Description:FIELD OF THE INVENTION
[0001] The present invention relates to a tool and method for visualizing and detecting fraudulent financial activities. Specifically, it provides a tool for identifying tax evasion and circular trading by mapping and analyzing transactional relationships among various entities in online or offline within secure intranet environments.
BACKGROUND OF THE INVENTION
[0002] Background description includes information that may be useful in understanding the present invention. It is not an admission that any of the information provided herein is prior art or relevant to the presently claimed invention, or that any publication specifically or implicitly referenced is prior art.
[0003] Financial systems often require the analysis of large datasets to detect patterns indicative of fraudulent transactions and tax evasion. Traditional methods struggle to visualize complex, circular transaction flows effectively and often demand extensive manual analysis. Current network analysis tools face challenges in handling large-scale, online or offline data interactively. Business users, particularly those addressing fraudulent activities like tax evasion, often find it difficult to identify complex relationships within data. Existing tools provide limited interactivity, struggle to visualize hidden patterns, and lack the capacity to accommodate big data or function effectively within restricted network environments, such as intranets. While some Graph Visualization and Data Security Solutions exist, they lack flexibility in data interaction, scalability, and compatibility with restricted network settings.
[0004] This invention addresses these limitations by enabling secure, online as well as offline visualization of entity relationships through interactive nodes, focusing on circular flow patterns associated with tax evasion and fraudulent trading activities. This invention introduces unique features that overcome existing limitations, including interactive data exploration, robust scalability, infrastructure compatibility, and effective fraud detection. Users can interact with nodes, highlight paths, and configure settings to easily map complex relationships. The tool efficiently handles large datasets, such as UPI transactions or confidential GST data, making it suitable for big data analytics, even within intranet environments. By identifying circular trading and fraudulent activities, it provides a visual representation that aids in detecting and preventing tax evasion.
[0005] In conclusion, the Circular Flow Visualization Tool (CFVT) serves as a powerful solution for identifying fraudulent transactions and circular trading. By integrating advanced technologies and data-driven insights, CFVT empowers users and authorities to proactively detect tax evasion and fraudulent trading, supporting enhanced decision-making.
[0006] The present invention satisfies the existing needs and addresses the forementioned limitations of prior arts.
OBJECTIVES OF THE PRESENT INVENTION
[0007] The main object of the present invention is to provide a tool and method for visualizing and detecting fraudulent financial activities.
[0008] Yet another object of the present invention is to provide a tool for identification of tax evasion and circular trading by mapping and analyzing transactional relationships among various entities in online or offline within secure intranet environments.
[0009] Yet another object of the present invention is to provide a tool having intranet compatibility for secure analysis of large, confidential datasets.
[0010] Yet another object of the present invention is to provide a tool for enhance decision-making through graphical representation and filtering.
[0011] Yet another object of the present invention is supply chain management for tracking relationships between vendors.
[0012] Yet another object of the present network analysis in IT infrastructures to monitor systems and relationships
SUMMARY OF THE INVENTION
[0013] This summary is provided to introduce a selection of concepts in a simplified form that is further described below in the detailed description section.
[0014] This summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used as an aid in determining the scope of the claimed subject matter.
[0015] The present invention relates to a tool and method for visualizing and detecting fraudulent financial activities. Specifically, it provides a tool for identifying tax evasion and circular trading by mapping and analyzing transactional relationships among various entities in online as well offline within secure intranet environments.
[0016] In one embodiment, the present disclosure includes a circular flow visualization tool, comprising:
a. a data collection module configured to collect and preprocess transactional data from various sources;
b. a node mapping functionality that represents entities as nodes and maps relationships based on transactional data;
c. an interactive visualization layer allowing online or offline exploration and manipulation of nodes and relationships;
d. a fraud detection mechanism that identifies circular relationships indicative of circular trading or tax evasion; and
e. a reporting engine for generating visual summaries and exporting analysis results.
