An NLP-based system with pipelines for automating text data processing in identity and fraud detection.

Abstract

An NLP-based system that uses pipelines to automate text data processing in identity verification and fraud detection, including: a text data preprocessing module configured to process raw, unstructured text data from multiple sources, including transaction logs, emails, application forms, and scanned documents; an NLP module integrated into the pipeline, configured to apply techniques such as tokenization, named entity recognition (NER), sentiment analysis and pattern matching to the preprocessed text data; a machine learning model configured to analyze the processed text data and detect fraud patterns by identifying anomalies in the data based on historical fraud data; a rule-based validation module configured to further analyze the detected fraud patterns and ensure the accuracy and relevance of the fraud detection results; a module for generating real-time alerts, configured to notify stakeholders upon detection of fraudulent activity via communication channels such as email, SMS or mobile notifications; a multilingual processing module that enables the system to process text data in multiple languages ​​to ensure global applicability.

Description

[0001] The present invention relates to the field of text data processing, particularly to an automated system that utilizes natural language processing (NLP) techniques to detect identity and fraud-related anomalies in text data. The invention is particularly applicable to fields requiring real-time processing of large text inputs, such as banking, insurance, e-commerce, and cybersecurity.

[0002] Fraud detection and identity verification are critical in sectors such as banking, e-commerce, insurance, and healthcare, where organizations need to protect confidential data and prevent fraudulent activity. Traditional systems rely heavily on rule-based algorithms and manual processes to analyze unstructured text data from sources such as transaction logs, emails, application forms, and scanned documents. These systems often struggle to efficiently process large amounts of data, leading to delays in fraud detection and a high rate of false positives or undetected fraud. Furthermore, existing solutions typically lack the flexibility to adapt to evolving fraud tactics, further compromising their effectiveness.

[0003] The rise of sophisticated fraud methods such as identity theft, synthetic identities, and money laundering has made it clear that traditional fraud detection systems need to be more dynamic. Current systems also face the challenge of processing disparate and multilingual text data, which is essential for global enterprises. Furthermore, fraud detection processes are often fragmented, with different steps (data extraction, entity recognition, fraud pattern matching) treated separately, leading to inefficiency and slow response times.

[0004] Despite advances in fraud detection, existing solutions suffer from significant drawbacks. Many rely on manual intervention or semi-automated workflows that are prone to errors and delays. Furthermore, these systems are often unable to scale effectively with growing data volumes. Traditional rule-based approaches lack flexibility, making them less able to detect emerging fraud patterns or adapt to new threats, leading to high rates of false positives and missed fraudulent activity. Furthermore, these systems are typically slow to process large data sets, which is problematic in high-volume environments. Furthermore, existing solutions often cannot handle multilingual text, limiting their global applicability.Finally, inconsistent data quality, such as noisy or incomplete text, can affect the accuracy of fraud detection.

[0005] The present invention introduces an NLP-based system that leverages modular pipelines to automate the entire text data processing workflow. By combining advanced NLP techniques with machine learning models and rule-based algorithms, the system is designed to improve the accuracy, scalability, and speed of real-time fraud detection, overcoming the limitations of conventional methods.

[0006] To solve the problem, the present invention provides an NLP-based system that uses pipelines to automate text data processing in identity and fraud detection.

[0007] The NLP-based system, which uses pipelines to automate text processing in identity and fraud detection, streamlines the entire text processing workflow for identity verification and fraud detection, reducing the need for manual intervention and improving operational efficiency.

[0008] The NLP-based system, which uses pipelines to automate text data processing in identity and fraud detection, enables the processing and analysis of unstructured text data in real time, allowing organizations to detect fraudulent activities immediately, minimizing delays and improving the speed of decision-making.

[0009] The NLP-based system, which uses pipelines to automate text data processing in identity and fraud detection, can handle large amounts of data from various sources and ensures efficient processing even as the data grows, without compromising on speed or accuracy.

[0010] The NLP-based system, which uses pipelines to automate text processing in identity and fraud detection, leverages machine learning and NLP techniques to identify emerging fraud patterns and adapt to new fraud tactics, improving the accuracy and effectiveness of fraud detection over time.

[0011] The NLP-based system uses pipelines to automate text processing in identity and fraud detection, supporting the processing of multilingual text data, enabling global applicability and ensuring that fraud detection capabilities are not limited by language barriers.

[0012] The NLP-based system that uses pipelines to automate text data processing in identity and fraud detection, reducing the rate of false positives and false negatives in fraud detection by leveraging NLP models to extract relevant information and more accurately identify suspicious activities.

[0013] The NLP-based system uses pipelines to automate text data processing in identity and fraud detection, ensuring consistent and accurate extraction, cleansing, and normalization of text data, thereby improving the overall quality of the data used in fraud detection and increasing the reliability of the system.

[0014] The NLP-based system uses pipelines to automate text data processing in identity and fraud detection, which integrate with existing fraud detection infrastructures and data sources, minimizing the need for significant changes to existing systems.

[0015] The NLP-based system that uses pipelines to automate text data processing in identity and fraud detection, ensuring the processing of sensitive personal and financial data in compliance with data protection regulations (e.g., GDPR, CCPA) and ensuring compliance with legal requirements in fraud detection processes.

[0016] The NLP-based system uses pipelines to automate text processing in identity and fraud detection, continuously learning from new data and adapting to changing fraud strategies to ensure that the fraud detection mechanism evolves and remains effective in the face of new challenges.

[0017] In one embodiment, an NLP-based system is provided that uses pipelines to automate text data processing in identity and fraud detection. The system uses modular NLP pipelines that integrate advanced natural language processing techniques with machine learning models and rule-based algorithms. The invention is intended to improve the accuracy, scalability, and efficiency of real-time fraud detection processes, enabling organizations to quickly identify fraudulent activities while reducing manual intervention.

