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OPTIMIZING FINANCIAL SECURITY: CLOUD AI AND MACHINE LEARNING IN RISK MANAGEMENT AND FRAUD DETECTION
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Abstract
Information
Inventors
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Specification
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ORDINARY APPLICATION
Published
Filed on 23 November 2024
Abstract
OPTIMIZING FINANCIAL SECURITY: CLOUD AI AND MACHINE LEARNING IN RISK MANAGEMENT AND FRAUD DETECTION The method for the development of an organizations can detect and prevent fraud more quickly and accurately thanks to real-time data analysis powered by contemporary technologies. Furthermore, the emergence of big data has enabled fraud detection systems to recognize intricate patterns, opening the door to increasingly complex fraud detection tactics. The significance of real-time data analysis, the function of big data in fraud detection, and successful data utilization case studies in fraud detection systems. Data science now has advanced methods to tackle these issues thanks to artificial intelligence (AI), which has become a revolutionary force. This study explores the use of artificial intelligence (AI) in data science for financial services, with a particular emphasis on investment strategies, risk management, and fraud detection. Strategic planning and regulatory frameworks are crucial for tackling both technological and operational challenges, and AI/ML has enormous potential to improve fraud detection.
Patent Information
Application ID | 202441091383 |
Invention Field | COMPUTER SCIENCE |
Date of Application | 23/11/2024 |
Publication Number | 49/2024 |
Inventors
Name | Address | Country | Nationality |
---|---|---|---|
Dinesh G | Assistant Professor, Department of Computational Intelligence, School of Computing, SRM Insititute of Science and Technology, Kattankulathur- 603203, Chengalpatu, Tamilnadu, India. | India | India |
Veernapu Sudheer Kumar | Assistant Professor, Department of Mechanical Engineering, V R Siddhartha Engineering College, Vijayawada, Krishna, Andhra Pradesh, India. | India | India |
Brindha N | Assistant Professor, Department of Commerce and Management, Presidency College (Autonomous) Kempapura, Bengaluru- 560024, Karnataka, India. | India | India |
M Lakshmi Priya | Assistant Professor, M.Tech Computer Science and Engineering, Erode Sengunthar Engineering College (Autonomous), Perundurai, Thudupathi, Erode- 638 057, Tamilnadu, India. | India | India |
Dr Amit Chauhan | Associate Professor & Head of Department, Department of Forensic Science, Parul Institute of Applied Sciences, Parul University, Vadodara, Gujarat, India- 391760 | India | India |
Dr S P. Karuppiah | Professor, Department of MBA, St.Joseph's College of Engineering, Chennai, Tamilnadu, India. | India | India |
V Sandhya | Assistant Professor, Department of Mathematics, SNS College of Technology, Coimbatore 641035, Tamilnadu, India. | India | India |
R. Kannan | Assistant Professor, Department of Commerce with Computer Application, Excel College for Commerce and Science, Komarapalyam, Namakkal, Tamilnadu, India. | India | India |
K. Perumal | Assistant Professor, Department of CSE, Annamacharya Institute of Technology & Science (Autonomous), Tiripati- 517520, Andhra Pradesh, India. | India | India |
Dr S. Nagendra Prabhu | Assistant Professor, Department of Computational Intelligence, School of Computing, SRM Institute of Science & Technology Kattankulathur, Chennai- 603203,Tamilnadu, India. | India | India |
Dr D. B. Jagannadha Rao | Associate Professor, Department of Data Science, Malla Reddy University, Dulapally, Hyderabad, Telangana- 500100,India. | India | India |
Dr Jyoti Prasad Patra | Principal, Nigam Institute of Engineering and Technology, Govind Pur, Cuttack, Odisha, India- 754006 | India | India |
Applicants
Name | Address | Country | Nationality |
---|---|---|---|
Dinesh G | Assistant Professor, Department of Computational Intelligence, School of Computing, SRM Insititute of Science and Technology, Kattankulathur- 603203, Chengalpatu, Tamilnadu, India. | India | India |
Veernapu Sudheer Kumar | Assistant Professor, Department of Mechanical Engineering, V R Siddhartha Engineering College, Vijayawada, Krishna, Andhra Pradesh, India. | India | India |
Brindha N | Assistant Professor, Department of Commerce and Management, Presidency College (Autonomous) Kempapura, Bengaluru- 560024, Karnataka, India. | India | India |