[0017] In another embodiment, a method for detecting fraudulent patterns in financial transactions, comprising the steps of:
a. Data Input: receiving transactional data through a database or a direct input interface, including Tableau;
b. Node Mapping: mapping entities as nodes and establishing relationships between the nodes based on the transactional data;
c. Customization & Filtering: applying customizable filters to focus on specific relationships, adjust node appearance, and highlight directional paths between entities to trace transaction flows;
d. Interactive Features: providing interactive features, including clickable nodes, drag-and-drop functionality, and directional arrows, for online or offline exploration of the network;
e. Fraud Detection: identifying fraudulent patterns, including circular trading or tax evasion, by detecting circular relationships among the nodes; and
f. Visualization & Reporting: generating visual summaries and reports based on filtered data, including the export of notifications.
[0018] These and other features will become apparent from the following detailed description of illustrative embodiments thereof, which is to be read in connection with the accompanying drawings. While the invention has been described and shown with reference to the preferred embodiment, it will be apparent that variations might be possible that would fall within the scope of the present invention. Various objects, features, aspects and advantages of the inventive subject matter will become more apparent from the following detailed description of preferred embodiments.
BRIEF DESCRIPTION OF THE DRAWINGS:
[0019] FIG. 1: A block diagram illustrating a circular flow visualization tool.
[0020] FIG. 2: A block diagram further illustrating interactive visualization layer of the circular flow visualization tool from FIG. 1.
[0021] FIG. 3: A block diagram further illustrating interactive visualization layer of the circular flow visualization tool from FIG. 1.
[0022] FIG. 4: A block diagram further illustrating the circular flow visualization tool from FIG. 1.
[0023] FIG. 5: A flowchart illustrating a method for detecting fraudulent patterns in financial transactions.
[0024] FIG. 6: A flowchart illustrating a stepwise method for detecting fraudulent patterns in financial transactions.
[0025] FIG. 7: A flowchart illustrating example 1 for detecting fraudulent patterns in financial transactions.
[0026] FIG. 8: A flowchart illustrating example 2 for detecting fraudulent patterns in financial transactions.
[0027] FIG. 9: A flowchart illustrating example 3 for detecting fraudulent patterns in financial transactions.
DETAILED DESCRIPTION OF THE INVENTION
[0028] The following is a detailed description of embodiments of the disclosure. The embodiments are in such detail as to clearly communicate the disclosure. However, the amount of detail offered is not intended to limit the anticipated variations of embodiments; on the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the spirit and scope of the present disclosure.
[0029] In the following description, embodiments of the invention are described in sufficient detail to enable those skilled in the art to practice the invention and it is understood that other embodiments may be utilized and that logical changes may be made without departing from the spirit or scope of the invention. To avoid detail not necessary to enable those skilled in the art to practice the embodiments described herein, the description may omit certain information known to those skilled in the art. The following brief description is, therefore, not to be taken in a limiting sense and the scope of the illustrative embodiments are defined only by the claims appended to the complete specification to be filed hereafter.
[0030] As used throughout this description, the word "may" is used in a permissive sense (i.e. meaning having the potential to), rather than the mandatory sense, (i.e. meaning must). Further, the words "a" or "an" mean "at least one” and the word “plurality” means “one or more” unless otherwise mentioned.
[0031] Furthermore, the terminology and phraseology used herein are solely used for descriptive purposes and should not be construed as limiting in scope. Language such as "including," "comprising," "having," "containing," or "involving," and variations thereof, is intended to be broad and encompass the subject matter listed thereafter, equivalents, and additional subject matter not recited, and is not intended to exclude other additives, components, integers, or steps. Likewise, the term "comprising" is considered synonymous with the terms "including" or "containing" for applicable legal purposes. Any discussion of documents acts, materials, devices, articles, and the like are included in the specification solely for the purpose of providing a context for the present invention. It is not suggested or represented that any or all these matters form part of the prior art base or were common general knowledge in the field relevant to the present invention.
[0032] In this disclosure, whenever a composition or an element or a group of elements is preceded with the transitional phrase “comprising”, it is understood that we also contemplate the same composition, element, or group of elements with transitional phrases “consisting of”, “consisting”, selected from the group of consisting of, “including”, or “is” preceding the recitation of the composition, element or group of elements and vice versa.
[0033] In one embodiment, the present disclosure includes a tool for fraud detection in financial transactions, including a data collection module configured to collect and preprocess transactional data from various sources. Embodiments also include a node mapping functionality that represents entities as nodes and maps relationships based on transactional data.
