Bank XYZ came to us with a problem for check verification. They faced many problems in manual check signature verification such as check fraud, Legibility Issues, Incorrect Information, Insufficient Funds and Endorsement Issues. To mitigate these issues, they wanted a sustainable, time-saving, low-cost solution that could reduce the strain on human resources.
Solution: -
RPA was best suited for them as per their requirements. First, our RPA team created a complete model of how the RPA bot would work for signature comparison.
Workflow: -
Document Understanding: -
Steps include in Document Understanding
Document Understanding: -
The first step is to input the document that contains the signature. This can be done in various ways but we have done this by Optical Character Recognition (OCR).
The second step is to extract the relevant information from documents. This can include removing any noise or inconsistencies, aligning the document, and cropping the signature image.
Once the information has extracted, the location of the signature on the document must be determined.
further, the signature image can be extracted and prepared for comparison.
The extracted signature image can then be compared to a reference signature using AI Signature Comparison model.
AI Signature Comparison Model: -
The first step in creating an AI signature comparison model is to gather a large dataset of reference signatures that will be used to train the model. The dataset should include a variety of signatures and should be representative of the types of signatures that will be encountered in the real world.
The next step is to create the AI signature comparison model. This can be done using various machine learning algorithms such as decision trees, neural networks, or support vector machines. The model is trained on the reference signature dataset, allowing it to learn the characteristics of a signature.
Once the model has been trained, it should be validated to ensure that it is accurate and reliable. This can be done using various techniques such as cross-validation, holdout validation, or performance evaluation.
The final step is to deploy the model in the UiPath environment. This can be done using various methods such as using pre-built activities for machine learning, integrating with existing machine learning frameworks, or using custom code.
Conclusion: -
using UiPath for automating the process of comparing bank cheque signatures can provide a number of benefits. By automating this process, financial institutions can reduce the time and resources required for manual signature comparison, leading to increased efficiency and accuracy. Ultimately, this automation solution can help financial institutions to ensure the authenticity of cheques, enhance their fraud detection capabilities, and improve their overall customer experience.