M Lakshmi Priya | Assistant Professor, M.Tech Computer Science and Engineering, Erode Sengunthar Engineering College (Autonomous), Perundurai, Thudupathi, Erode- 638 057, Tamilnadu, India. | India | India |
Dr Amit Chauhan | Associate Professor & Head of Department, Department of Forensic Science, Parul Institute of Applied Sciences, Parul University, Vadodara, Gujarat, India- 391760 | India | India |
Dr S P. Karuppiah | Professor, Department of MBA, St.Joseph's College of Engineering, Chennai, Tamilnadu, India. | India | India |
V Sandhya | Assistant Professor, Department of Mathematics, SNS College of Technology, Coimbatore 641035, Tamilnadu, India. | India | India |
R. Kannan | Assistant Professor, Department of Commerce with Computer Application, Excel College for Commerce and Science, Komarapalyam, Namakkal, Tamilnadu, India. | India | India |
K. Perumal | Assistant Professor, Department of CSE, Annamacharya Institute of Technology & Science (Autonomous), Tiripati- 517520, Andhra Pradesh, India. | India | India |
Dr S. Nagendra Prabhu | Assistant Professor, Department of Computational Intelligence, School of Computing, SRM Institute of Science & Technology Kattankulathur, Chennai- 603203,Tamilnadu, India. | India | India |
Dr D. B. Jagannadha Rao | Associate Professor, Department of Data Science, Malla Reddy University, Dulapally, Hyderabad, Telangana- 500100,India. | India | India |
Dr Jyoti Prasad Patra | Principal, Nigam Institute of Engineering and Technology, Govind Pur, Cuttack, Odisha, India- 754006 | India | India |
Specification
Description:OPTIMIZING FINANCIAL SECURITY: CLOUD AI AND MACHINE LEARNING IN RISK MANAGEMENT AND FRAUD DETECTION
Technical Field
[0001] The embodiments herein generally relate to a method for optimizing financial security: cloud AI and machine learning in risk management and fraud detection.
Description of the Related Art
[0002] The Fraud-related activities have significantly increased as a result of the growth of digital transactions, mobile banking, and e-commerce. To safeguard clients and stop financial losses, financial institutions are under pressure to identify fraudulent transactions instantly. The rule-based methods used by traditional fraud detection systems are becoming less and less effective at detecting intricate and dynamic fraud patterns. In the financial services sector, risk management is essential for maintaining financial stability and reducing possible losses. AI provides data science with strong instruments for thorough risk analysis. By using methods like regression analysis and time series forecasting, predictive analytics helps organizations foresee possible risks by using past data and market trends. Corporate finance fraud has a significant impact on both individual companies and the overall economy. Financial loss, harm to a company's reputation, and legal ramifications are among the immediate effects on businesses. Businesses, especially small and medium-sized ones, which might lack the resources to recover from widespread fraud, can be severely damaged by these losses. Businesses also have to pay more for anti-fraud measures like putting in place sophisticated security systems and adhering to more stringent rules.
[0003] Real-time machine learning models are being used by financial institutions. These models have the ability to instantly process massive amounts of transaction data, spot questionable trends, and anticipate fraud before it happens. Machine learning models, in contrast to traditional systems, are able to continuously adjust to new fraud techniques and learn from past fraud data. Financial security is being revolutionized by the ability to identify fraud in real time while reducing false positives. AI provides novel methods for producing alpha, or excess return above the market benchmark. In order to find lucrative trading opportunities, algorithmic trading-which is driven by machine learning models-analyses news feeds, sentiment on social media, and historical market data. These algorithms are able to make trades quickly, taking advantage of short-lived market inefficiencies that human investors might overlook. Additionally, it leads to regulatory crackdowns, which raises the cost of compliance for companies in all sectors. In conclusion, fraud in corporate finance is a serious problem that includes a variety of dishonest practices, such as insider trading, identity theft, and cyberattacks. Its effects on the economy and businesses emphasize how important it is to have strong fraud detection and prevention systems in place to safeguard financial integrity.