[0034] In another embodiment of the present invention, tool also include an interactive visualization layer allowing online or offline exploration and manipulation of nodes and relationships. Embodiments also include a fraud detection mechanism that identifies circular relationships indicative of circular trading or tax evasion. Embodiments also include a reporting engine for generating visual summaries and exporting analysis results.
[0035] In yet another embodiment, the visualization layer including clickable nodes and a drag-and-drop interface.
[0036] In yet another embodiment, the visualization layer includes color-coded filters and customizable node appearance options.
[0037] In yet another embodiment, visualization layer also include relation arrows displaying transaction direction between nodes.
[0038] In yet another embodiment, the color-coded filter engine is configured to highlight specific transaction types, such as high-value transactions or frequent transactions, by changing node colors or link styles in online or offline.
[0039] In some embodiments, the relation arrows configured to show transaction metadata, such as timestamps and transaction amounts, on the arrows to facilitate quick and clear understanding of the transaction flows. In some embodiments, the tool include Intranet compatibility for secure analysis of large, confidential datasets.
[0040] In some embodiments, the node mapping functionality configured to automatically classify nodes based on predefined criteria such as transaction type, entity type, or transaction volume. In some embodiments, the node mapping functionality include a machine learning module trained to detect unusual transaction patterns, thereby identifying potentially fraudulent activities through anomaly detection.
[0041] In some embodiments, the interactive visualization layer includes a zoom and pan feature that allows users to view both macro-level and micro-level transactional relationships within the network. In some embodiments, the interactive visualization layer supports a tooltip display for each node, providing details such as transaction date, amount, and involved entities upon hovering over the node.
[0042] In some embodiments, the reporting engine configured to export visualizations and analytical reports in multiple formats, including PDF, Excel, and image formats, for easy distribution and compliance reporting. In some embodiments, the node mapping functionality further includes a modular design that enables integration with third-party visualization and analytics platforms, such as Tableau, Power BI or QLIK, for enhanced analysis. In some embodiments, the tool configured to anonymize sensitive data fields to comply with data protection regulations, ensuring that confidential information secure within the intranet environment.
[0043] Embodiments of the present disclosure also include a method for detecting fraudulent patterns in financial transactions, including the steps of Collecting and preprocessing data from multiple sources. Embodiments also include Mapping entities as nodes and establishing relationships between nodes based on transactional data. Embodiments also include applying color-coded filters and directional relation indicators. Embodiments also include Identifying circular relationships and providing graphical highlights for potential fraud. Embodiments also include generating and exporting reports based on the visualized data.
[0044] In one embodiment, a tool (100) as shown FIG. 1 describes, according to some embodiments of the present disclosure. In some embodiments, the tool (100) include a data collection module (110) configured to collect and preprocess transactional data from various sources, a node mapping functionality (120) that represents entities as nodes and maps relationships based on transactional data, an interactive visualization layer (130) allowing online or offline exploration and manipulation of nodes and relationships, a fraud detection mechanism (140) that identifies circular relationships indicative of circular trading or tax evasion, and a reporting engine (150) for generating visual summaries and exporting analysis results.
[0045] In one embodiment, the present invention related to a circular flow visualization tool (100), wherein the circular flow visualization tool (100) include a data collection module (110) configured to collect and preprocess transactional data from various sources, a node mapping functionality (120) that represents entities as nodes and maps relationships based on transactional data, an interactive visualization layer (130) allowing online or offline exploration and manipulation of nodes and relationships, a fraud detection mechanism (140) that identifies circular relationships indicative of circular trading or tax evasion, and a reporting engine (150) for generating visual summaries and exporting analysis results.
[0046] In another embodiment, the circular flow visualization tool (100) also includes Intranet compatibility for secure analysis of large, confidential datasets.
[0047] In another embodiment, the circular flow visualization tool (100) is configured to anonymize sensitive data fields to comply with data protection regulations, ensuring that confidential information is secure within the intranet environment.
[0048] In yet another embodiment, the circular flow visualization tool (100 wherein the node mapping functionality is configured to automatically classify nodes based on predefined criteria such as transaction type or entity type.
