AI-Driven Fraud Detection in Banking: Enhancing Transaction Security (original) (raw)
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nternational journal of advanced research in computer and communication engineering, 2023
Financial crimes pose a significant threat to the stability and integrity of financial systems, necessitating advanced technologies to mitigate risks. It can be challenging to identify financial cybercrime-related activity because, for instance, an extremely restrictive algorithm might prevent any suspicious activity that would impede legitimate customer transactions. Financial institutions face challenges beyond just navigating and identifying legitimate illicit transactions. Customers and regulators are increasingly demanding transparency, fairness, and privacy, which places special restrictions on the use of AI techniques to identify fraud-related activity. This research paper aims to investigate the pivotal role of Artificial Intelligence (AI) in the early detection and prevention of financial frauds within the global banking sector. The study delves into the background of financial crimes, reviews relevant literature, explores AI technologies used in intelligent banking, provides recommendations for enhanced prevention strategies, and concludes with the potential impact of AI on global banking.
ROLE OF ARTIFICIAL INTELLIGENCE IN COMBATING CYBER THREATS IN BANKING
International Engineering Journal For Research & Development, 2019
With the advances in information technology, various cyberspaces are used by criminals to enhance cybercrime. To mitigate this cybercrime and cyber threats, the bank and financial industry try to implement artificial intelligence. Various opportunities are provided by AI techniques, which help the banking sector to increase prosperity and growth. To maintain trust in artificial intelligence, it is important to maintain transparency and explain ability. Information about customer's behavior and interest is provided by artificial intelligence techniques. Robo-advice is an automated platform that is maintained by AI. Artificial Intelligence is also involved in protecting personal data. Proper design provided by AI towards the banking sector, by which they are able to identify fraud in transactions. AI directly linked with the domain of cyber security. Various kinds of cybercrimes are prevented and identified by AI-based fraud detection systems. However, implementation and maintenance of artificial intelligence consist of the high cost. Along with this unemployment rate is increased by AI techniques.
Deleted Journal, 2024
AI is anticipated to enhance competitive advantages for financial organisations by increasing efficiency through cost reduction and productivity improvement, as well as by enhancing the quality of services and goods provided to consumers. AI applications in finance have the potential to create or exacerbate financial and non-financial risks, which could result in consumer and investor protection concerns like biassed, unfair, or discriminatory results, along with challenges related to data management and usage. The AI model's lack of transparency may lead to pro-cyclicality and systemic risk in markets, posing issues for financial supervision and internal governance frameworks that may not be in line with a technology-neutral regulatory approach. The primary objective of this research is to explore the effectiveness of Artificial Intelligence in preventing financial misconduct. This study extensively examines sophisticated methods for combating financial fraud, specifically evaluating the efficacy of Machine Learning and Artificial Intelligence. When examining the assessment metrics, this study utilized various metrics like accuracy, precision, recall, F1 score, and the ROC-AUC. The study found that Deep Learning techniques such as "Neural Networks, Convolutional Neural Networks, Recurrent Neural Networks /Long Short-Term Memory, and Auto encoders" achieved high precision and AUC-ROC scores in detecting financial fraud. Voting classifiers, stacking, random forests, and gradient boosting machines demonstrated durability and precision in the face of adversarial attacks, showcasing the strength of unity.