[0004] The supervised learning, unsupervised learning, and reinforcement learning are some of the machine learning techniques used in fraud detection. The technical difficulties of implementing real-time models at scale, like preserving low system latency and protecting data privacy, will also be covered. This paper also discusses future directions for fraud detection, such as integrating blockchain technology and Explainable AI (XAI) to improve security and transparency. Techniques for Natural Language Processing (NLP) are essential for drawing conclusions from unstructured data sources such as press releases, social media posts, and financial news articles.
SUMMARY
[0001] In view of the foregoing, an embodiment herein provides a method for optimizing financial security: cloud ai and machine learning in risk management and fraud detection. In some embodiments, wherein large volumes of transaction data are difficult for traditional fraud detection systems to process in real time. A bad customer experience results from transactions being frequently delayed while systems examine the data. Furthermore, the harm might already be done when a fraudulent transaction is reported. In order to solve this problem, real-time fraud detection processes data as transactions take place, enabling immediate analysis and action. Data quality is still a major concern. Large amounts of precise, clean data are needed to train AI models. Biases and inconsistencies in the data can result in erroneous models and poor decision-making. Furthermore, AI models' explainability-especially that of deep learning architectures-can be ambiguous. These models' intricate internal mechanisms make it challenging to comprehend how they reach their conclusions, which raises questions regarding transparency and fairness. Effective risk management, particularly in the corporate finance industry, depends heavily on fraud detection. Strong detection systems are more important than ever as companies continue to face rising financial fraud risks. The purpose of risk management strategies is to recognize, evaluate, and reduce risks that might endanger a company.
[0002] In some embodiments, wherein a disproportionate number of false positives are frequently produced by rule-based systems. Although many fraud cases can be detected by these systems, they often mark legitimate transactions as suspicious, which impedes customer service and causes annoyance. By discovering increasingly intricate patterns in transaction data, machine learning models-especially those that employ unsupervised techniques-can aid in the reduction of false positives. Machine learning-powered fraud detection systems have greatly decreased chargebacks and fraudulent transactions. Institutions have been able to enhance loan approval procedures, optimize capital allocation, and more successfully withstand market downturns thanks to AI-driven risk management solutions. In the field of investment management, some hedge funds and asset management companies have seen higher returns thanks to AI-powered algorithms. Businesses put themselves at increased risk of irreparable financial harm if fraud detection is not done well. In addition, regulatory agencies are enforcing stricter compliance standards, and penalties and fines may be incurred for failing to identify and handle fraud. As a result, incorporating fraud detection into risk management involves more than just safeguarding financial assets; it also involves maintaining stakeholder trust and guaranteeing regulatory compliance.
[0003] In some embodiments, wherein static systems find it challenging to stay up to date with the ever-evolving tactics of fraudsters. Financial institutions have to update their rule sets frequently due to the emergence of new fraud types, which can be inefficient and time-consuming. Because they can adjust to novel fraud patterns without requiring human intervention, real-time machine learning models provide a dynamic solution. The potential for financial innovation is further increased by the combination of AI with other cutting-edge technologies like blockchain and quantum computing. However, the responsible and long-term implementation of AI in financial services will continue to depend heavily on resolving ethical issues, protecting data privacy, and promoting transparency. Conventional techniques for detecting fraud, like rule-based systems and manual audits, mainly depend on human oversight and predetermined patterns. These techniques are frequently reactive, which means they only identify fraud after it has already happened, and they might not be effective in identifying intricate or dynamic fraud schemes. By analyzing massive datasets in real-time, finding patterns, and learning from anomalies without the need for explicit programming instructions, AI and ML, on the other hand, make proactive fraud detection possible.
[0004] These and other aspects of the embodiments herein will be better appreciated and understood when considered in conjunction with the following description and the accompanying drawings. It should be understood, however, that the following descriptions, while indicating preferred embodiments and numerous specific details thereof, are given by way of illustration and not of limitation. Many changes and modifications may be made within the scope of the embodiments herein without departing from the spirit thereof, and the embodiments herein include all such modifications.
BRIEF DESCRIPTION OF THE DRAWINGS
[0001] The embodiments herein will be better understood from the following detailed description with reference to the drawings, in which:
[0002] FIG. 1 illustrates a method for optimizing financial security: cloud ai and machine learning in risk management and fraud detection according to an embodiment herein; and
[0003] FIG. 2 illustrates a method for real-time machine learning workflow for fraud detection according to an embodiment herein.
DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS
[0001] The embodiments herein and the various features and advantageous details thereof are explained more fully with reference to the non-limiting embodiments that are illustrated in the accompanying drawings and detailed in the following description. Descriptions of well-known components and processing techniques are omitted so as to not unnecessarily obscure the embodiments herein. The examples used herein are intended merely to facilitate an understanding of ways in which the embodiments herein may be practiced and to further enable those of skill in the art to practice the embodiments herein. Accordingly, the examples should not be construed as limiting the scope of the embodiments herein.
[0002] FIG. 1 FIG. 1 illustrates a method for optimizing financial security: cloud ai and machine learning in risk management and fraud detection according to an embodiment herein. In some embodiments, financial institutions can process massive datasets instantly and spot fraudulent transactions as they occur thanks to real-time machine learning. The process of feeding transaction data streams into real-time machine learning models-which examine the data and identify questionable activity-will be covered in this section. Financial institutions can stay ahead of fraudsters by handling large volumes of data in milliseconds. Algorithms for supervised learning are trained on labeled data, in which every data point has a predetermined label that indicates whether the transaction is fraudulent or legitimate. The algorithm gains knowledge of the fundamental connections between different data features, such as transaction amount, location, time, and the associated labels, during the training phase. After being trained, the model can use the learned patterns to evaluate fresh, unlabeled data and forecast the possibility that a transaction is fraudulent. Models are trained on labeled datasets in supervised learning, where the results are predetermined. When given new, unseen data, the model becomes very good at predicting fraud because it learns to recognize patterns and correlations between input features and the result. Decision trees are a common supervised learning method in fraud detection that generates a flowchart of choices and their potential outcomes. Multiple decision trees are used in random forests, which aggregate the results of each tree to further improve accuracy.
[0003] In some embodiments, for fraud classification tasks, algorithms like Random Forests, Gradient Boosting Machines, and Logistic Regression are frequently employed. To differentiate between authentic and fraudulent transactions, these models are trained using historical data. In a high-dimensional feature space, SVMs seek to construct a hyperplane that best divides data points that represent authentic and fraudulent transactions. The model can successfully categorize new transactions as either fraudulent or legitimate thanks to this hyperplane. SVMs are renowned for their effectiveness in managing high-dimensional data and for being less vulnerable to the overfitting issue that can afflict other algorithms. Unsupervised learning, in contrast to supervised learning, uses unlabeled data, so the results are unpredictable. This is especially helpful in identifying fraud patterns that were previously unknown. Transactions are frequently grouped into clusters of normal and anomalous behavior in unsupervised learning using clustering algorithms like K-means and anomaly detection models. Unusual data points could be a sign of possible fraud that would be hard to find with conventional rule-based techniques.
[0004] In some embodiments, when there is a lack of labeled fraud data, algorithms such as isolation forests and auto encoders are used. Outliers or anomalous behavior that might point to fraud are picked up by these models. Institutions can identify fraud as transactions take place thanks to real-time machine learning models, which are made to process data streams in milliseconds. Algorithms for unsupervised learning work with unlabeled data, which is data that has no predetermined labels. These algorithms are very good at finding anomalies and hidden patterns in the data. Unsupervised learning methods, especially anomaly detection algorithms, are essential for detecting new and unexpected fraudulent schemes in the context of fraud detection. A subset of machine learning models that draw inspiration from the architecture of the human brain are neural networks. Because they can model complex, non-linear relationships between input features, they are especially helpful for complex fraud detection tasks. Layers of interconnected nodes, or neurons, make up a neural network. Each layer learns to represent the data in progressively more abstract ways. Neural networks can automatically detect fraud patterns that may be invisible to simpler models when trained on large datasets.
[0005] FIG. 2 illustrates a method for real-time machine learning workflow for fraud detection according to an embodiment herein. In some embodiments, one of the most popular supervised learning models for fraud detection is the Random Forest. Using subsets of the training data, this ensemble approach constructs several decision trees, then aggregates the results to generate a final prediction. Because Random Forests can process large datasets accurately and quickly, they are very effective in real-time fraud detection. These models learn to categorize future transactions as either legitimate or fraudulent based on patterns they have learned from training on historical fraud data. Financial institutions can create comprehensive fraud detection systems that are not only skilled at spotting well-established fraud patterns but also flexible enough to adjust to changing threats by combining supervised and unsupervised learning techniques. While unsupervised learning techniques serve as a safety net, identifying anomalies that might elude conventional rule-based systems or supervised models trained on historical examples, supervised learning models offer a strong foundation for fraud detection by learning from historical data. In fraud detection, natural language processing, or NLP, has grown in significance, especially when it comes to analyzing unstructured data like emails, transaction descriptions, and communication logs. Because NLP allows machines to comprehend, interpret, and react to human language, it is a vital tool for spotting fraudulent activity in documents and communications.