[0049] In yet another embodiment, the circular flow visualization tool (100 wherein the node mapping functionality further includes a modular design that enables integration with third-party visualization and analytics platforms like Tableau, Power BI or QLIK for enhanced analysis.
[0050] In some embodiments, the node mapping functionality (120) configured to automatically classify nodes based on predefined criteria such as transaction type, entity type, or transaction volume. In some embodiments, the node mapping functionality (120) also include a machine learning module trained to detect unusual transaction patterns, thereby identifying potential fraudulent activities through anomaly detection.
[0051] In yet another embodiment, the node mapping functionality (120) further includes a modular design that enables integration with third-party visualization and analytics platforms like Tableau, Power BI or QLIK for enhanced analysis.
[0052] In yet another embodiment, the interactive visualization layer (130) includes a zoom and pan feature that allows users to view both macro-level and micro-level transactional relationships within the network.
[0053] In yet another embodiment, the interactive visualization layer (130) supports a tooltip display for each node, providing details such as transaction date, amount, and involved entities upon hovering over the node.
[0054] In some embodiments, the node mapping functionality (120) also include a modular design that enables integration with third-party visualization and analytics platforms, such as Tableau, Power BI, or QLIK for enhanced analysis. In some embodiments, the tool (100) configured to anonymize sensitive data fields to comply with data protection regulations, ensuring that confidential information secure within the intranet environment.
[0055] FIG. 2 is a block diagram that further describes the tool (100) from FIG. 1, according to some embodiments of the present disclosure. In some embodiments, the interactive visualization layer (130) includes Clickable nodes (241), a drag-and-drop interface (242), Color-coded filters (243), and customizable node Relation arrows (244) displaying transaction direction between nodes. In some embodiments, the color-coded filter engine configured to highlight specific transaction types, such as high-value transactions or frequent transactions, by changing node colors or link styles in online or offline.
[0056] FIG. 3 is a block diagram that further describes the tool (100) from FIG. 1, according to some embodiments of the present disclosure. In some embodiments, the interactive visualization layer (130) include Clickable nodes (341), a drag-and-drop interface (342), Color-coded filters (343), and Relation arrows (344) displaying transaction direction between nodes. In some embodiments, the relation arrows configured to show transaction metadata, such as timestamps and transaction amounts, on the arrows to facilitate quick and clear understanding of the transaction flows.
[0057] FIG. 4 is a block diagram that further describes the tool (100) from FIG. 1, according to some embodiments of the present disclosure. In some embodiments, the reporting engine (150) configured to export visualizations and analytical reports in multiple formats.
[0058] FIG. 5 is a flowchart that describes a method for detecting fraudulent patterns in financial transactions, according to some embodiments of the present disclosure. In some embodiments, at 510, the method includes collecting and preprocessing data from multiple sources. At 520, the method includes Applying color-coded filters and directional relation indicators. At 530, the method includes identifying circular relationships and providing graphical highlights for potential fraud. The steps of, the method includes 510 to 530. Mapping entities as nodes and establishing relationships between nodes based on transactional data. Generating and exporting reports based on the visualized data.
[0059] FIG. 6 is a flowchart that describes a stepwise method for detecting fraudulent patterns in financial transactions, according to some embodiments of the present disclosure.
[0060] The following examples further describe and demonstrate embodiments within the scope of the present invention. The examples are given solely for the purpose of illustration and not to be construed as limitations of the present invention, as many variations are possible without departing from the spirit and scope of the invention. All embodiments apparent to a process in the art are deemed to fall within the scope of the present invention The general method of preparation of compounds described in scheme is provided below.
EXAMPLES
[0061] Example 1: As illustrated in FIG. 7, a pattern of circular trading is observed among family members, including Tom, Rhea, Mike, Charles, Shelly, and back to Tom. This example highlights how circular trading occurs within a closely-knit group to evade applicable taxes and fabricate a chain of fictitious transactions. The interconnected nature of these trades creates a loop that is challenging to identify without specialized analysis tools.
