ADAPTIVE DEEP LEARNING SYSTEM FOR FINANCIAL FRAUD DETECTION

Abstract

The present invention provides an adaptive deep learning system for detecting financial fraud in real-time. By leveraging deep learning models, the system continuously learns from transaction data and updates its fraud detection capabilities without manual intervention. It collects and processes various types of transaction data, including payment method, transaction amount, and user behavior, to identify both known and emerging fraud patterns. The system employs an adaptive learning engine to refine the model based on new data, reducing false positives and improving detection accuracy over time. This real-time, scalable solution offers enhanced fraud prevention for financial institutions and businesses across multiple payment channels.

Specification

Description:In the following description, for the purposes of explanation, various specific details are set forth in order to provide a thorough understanding of embodiments of the present disclosure. It will be apparent, however, that embodiments of the present disclosure may be practiced without these specific details. Several features described hereafter can each be used independently of one another or with any combination of other features. An individual feature may not address all of the problems discussed above or might address only some of the problems discussed above. Some of the problems discussed above might not be fully addressed by any of the features described herein.

The ensuing description provides exemplary embodiments only and is not intended to limit the scope, applicability, or configuration of the disclosure. Rather, the ensuing description of the exemplary embodiments will provide those skilled in the art with an enabling description for implementing an exemplary embodiment. It should be understood that various changes may be made in the function and arrangement of elements without departing from the spirit and scope of the disclosure as set forth.

Specific details are given in the following description to provide a thorough understanding of the embodiments. However, it will be understood by one of ordinary skill in the art that the embodiments may be practiced without these specific details. For example, circuits, systems, networks, processes, and other components may be shown as components in block diagram form in order not to obscure the embodiments in unnecessary detail. In other instances, well-known circuits, processes, algorithms, structures, and techniques may be shown without unnecessary detail to avoid obscuring the embodiments.

Also, it is noted that individual embodiments may be described as a process that is depicted as a flowchart, a flow diagram, a data flow diagram, a structure diagram, or a block diagram. Although a flowchart may describe the operations as a sequential process, many of the operations can be performed in parallel or concurrently. In addition, the order of the operations may be re-arranged. A process is terminated when its operations are completed but could have additional steps not included in a figure. A process may correspond to a method, a function, a procedure, a subroutine, a subprogram, etc. When a process corresponds to a function, its termination can correspond to a return of the function to the calling function or the main function.

The word "exemplary" and/or "demonstrative" is used herein to mean serving as an example, instance, or illustration. For the avoidance of doubt, the subject matter disclosed herein is not limited by such examples. In addition, any aspect or design described herein as "exemplary" and/or "demonstrative" is not necessarily to be construed as preferred or advantageous over other aspects or designs, nor is it meant to preclude equivalent exemplary structures and techniques known to those of ordinary skill in the art. Furthermore, to the extent that the terms "includes," "has," "contains," and other similar words are used in either the detailed description or the claims, such terms are intended to be inclusive in a manner similar to the term "comprising" as an open transition word without precluding any additional or other elements.

Reference throughout this specification to "one embodiment" or "an embodiment" or "an instance" or "one instance" means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present disclosure. Thus, the appearances of the phrases "in one embodiment" or "in an embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. As used herein, the singular forms "a", "an", and "the" are intended to include the plural forms as well, unless the context indicates otherwise. It will be further understood that the terms "comprises" and/or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items.

The adaptive deep learning system for financial fraud detection is designed to address the shortcomings of traditional fraud detection methods by utilizing deep learning techniques that allow the system to dynamically adapt to evolving fraud patterns. The system continuously learns from incoming transaction data and updates its detection models to improve accuracy over time, without requiring manual intervention.

The system comprises several core components: a data acquisition module, a preprocessing unit, a deep learning model, an adaptive learning engine, and a fraud detection engine. The data acquisition module is responsible for gathering relevant transaction data from multiple sources, including credit card transactions, bank transfers, mobile payments, and online purchases. This data typically includes features such as transaction amount, transaction time, payment method, user profile data, geographic location, device ID, and other behavioral data points that are relevant to detecting fraud.