[0006] In some embodiments, without the need for labeled fraud examples, anomalies in data can be found using autoencoders, a kind of unsupervised learning model. Autoencoders are trained to compress and reconstruct authentic transaction data in order to detect fraud. The autoencoder marks a new transaction as possibly fraudulent if it differs noticeably from the learned normal pattern. This method works especially well for spotting new fraud trends that may not be visible in the training set. CNNs are excellent at processing grid-like and image data. CNNs can be used to examine transaction-related images, like invoices, screenshots, and receipts, in the context of fraud detection. Through feature extraction from these images (such as logos, text, and inconsistent layout), CNNs are able to spot minute irregularities that could be signs of fraud. For example, a CNN may identify discrepancies in a receipt's font or logo that could indicate a potential attempt at forgery. Text is transformed into numerical vectors by NLP models like Bag-of-Words or word embeddings, which machine learning models can use to identify irregularities in communication patterns. By examining not only the data but also the environment in which financial transactions take place, this technology makes it possible to detect fraud more precisely.
[0007] In some embodiments, the ability of Recurrent Neural Networks (RNNs) to process sequential data effectively makes them ideal for identifying fraudulent patterns that emerge over time. RNNs are capable of analyzing transaction behavior sequences, such as multiple purchases made in a short period of time or anomalous geographic shifts, in real-time fraud detection. RNNs can determine whether a sequence of transactions is likely to be fraudulent by identifying patterns in these sequences. In the financial services sector, risk management is the cornerstone of financial stability. It includes an advanced set of procedures intended to proactively detect, evaluate, and lessen possible monetary losses. These losses can have a wide range of causes, which puts institutions' financial stability and the efficient operation of the financial system at large at serious risk. Organizations can detect and prevent fraud more quickly and accurately thanks to real-time data analysis powered by contemporary technologies. Furthermore, the emergence of big data has enabled fraud detection systems to recognize intricate patterns, opening the door to increasingly complex fraud detection tactics.
, Claims:1. A method for optimizing financial security: cloud ai and machine learning in risk management and fraud detection, wherein the method comprises;
cloud-based AI and machine learning systems can process vast amounts of transactional data in real time, identifying fraud patterns with greater accuracy and reducing false positives;
predicting and prevent potential financial threats by analyzing historical and current data, enabling institutions to take proactive measures.
using of cloud infrastructure allows ai systems to scale effortlessly, ensuring rapid analysis of large datasets and minimizing response times in critical scenarios;
leveraging cloud-based ai, organizations reduce the need for on-premise infrastructure, cutting operational costs while accessing advanced fraud detection and risk management tools;
having continuously updated and refined based on new data, improving their ability to detect emerging fraud tactics and evolving risks;
offering real-time insights, enabling financial institutions to make timely decisions and mitigate risks effectively; and
allowing financial institutions to monitor and manage risks globally, ensuring consistent fraud detection and security measures across multiple regions.
Documents
Name | Date |
---|---|
202441091383-COMPLETE SPECIFICATION [23-11-2024(online)].pdf | 23/11/2024 |
202441091383-DECLARATION OF INVENTORSHIP (FORM 5) [23-11-2024(online)].pdf | 23/11/2024 |
202441091383-DRAWINGS [23-11-2024(online)].pdf | 23/11/2024 |
202441091383-FORM 1 [23-11-2024(online)].pdf | 23/11/2024 |
202441091383-FORM-9 [23-11-2024(online)].pdf | 23/11/2024 |
202441091383-POWER OF AUTHORITY [23-11-2024(online)].pdf | 23/11/2024 |
202441091383-PROOF OF RIGHT [23-11-2024(online)].pdf | 23/11/2024 |
202441091383-REQUEST FOR EARLY PUBLICATION(FORM-9) [23-11-2024(online)].pdf | 23/11/2024 |
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