[0062] Example 2: As illustrated in FIG. 8, a more complex scenario where multiple circular trading patterns are identified. In this example, multiple trades happening which would have not been captured unless we saw the visual representation of it where one family Ray, Smith and Kane are doing their own circular trading. Additionally, the visual representation reveals the presence of four groups engaging in circular trading as shown in FIG. 8. This embodiment illustrates the interplay of smaller trading cycles within a broader network, emphasizing how such practices can involve multiple participants across different groups.
[0063] Example 3: FIG. 9 illustrated, another instance of circular trading is observed involving Smith, Kane, and Ray in a single cycle. Beyond this, the embodiment depicts three distinct groups engaged in circular trading. These groups are linked through two common intermediaries, Mike and John, who act as connectors, facilitating trade between the otherwise separate circular trading networks. This example underscores the complexity of identifying and addressing circular trading when it spans multiple groups with shared participants.
[0064] While considerable emphasis has been placed herein on the specific features of the preferred embodiment, it will be appreciated that many additional features can be added and that many changes can be made in the preferred embodiment without departing from the principles of the disclosure. These and other changes in the preferred embodiment of the disclosure will be apparent to those skilled in the art from the disclosure herein, whereby it is to be distinctly understood that the foregoing descriptive matter is to be interpreted merely as illustrative of the disclosure and not as a limitation.
, Claims:WE CLAIM:
1. A circular flow visualization tool (100), comprising:
a. a data collection module (110) configured to collect and preprocess transactional data from various sources;
b. a node mapping functionality (120) that represents entities as nodes and maps relationships based on transactional data;
c. an interactive visualization layer (130) allowing online or offline exploration and manipulation of nodes and relationships;
d. a fraud detection mechanism (140) that identifies circular relationships indicative of circular trading or tax evasion; and
e. a reporting engine (150) for generating visual summaries and exporting analysis results.
2. The circular flow visualization tool (100) of claim 1, further comprising:
a. intranet compatibility for secure analysis of large, confidential datasets.
3. The circular flow visualization tool (100) of claim 1, wherein the tool is configured to anonymize sensitive data fields to comply with data protection regulations, ensuring that confidential information is secure within the intranet environment.
4. The circular flow visualization tool (100) of claim 1, wherein the node mapping functionality (120) is configured to automatically classify nodes based on predefined criteria such as transaction type or entity type.
5. The circular flow visualization tool (100) of claim 1, wherein the node mapping functionality (120) further includes a modular design that enables integration with third-party visualization and analytics platforms like Tableau, Power BI or QLIK for enhanced analysis.
6. The circular flow visualization tool (100) of claim 1, wherein the interactive visualization layer (130) includes a zoom and pan feature that allows users to view both macro-level and micro-level transactional relationships within the network.
7. The circular flow visualization tool (100) of claim 1, wherein the interactive visualization layer (130) supports a tooltip display for each node, providing details such as transaction date, amount, and involved entities upon hovering over the node.
8. The circular flow visualization tool (100) of claim 1, wherein the interactive visualization layer (130) comprising:
a. a clickable node (241);
b. a drag-and-drop interface (242);
c. a color-coded filters (243) with customizable node appearance options; and
d. a relation arrows (244) displaying transaction direction between nodes.
9. The interactive visualization layer (130) of claim 8, wherein the color-coded filter engine (243) is configured to highlight specific transaction types.
10. The interactive visualization layer (130) of claim 8, wherein the relation arrows (244) are configured to show transaction metadata, such as timestamps and transaction amounts, on the arrows to facilitate quick and clear understanding of the transaction flows.
11. A method for detecting fraudulent patterns in financial transactions, comprising the steps of:
a. Data Input: receiving transactional data through a database or a direct input interface, including Tableau;
b. Node Mapping: mapping entities as nodes and establishing relationships between the nodes based on the transactional data;
c. Customization & Filtering: applying customizable filters to focus on specific relationships, adjust node appearance, and highlight directional paths between entities to trace transaction flows;
d. Interactive Features: providing interactive features, including clickable nodes, drag-and-drop functionality, and directional arrows, for online or offline exploration of the network;
e. Fraud Detection: identifying fraudulent patterns, including circular trading or tax evasion, by detecting circular relationships among the nodes; and
f. Visualization & Reporting: generating visual summaries and reports based on filtered data, including the export of notifications.