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YouTube Thumbnail - AI Financial Forecasting Explained

AI-Based Financial Forecasting Model using Random Forest

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Objective:
The objective of this model is to accurately forecast monthly financial metrics—specifically Credit, Debit, and Balance—for each account group across an upcoming fiscal year. By analyzing historical balance sheet data, the model learns seasonal and temporal trends using Random Forest Regression and lag-based features. This enables organizations to proactively plan budgets, monitor cash flow, and identify financial risks or anomalies in advance. The forecasts are generated month- wise and exported to Excel, providing a structured and easily interpretable format for finance teams. Additionally, a user-friendly Gradio application facilitates quick predictions by uploading input files and selecting the desired fiscal year.

Data Source:

  • Historical Data: Two Excel files containing monthly balance sheet data:
    • Balance sheet_Monthly_FY22-23.xlsx
    • Balance sheet_Monthly_FY23-24.xls
  • Input Format: Each file contains multiple sheets, one for each month, with financial values for various account groups.

Data Preprocessing:

  • Extracts month and year from sheet names.
  • Cleans and consolidates data across fiscal years into one DataFrame.
  • Converts Current Period (INR) to numeric values.
  •  Maps months to integers and encodes account names numerically.
  • Builds a time series DataFrame with one row per account per month.

Model Architecture:

  • Model Used: RandomForestRegressor from scikit-learn.
  • Lag-Based Feature Engineering: For each account, 12 lag features are created (e.g., values
    from the past 12 months) to capture historical trends.
  •  Forecasting Window: 12-month forecast for each fiscal year using the trained model.
  • Rolling Forecast: Model predictions for each year are appended to the historical data for
    cumulative forecasting.

Gradio Application:
An interactive Gradio app is integrated to:

  • Allow users to upload Excel files.
  • Specify a target fiscal year (e.g., “2026–27”).
  • Automatically generate:
    • A downloadable Excel file with predicted values.
    • Line plots comparing historical vs. predicted values for key accounts.

Output:

  • Main Forecast File: Forecasted_Monthly_Amounts.xlsx
  • Monthly Excel Sheets by Fiscal Year: Auto-generated in the Forecast_Files_By_Fiscal_Year/
    folder.
  •  Line Graphs: Auto-saved and visualized to show trends for top accounts.

Technology Used:

  • Python: The core programming language used to develop the entire forecasting pipeline
    and user interface.
  • Pandas: Utilized for efficient data manipulation, cleaning, and transformation of Excel-based
    financial records.
  •  NumPy: Supports numerical computations and array-based operations essential for feature
    engineering and modeling.
  • scikit-learn: Provides the Random Forest Regressor and tools for training, prediction, and
    evaluation of the forecasting model.
  •  Matplotlib: Used for visualizing trends and comparing actual vs. predicted financial values
    over time.
  • Seaborn: Enhances the aesthetics of plots for deeper insight into feature relationships and
    distribution patterns.
  • OpenPyXL: Enables programmatic creation and export of structured Excel files containing
    forecasted financial results.
  •  Gradio: Powers the interactive web interface that allows users to upload data and generate
    predictions without coding.

YouTube Thumbnail - Resolve SAP Errors Easily with AI

AI-Powered SAP Error Resolution Assistant — Summary

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This project delivers an intelligent assistant designed to resolve SAP system errors using advanced AI techniques, with a focus on Type E (hard stop) errors. The solution integrates both traditional AI libraries and modern Large Language Models (LLMs) to provide real-time, context-aware resolutions for SAP users and consultants.

Key Features

Defect Library (Knowledge Base)

A structured internal database of known SAP errors (e.g., F5 263, M8147) and their resolutions.

AI Assistant Function

Takes user input (SAP error message) and searches the local library for solutions. If not found, it escalates to an LLM for assistance.

Auto-Learning Capability

When a new error is resolved via LLM or web search, the solution is added to the internal knowledge base — making the assistant smarter over time.

