketing spend).
Classification Models: Predict
categorical outcomes (e.g., will a
project finish on time: Yes/No).
Ensemble Methods: Combine multiple
models for higher accuracy.
2. Choose the Right Forecasting
Method

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1.Python / R: scikit-learn, TensorFlow,
PyTorch, Prophet.
2. Cloud AI Platforms:
Microsoft Azure AI
Google Cloud AI / BigQuery ML
AWS Forecast
3. Business Intelligence Tools with AI:
Power BI (Forecasting + AI visuals)
Tableau (with AI extensions)
4. No-code AI tools:
DataRobot, H2O.ai, Forecast Pro
3. Select an AI Tool

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1.Split data into training set (to teach
the AI) and test set (to check
accuracy).
2. Evaluate accuracy using metrics like:
MAPE (Mean Absolute Percentage
Error)
RMSE (Root Mean Square Error)
4. Train & Validate the Model

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1.Use the AI model to predict future
outcomes.
2.Continuously feed new data to retrain
& improve predictions.
3.Visualize results in dashboards for
decision-making.
5. Deploy Forecast

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1.Historical data: previous project
durations, resources, tasks.
2.AI model predicts:
Estimated completion date
Risk of delay per task
Optimal resource allocation to avoid
overruns
Example: Project Timeline
Forecast

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1.Microsoft Project + AI plugin
2.Excel with Azure ML integration
3.Python LSTM model for time-series
predictions
Tools you could use:

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Start with Excel or Power BI AI
forecasting for simple datasets.
Learn Python basics + scikit-learn /
Prophet for more advanced
forecasting.
Always validate predictions against
historical outcomes.
Combine AI forecasting with human
expertise — AI predicts, humans
interpret.
Tips for Beginners

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In our next slide will share how
to use forecast pro

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Thank you for reading
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