SYSTEM FOR DETECTING LEGITIMATE AND ILLICIT TRANSACTIONS

Abstract

ABSTRACT A system 100 for detecting legitimate and illicit transactions, comprising a data processing module 102 for cleaning and structuring transaction data, a feature extraction module 104 for identifying transaction-related attributes, a classification module 106 with machine learning protocols for detecting suspicious transactions and an analytics module 108 for visualizing transaction trends and anomalies. <>

Specification

Description:
FIELD OF THE DISCLOSURE
[0001] This invention generally relates to transaction analysis using machine learning, specifically a system for detecting legitimate and illicit financial transactions.
BACKGROUND

[0002] The subject matter discussed in the background section should not be assumed to be prior art merely as a result of its mention in the background section. Similarly, a problem mentioned in the background section or associated with the subject matter of the background section should not be assumed to have been previously recognized in the prior art. The subject matter in the background section merely represents different approaches, which in and of themselves may also correspond to implementations of the claimed technology.
[0003] Current techniques typically rely on predefined rules and are limited by static parameters that lack adaptability to emerging trends. Machine learning presents a dynamic solution, enabling more accurate detection by identifying unseen patterns and relationships within tr , Claims:1. A system 100 for detecting legitimate and illicit transactions, comprising:
a data processing module 102 for cleaning and structuring transaction data,
a feature extraction module 104 for identifying transaction-related attributes,
a classification module 106 with machine learning protocols for detecting suspicious transactions, and
an analytics module 108 for visualizing transaction trends and anomalies.
2. The system 100 as claimed in claim 1, wherein the data processing module 102 handles missing values, standardizes data formats, and normalizes transaction amounts.
3. The system 100 as claimed in claim 1, wherein the classification module 106 employs Random Forest and Logistic Regression algorithms to classify transactions.
4. The system 100 as claimed in claim 1, wherein the analytics module 108 visualizes illicit transaction trends using graphs, heat maps, and flowcharts.

Documents