Technology Used

1

Python in Google Colab

core development environment

 

2

FAISS + Sentence Transformers

semantic search for matching error messages

 

3

Python in Google Colab

LLM backbone (used Mistral-7B-Instruct)

 

4

Pandas / JSON

for handling and persisting the error library

 

LLM Integration

  • Replaced basic keyword search with a semantic assistant powered by Mistral-7B-Instruct, running on GPU-enabled Colab.
  • Enables the assistant to generate human-like, step-by-step SAP error resolutions even if
    the error isn't present in the training data.

Real-Time Learning Workflow

  1. User submits an SAP error (e.g., “F5808”).
  2. Assistant checks local library (via FAISS).
  3. If not found, Mistral LLM generates a detailed resolution.
  4. The solution is appended to the library for future use.
  5. Current library is updated and displayed for verification.

YouTube Thumbnail - Forecasting Trial Balance with Random Forest

AI-Based Trial Balance Forecasting using Random Forest

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Objective:
The primary goal of this notebook is to forecast financial figures—specifically Credit, Debit, and Balance amounts—for various account codes on a monthly basis for the upcoming fiscal year. By utilizing machine learning, specifically Random Forest Regression, the notebook automates financial predictions based on patterns found in historical trial balance data. This enables organizations to proactively plan finances, allocate budgets, and identify irregularities or potential risks. The outputs are organized into Excel sheets for user-friendly review and analysis.


Data Source:
The input to this notebook is an Excel workbook consisting of multiple sheets. Each sheet corresponds to a particular month and includes:

  • Account Code (N-Code or equivalent)
  • Account Description
  • Debit Amount
  • Credit Amount
  • Balance (net value)
  • Other metadata such as opening balance (OB), which is dropped during cleaning
    The sheet names are used to extract the month information for time-based modeling.

Data Preprocessing:

Key steps include:

  • Sheet Consolidation: All monthly sheets are read into pandas and concatenated into one comprehensive
    DataFrame.
  • Column Cleanup: Unnecessary columns like ‘OB’ (Opening Balance) are removed.
  • Null Handling: Missing values are filled using appropriate techniques (e.g., forward fill, zeros).
  • Date Engineering: Sheet names are parsed to extract month-year, which is then converted to numeric
    format for modeling.
  • Categorical Encoding: Account codes and descriptions are transformed to numerical values where necessary for model compatibility.
  • Lag Feature Engineering: Previous month values are incorporated for trend learning (e.g., last month’s balance influencing the next).

Model Architecture:

  • Model: RandomForestRegressor from the scikit-learn library.
  • Model details:
    • Trained separately for each target variable: Debit, Credit, and Balance.
    • Input features include: account code, month index, and lagged historical values.
    •  Model Parameters: Number of estimators (trees), max depth, and random state are set to ensure reproducibility.
    • Training involves splitting historical data (e.g., FY 2022–2023) and predicting for future months (e.g., FY 2023–2024).
    • The model’s goal is to minimize forecasting error and generalize well to unseen months.

Output:

The notebook generates several key deliverables:

  • Forecast File: An Excel workbook containing predicted Credit, Debit, and Balance for each account by month.
  • Sheet-wise Organization: Optionally, each predicted month can be exported as an individual Excel sheet.
  • Graphs and Visualizations: Includes line charts comparing real vs. predicted values for top account codes.
  • Performance Logs: Accuracy, precision, recall, and F1-score for classification of variances (if
    implemented).

Technologies Used:

  •  Python: The base language for all preprocessing, modeling, and output automation.
  • Pandas: Handles tabular data, Excel input/output, and cleaning routines.
  • NumPy: Used for numerical operations and array handling.
  • scikit-learn: Powers the Random Forest Regression model and provides evaluation tools.
  • Matplotlib / Seaborn: Generates trend plots for visual comparison of actual vs. forecasted data.
  • OpenPyXL: Automates structured writing of predictions into formatted Excel files.
  • Gradio: (If integrated) Builds a no-code user interface for data upload and result download.