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FRAUD DETECTION AND PREVENTION USING AI
International Journal On Engineering Technology and Sciences – IJETS , 2024
Fraud detection is vital in thwarting deceptive tactics aimed at unlawfully obtaining assets. It employs various methods, from basic rules to advanced machine learning, to uncover suspicious activities and safeguard organizational reputation and stakeholder trust. Adaptability is crucial, necessitating ongoing strategy refinement to counter evolving fraudster tactics. Collaboration across sectors enhances resilience through information sharing and coordinated action. Integration with robust risk management frameworks identifies vulnerabilities and preempts risks. In the digital age, cyber-enabled fraud demands sophisticated tools like behavioral analytics and real-time monitoring. Ultimately, fraud detection is indispensable for preserving financial integrity and trust, requiring vigilance, adaptability, and collaboration to combat evolving threats. The Oxford Dictionary defines fraud as the act of deceptive or criminal behaviour that leads to financial or personal benefit.[1] Organizations combat various fraudulent activities like money laundering, cyberattacks, and identity theft through advanced fraud detection technologies and risk management strategies. These methods utilize adaptive analytics, including machine learning, to create fraud risk scores and enable real-time monitoring of suspicious transactions. Automation facilitates the implementation of new preventive measures, aiding in staying ahead of evolving fraud schemes. Real-time monitoring ensures swift action against fraudulent behaviour, minimizing financial losses and maintaining stakeholder trust. In today's digital landscape, these modern techniques are crucial for organizations to effectively combat fraud and protect their assets and reputation. However, in certain jurisdictions, there are now mandates for fraud prevention programs, particularly notable in the insurance sector across multiple US states and under the UK's "Failure to Prevent Fraud" legislation since April 2023.lastly.The importance of fraud detection is underscored by data from the FBI in 2022, revealing that elderly fraud victims in the US faced an average loss of 35,101each,culminatinginatotallossexceeding35,101 each, culminating in a total loss exceeding 35,101each,culminatinginatotallossexceeding3 billion. Similarly, global fraud losses surpassed $55 billion in 2021
Internet fraud is increasing on a daily basis with new methods for fraudulently extracting funds from governments, corporations, businesses, and ordinary people appearing almost hourly. The increasing use of on-line purchasing and the constant and sometimes ineffective vigilance of both seller and buyer seemingly lead to the conclusion that the criminal seems to be one-step ahead at all times. Today, pre-empting or preventing fraud before it happens occurs in the manual, non-computer based business transactions because of the natural intelligence of both seller and buyer. Currently, even with advances in computing techniques, near human levels of intelligence is not the strength of any computing system, yet techniques are available which may reduce the occurrences of fraud, and are usually referred to as artificial intelligence systems. This paper provides an overview of the use of current artificial intelligence (AI) techniques as a means of combating transaction fraud. Initially this paper describes how artificial intelligence techniques are employed in systems for detecting credit card fraud (online and offline) and insider trading within the Bourses. Following this, an attempt is made to propose using the MonITARS (Monitoring Insider Trading and Regulatory Surveillance) Systems framework which uses a combination of genetic algorithms, neural nets and statistical analysis in detecting insider dealing, to be used in the detection of transaction fraud. Finally, the paper discusses future research agenda to the role of using MonITARS-type systems.
International Journal of Electrical and Computer Engineering (IJECE), 2024
This paper reviews the advances, challenges, and approaches of artificial intelligence (AI) and machine learning (ML) in the banking sector. The use of these technologies is accelerating in various industries, including banking. However, the literature on banking is scattered, making a global understanding difficult. This study reviewed the main approaches in terms of applications and algorithmic models, as well as the benefits and challenges associated with their implementation in banking, in addition to a bibliometric analysis of variables related to the distribution of publications and the most productive countries, as well as an analysis of the co-occurrence and dynamics of keywords. Following the preferred reporting items for systematic reviews and meta-analyses (PRISMA) framework, forty articles were selected for review. The results indicate that these technologies are used in the banking sector for customer segmentation, credit risk analysis, recommendation, and fraud detection. It should be noted that credit analysis and fraud detection are the most implemented areas, using algorithms such as random forests (RF), decision trees (DT), support vector machines (SVM), and logistic regression (LR), among others. In addition, their use brings significant benefits for decision-making and optimizing banking operations. However, the handling of substantial amounts of data with these technologies poses ethical challenges.
IJEER, 2022
Cyber security comes with a combination of various security policies, AI techniques, network technologies that work together to protect various computing resources like computing networks, intelligent programs, and sensitive data from attacks. Nowadays, the shift to digital freedom had led to opened many new challenges for financial services. Cybercriminals have found the ability to leverage e- currency exchanges and other financial transactions to perform their fraudulent activities. The unregulated channel makes it essential for banks and financial institutions to deploy advanced AI & ML (DL) techniques to fight cybercrime. This can be implemented by deploying AI & ML (DL) techniques. Customers are experiencing an increase in the fraud-hit rate in financial banking operations. It is difficult to defend against dynamic cyber-attacks using conventional non- dynamic algorithms. Therefore, AI with machine learning techniques has been set up with cyber security to build intelligent models for malware categorization & intelligently sensing the fraught with danger. This paper introduces the cyber security defense mechanism by using artificial intelligence (AI), machine learning (ML)) techniques with the current Feedzai security model to identifying fraudulent banking transaction. We have given a preface to the popular ML & AI model with random forest algorithm and Feedzai’s Open ML fraud detection software tool, which provides automatic fraud-recognition to the current intelligent framework for solving Financial Fraud Detection.
Prospective assessment of ai technologies for fraud detection: A case study
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In September 1995, the Congressional O ce of Technology Assessment completed a study of the potential for AI technologies to detect money laundering by screening wire transfers. The study, conducted at the request of the Senate Permanent Subcommittee on Investigations, evaluates the technical and public policy implications of widespread use of AI technologies by the Federal government for fraud detection.