Various ML algorithms can be used for this classification task, such as:
Using ML to solve for high-value deductions – using auto classification for deductions ML algorithms play a pivotal role in automating the classification of deduction types. Various models can be utilized for this task, including: • Natural language processing (NLP) models: NLP models such as Bidirectional Encoder Representations from Transformers (BERT) or Robustly Optimized BERT Approach (RoBERTa) can process textual data from deduction documentation, invoices, and communication logs to categorize deductions accurately. • Supervised learning models: Supervised learning algorithms like Random Forest or Gradient Boosting can be trained on historical deduction data to classify deductions such as pricing discrepancies, promotional allowances, and invoicing errors. • Deep learning models: Deep learning architectures like Convolutional Neural Networks or Recurrent Neural Networks can analyze structured and unstructured data to identify patterns and classify deductions. After categorizing deductions, the next step is identifying their validity using a second set of ML models. Classification for validity/invalidity in deductions management involves using AI-ML models to make probabilistic predictions on whether a deduction is valid based on a set of input features.
• Logistic regression • Decision trees • Random forests • Support vector machines • Neural networks
Ensemble learning methods, such as stacking or voting classifiers, are particularly effective in combining the predictions of multiple base models to improve accuracy.
Feature selection
The first step is to select relevant features (functional KPIs) that differentiate between valid and invalid deductions.
Detailed feature engineering will help drive model accuracy, with features including:
• Contractual terms and conditions • Historical deduction trends • Invoice accuracy metrics • Customer payment history • Product or service delivery confirmation • Communication logs • Correspondence with customers • Seasonality Rather than providing a binary classification, the model generates a probabilistic output indicating the likelihood (percentage probability) of a valid or invalid deduction. This probabilistic approach provides more nuanced insights into the model's confidence level in its predictions. To enhance transparency and trust in the
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