Title: Similar Document Template Matching Algorithm. Category: Miscellaneous. Subcategory: Software. Organization: Bajaj Finserv Health Ltd.. Description: In our organization, we encounter a significant challenge when dealing with a large volume of medical invoices, prescriptions, and lab test reports received from numerous providers, including doctors, hospitals, and labs, as well as from customers. To streamline our processes and improve efficiency, we aim to develop a system capable of extracting and standardizing these documents at a provider level. Additionally, we need to identify claims across different providers that exhibit similar document templates. The primary issue we face stems from fraudulent customers who exploit identical digital or printed templates, with minor modifications such as changing the provider names, logos, colors, and text content positioning. By manipulating the customer details within these templates, they attempt to file reimbursement claims that are difficult to detect using standard document comparison checks. To tackle this problem, we require the development of a robust Similar Document Template Matching Algorithm. This algorithm will automate the process of template extraction and standardization, allowing us to identify similarities and patterns across various documents. By comparing the templates used in different claims, we can effectively identify instances of potential fraud, even when the textual content has been altered. The algorithm should be designed to handle a high volume of documents from diverse providers and customers. It should be capable of identifying commonalities in template structure, design elements, and formatting, while also accounting for variations resulting from legitimate differences between providers. Key objectives of the Similar Document Template Matching Algorithm 1. Template Extraction Develop a mechanism to extract and standardize templates from medical invoices, prescriptions, and lab test reports received from providers and customers. 2. Template Comparison Implement an algorithm to compare and identify similarities among different document templates, accounting for minor modifications and variations in content. 3. Fraud Detection Enhance the algorithm to flag instances of potentially fraudulent claims where similar templates are used, with modifications made to customer details while maintaining the overall structure and design. 4. Scalability and Efficiency Ensure the algorithm can handle a large volume of documents efficiently, considering the growing number of providers and customers we deal with. 5. Flexibility Design the algorithm to accommodate variations in template design and content across different providers, while maintaining the ability to detect fraudulent patterns The successful development and implementation of the Similar Document Template Matching Algorithm will significantly enhance our ability to detect fraudulent claims and improve the overall accuracy and efficiency of our document processing workflows. Theme Fraud Detection  Document Templatization Evaluation Criteria The effectiveness of the Similar Document Template Matching Algorithm will be evaluated based on several key criteria. The primary criterion is the accuracy of detection, which measures the algorithms ability to identify similarities in document templates accurately and efficiently. The algorithm should be able to detect instances where fraudulent customers exploit identical templates with minor modifications, distinguishing between suspicious and fraudulent documents. To aid in the interpretation and communication of the algorithms findings, the detected output should be represented using colorcoded flags. Amber flags can be used to indicate suspicious documents that exhibit similarities in template structure or design elements, while red flags should be assigned to identify documents with high potential for fraud. This visual representation will assist investigators in prioritizing their efforts and taking appropriate actions. Input Data Set The team will have to generate their own dataset using publicly available datasets or archives. This approach ensures that the algorithm is exposed to a wide range of document templates from various providers and customers. The dataset should encompass different document types, such as medical invoices, prescriptions, and lab test reports, reflecting the realworld scenarios the algorithm will encounter during deployment. By using this selfgenerated dataset, the algorithm can be trained and evaluated under realistic conditions. This approach enables the algorithm to learn patterns and similarities that exist in actual documents, allowing it to accurately detect fraudulent claims and identify suspicious patterns across different providers and customers. Teams working on this problem statement are advised to create their own dataset to build and train their algorithms.