Enhance Bulk Salary Transfer Systems with ML-Assisted Labeling and Datasaur Dinamic

Update your bulk salary transfer systems effortlessly with Datasaur Dinamic's ML-assisted labeling. Learn how to integrate new data entities smoothly.
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Mega Fransiska
May 18, 2024
Published on
May 18, 2024
May 25, 2024
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The financial services industry must continually adapt to new regulations and evolving business needs, requiring frequent updates to automated systems. A common challenge is integrating new types of extracted information and data into these systems, such as the need to add extracted information from letter of instruction, which facilitate bulk salary transfers.

Case Example: Expanding Labels

In our previous blog post, "Simple Steps to Build a Model for Bulk Salary Transfers with Datasaur Dinamic", we outlined the process of building an extraction model to enhance bulk salary transfer processes with Datasaur Dinamic. Now, let's explore how this technology can assist in expanding labels to accommodate new requirements, such as transitioning from single-currency to multi-currency transactions.

Imagine a bank that previously handled single-currency bulk transfers moving forward to process multi-currency transactions. This requires the system to recognize and extract new details like different currencies and their exchange rates. To achieve this, the existing model needs to be updated. This involves re-labeling the dataset to include these new entities and re-training the machine learning models for accurate recognition.

Re-Labeling Data using ML Assisted Labeling

Updating your system doesn’t have to be cumbersome. Datasaur’s ML-Assisted labeling simplifies the re-labeling process significantly. Here’s how you can effortlessly integrate new data entities into your model:

  1. Create a New Project: Set up a project including datasets that need labels for new entities.
  2. Enable ML-Assisted Labeling: Activate this extension and select the Huggingface provider (Documentation) to leverage the existing Huggingface model, that was trained by Datasaur Dinamic.
  3. Configure and Predict: Ensure the model name matches the existing Datasaur Dinamic setup, then click the ‘Predict labels’ button to automate data labeling.
  4. Focus on New Entities: With original information on data labeled automatically, the labelers can concentrate on annotating new, crucial information such as different currencies and their corresponding rates.

Re-Training Data using Datasaur Dinamic

Once your data is fully labeled, re-training is simple with Datasaur Dinamic. Follow these steps to update your model:

  1. Set Up Your Training: Follow the same steps from your initial setup to configure the training process.
  2. Train Your Model: Use the detailed instructions available in the Datasaur Dinamic documentation to complete the training.

This straightforward process allows you to develop an updated model, ready to handle new requirements efficiently.

Expanding Labels with Real-World Impact

In our previous blog post, "Simple Steps to Build a Model for Bulk Salary Transfers with Datasaur Dinamic", we demonstrated how an extraction model could enhance bulk salary transfer systems at Indonesia's largest bank. By adopting Datasaur's automated data extraction technology, the bank improved operational efficiency by up to 60%. This substantial enhancement allowed staff to focus on verifying and validating data, reducing human errors by over 60%.

Imagine extending this improved efficiency to manage multi-currency transactions. The system requires updates to identify and extract essential details such as various currencies and their exchange rates. Datasaur streamlines this transition through:

  1. Re-Labeling Data Using ML-Assisted Labeling: Most data is automatically labeled, allowing your team to focus on new, critical details like different currencies and their rates.
  2. Re-Training Data Using Datasaur Dinamic: Once the data is accurately labeled, re-training the model is straightforward. Simply follow the established procedures to update and configure your training processes, ensuring the model adapts accurately to the new specifics.

With Datasaur Dinamic and ML Assisted Labeling, upgrading automated bulk salary transfer systems becomes a streamlined process. This empowers financial institutions to manage complex tasks with greater accuracy and reduced processing times.

Transform Your Model Training Today

Adapting automated systems to new requirements is crucial for banks to stay competitive and compliant. Datasaur Dinamic, combined with ML-Assisted Labeling, offers a powerful and efficient solution for updating machine learning models. This ensures smooth upgrades, like moving from single-currency to multi-currency transactions, without hassle.

Ready to enhance your labeling efficiency or boost your NLP capabilities? Contact us at sales@datasaur.ai to find out how our solutions can streamline your operations. Book a demo today and experience the benefits of efficient, automated data management and advanced NLP solutions.

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