Once collected, the data is passed to the preprocessing unit, which cleans, normalizes, and transforms the raw data into a format suitable for the deep learning model. This unit ensures that the data is consistent, removing anomalies or errors, and also performs feature engineering to generate derived features that may enhance the model's ability to detect fraud. For instance, time-based features or the user's transaction history might be computed to capture temporal patterns indicative of fraudulent activity.

The deep learning model forms the heart of the system. It typically employs architectures such as Convolutional Neural Networks (CNNs) or Recurrent Neural Networks (RNNs), depending on the nature of the data. CNNs may be used if the data has spatial relationships (e.g., images or structured data), while RNNs are better suited for sequence-based data, such as time-series data related to transactions. The model is trained on historical transaction data that includes labeled examples of fraudulent and non-fraudulent transactions. This training allows the system to identify complex patterns and anomalies that might indicate fraud.

A key feature of the system is the adaptive learning engine, which enables continuous updating of the deep learning model. This engine ensures that the model is capable of evolving with new fraud techniques and patterns. The engine can use methods such as online learning, where the model is updated incrementally as new data becomes available, or reinforcement learning, where the system refines its fraud detection decisions based on feedback from its actions (e.g., whether a flagged transaction was indeed fraudulent or not).

The fraud detection engine utilizes the trained and continuously updated deep learning model to evaluate incoming transactions in real-time. Each transaction is processed to calculate a fraud probability score, and based on a predefined threshold, the system either flags the transaction for manual review or blocks it automatically to prevent any potential fraud from taking place. The system can also incorporate feedback loops to improve model accuracy over time, by using both user feedback and post-transaction analysis.

In a first embodiment of the invention, the system is implemented for use by a bank to detect fraudulent transactions across various payment channels such as credit card payments, online bank transfers, and ATM withdrawals. The bank's fraud detection system is integrated with the adaptive deep learning model, where data from these payment channels is continuously fed into the system.

Each time a customer makes a transaction, the system evaluates it against historical transaction patterns, customer behavior, and real-time data. The system's deep learning model uses an RNN architecture to analyze temporal patterns, such as transaction frequency, location-based anomalies, and time-of-day activity. The adaptive learning engine updates the model daily based on new transaction data, ensuring that the system can detect newly emerging fraud tactics, such as synthetic identity fraud or card-not-present fraud. Transactions deemed suspicious are flagged, and a fraud analyst can investigate them further. If the transaction is found to be legitimate, the model learns from this feedback, which is used to improve its fraud detection accuracy.

In a second embodiment, the invention is applied to an e-commerce platform that processes large volumes of digital transactions. Here, the system uses a CNN-based architecture to process transaction data in the form of structured logs from user accounts, including items purchased, session duration, browsing patterns, and the device being used.

The e-commerce platform integrates the deep learning system to evaluate the risk of fraud at the point of sale, with the system flagging potentially fraudulent purchases based on unusual behavior patterns. For example, if a customer places an unusually large order shortly after signing up or if an order comes from a geographically distant location compared to the customer's typical behavior, the system automatically assigns a higher fraud risk score to that transaction. The adaptive learning engine continues to refine its fraud detection models with every transaction and adjusts to new fraud tactics, such as account takeover or payment gateway fraud. For transactions flagged as high risk, the system either triggers an automatic verification process or temporarily blocks the transaction until further investigation is conducted. This reduces the risk of financial loss and improves the platform's trustworthiness and security.

While considerable emphasis has been placed herein on the preferred embodiments, it will be appreciated that many embodiments can be made and that many changes can be made in the preferred embodiments without departing from the principles of the invention. These and other changes in the preferred embodiments of the invention 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 to be implemented merely as illustrative of the invention and not as limitation. , Claims:1.A method for detecting financial fraud in real-time, comprising:
acquiring transaction data including one or more of transaction amount, user profile, transaction time, and payment method;
pre-processing the acquired data into a format suitable for deep learning analysis;
applying a deep learning model to the pre-processed data to determine a likelihood of fraud;
continuously updating the deep learning model using new transaction data and fraud feedback; and
generating an alert or taking an action to block the transaction based on the likelihood of fraud.

2.The method of claim 1, wherein the deep learning model is selected from the group consisting of a Convolutional Neural Network (CNN) and a Recurrent Neural Network (RNN).

3.The method of claim 1, wherein the deep learning model is adapted using one or more of online learning, transfer learning, and reinforcement learning to update the model in real-time based on new transaction data.

Documents