[0018] The system processes large amounts of text data from various sources such as transaction logs, emails, application forms, and scanned documents. It automates data extraction, entity recognition, and pattern matching, providing a more streamlined and efficient fraud detection workflow. By incorporating machine learning, the system can adapt to evolving fraud tactics, reduce false positives and negatives, and provide greater flexibility in detecting new fraud patterns.

[0019] The system is capable of processing multilingual text data, making it suitable for global organizations with diverse customer bases. It also ensures high data quality by cleansing and normalizing text, thus improving the reliability of fraud detection. The system integrates easily with existing fraud detection infrastructures, enabling a seamless transition without requiring major changes to legacy systems. Furthermore, it prioritizes data protection and complies with relevant regulations such as the GDPR and CCPA, ensuring the secure handling of sensitive information.

[0020] The invention is explained again below with reference to the figure. It shows: Fig. : shows an NLP-based system that uses pipelines to automate text data processing in identity and fraud detection.

[0021] Fig.shows an NLP-based system that uses pipelines to automate text data processing in identity and fraud detection. The system includes a text data preprocessing module, an NLP module, a machine learning model, a rule-based validation module, and a multilingual processing module. The text data preprocessing module is configured to process raw, unstructured text data from various sources such as transaction logs, emails, application forms, and scanned documents. The NLP module is integrated into the pipeline. The NLP module is configured to apply techniques such as tokenization, named entity recognition (NER), sentiment analysis, and pattern matching to the preprocessed text data. The machine learning model is configured to analyze the processed text data and detect fraud patterns by identifying anomalies in the data based on historical fraud data.The rule-based validation module is configured to further analyze detected fraud patterns and ensure the accuracy and relevance of fraud detection results. The real-time alert generation module is configured to inform stakeholders upon detection of fraudulent activity via communication channels such as email, SMS, or mobile notifications. The multilingual processing module enables the system to process text data in multiple languages ​​to ensure global applicability. The text data preprocessing module includes components for cleansing, normalizing, and structuring text data to ensure consistency and reliability for further analysis.The NLP module includes a tokenization component, a named entity recognition (NER) component, and a part-of-speech tagging (POS) component to identify important entities and grammatical relationships within the text data. The machine learning model leverages supervised and unsupervised learning techniques to detect fraudulent patterns and anomalies and continuously improves based on new data. The rule-based validation module includes customizable detection rules that allow for refinement of fraud detection based on changing fraud patterns and thresholds. The data normalization module is configured to filter out irrelevant or noisy data and standardize the text data before it is processed by the NLP module.The multilingual processing module includes automatic language recognition and language-specific processing capabilities for accurate processing of text data in various languages. The system is configured to process and analyze various types of data formats, including structured, semi-structured, and unstructured data, ensuring compatibility with a wide range of data sources. The feedback loop allows the system to incorporate user feedback and continuously learn from detected fraud cases, improving detection accuracy over time.

[0022] The process for automating text data processing in identity verification and fraud detection using the NLP-based system includes preprocessing unstructured text data extracted from transaction logs, emails, application forms, and scanned documents; applying NLP techniques, including tokenization, Named Entity Recognition (NER), sentiment analysis, and pattern matching, to the preprocessed text data; analyzing the processed text data using machine learning algorithms to detect fraudulent patterns and anomalies; validating the detected fraud patterns using a rule-based validation module to ensure their accuracy and relevance; generating real-time alerts for detected fraudulent activities and notifying relevant stakeholders via email, SMS, or mobile notifications. List of reference symbols 100 System

Claims

[1] An NLP-based system that uses pipelines to automate text data processing in identity verification and fraud detection, comprising: a text data preprocessing module configured to process raw, unstructured text data from multiple sources including transaction logs, emails, application forms, and scanned documents; an NLP module integrated into the pipeline, configured to apply techniques such as tokenization, named entity recognition (NER), sentiment analysis, and pattern matching to the preprocessed text data; a machine learning model configured to analyze the processed text data and detect fraud patterns by identifying anomalies in the data based on historical fraud data; a rule-based validation module configured to further analyze the detected fraud patterns and ensure the accuracy and relevance of the fraud detection results; a real-time alert generation module configured to notify stakeholders upon detection of fraudulent activity through communication channels such as email, SMS or mobile notifications; a multilingual processing module that enables the system to process text data in multiple languages to ensure global applicability. [2] The NLP-based system of claim 1, wherein the text data preprocessing module includes components for cleaning, normalizing, and structuring the text data to ensure consistency and reliability for further analysis. [3] The NLP-based system of claim 1, wherein the NLP module includes a tokenization component, a named entity recognition (NER) component, and a part-of-speech (POS) tagging component to identify key entities and grammatical relationships within the text data. [4] The NLP-based system of claim 1, wherein the machine learning model uses supervised and unsupervised learning techniques to detect fraudulent patterns and anomalies and continuously improves based on new data. [5] The NLP-based system of claim 1, wherein the rule-based validation module includes customizable detection rules that enable refinement of fraud detection based on changing fraud patterns and thresholds. [6] The NLP-based system of claim 1, further comprising a data normalization module configured to filter out irrelevant or noisy data and standardize the text data before it is processed by the NLP module. [7] The NLP-based system of claim 1, wherein the multilingual processing module includes automatic speech recognition and language-specific processing capabilities for accurately handling text data in different languages. [8] The NLP-based system of claim 1, wherein the system is configured to process and analyze various types of data formats, including structured, semi-structured, and unstructured data, thereby ensuring compatibility with a wide range of data sources. [9] The NLP-based system of claim 1, wherein the feedback loop enables the system to incorporate user feedback and continuously learn from detected fraud cases, thereby improving detection accuracy over time.

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