Privacy Technologies for Financial Intelligence

• URL: http://arxiv.org/abs/2408.09935v1
• Date: Mon, 19 Aug 2024 12:13:53 GMT
• Title: Privacy Technologies for Financial Intelligence
• Authors: Yang Li, Thilina Ranbaduge, Kee Siong Ng,
• Abstract summary: Financial crimes like terrorism financing and money laundering can have real impacts on society. Data related to different pieces of the overall puzzle is usually distributed across a network of financial institutions, regulators, and law-enforcement agencies. Recent advances in Privacy-Preserving Data Matching and Machine Learning provide an opportunity for regulators and the financial industry to come together.
• Score: 6.287201938212411
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Financial crimes like terrorism financing and money laundering can have real impacts on society, including the abuse and mismanagement of public funds, increase in societal problems such as drug trafficking and illicit gambling with attendant economic costs, and loss of innocent lives in the case of terrorism activities. Complex financial crimes can be hard to detect primarily because data related to different pieces of the overall puzzle is usually distributed across a network of financial institutions, regulators, and law-enforcement agencies and they cannot be easily shared due to privacy constraints. Recent advances in Privacy-Preserving Data Matching and Machine Learning provide an opportunity for regulators and the financial industry to come together to solve the risk-discovery problem with technology. This paper provides a survey of the financial intelligence landscape and where opportunities lie for privacy technologies to improve the state-of-the-art in financial-crime detection.

Related papers

• Locally Differentially Private Embedding Models in Distributed Fraud Prevention Systems [2.001149416674759]
  We present a collaborative deep learning framework for fraud prevention, designed from a privacy standpoint, and awarded at the recent PETs Prize Challenges. We leverage latent embedded representations of varied-length transaction sequences, along with local differential privacy, in order to construct a data release mechanism which can securely inform externally hosted fraud and anomaly detection models. We assess our contribution on two distributed data sets donated by large payment networks, and demonstrate robustness to popular inference-time attacks, along with utility-privacy trade-offs analogous to published work in alternative application domains.
  arXiv  Detail & Related papers  (2024-01-03T14:04:18Z)
• Transparency and Privacy: The Role of Explainable AI and Federated Learning in Financial Fraud Detection [0.9831489366502302]
  This research introduces a novel approach using Federated Learning (FL) and Explainable AI (XAI) to address these challenges. FL enables financial institutions to collaboratively train a model to detect fraudulent transactions without directly sharing customer data. XAI ensures that the predictions made by the model can be understood and interpreted by human experts, adding a layer of transparency and trust to the system.
  arXiv  Detail & Related papers  (2023-12-20T18:26:59Z)
• Investigating Online Financial Misinformation and Its Consequences: A Computational Perspective [31.24530091590395]
  This research paper aims to provide a comprehensive survey of online financial misinformation, including its types, sources, and impacts. Early detection and mitigation strategies can help protect investors, enhance market transparency, and preserve financial stability.
  arXiv  Detail & Related papers  (2023-09-06T02:50:16Z)
• Designing an attack-defense game: how to increase robustness of financial transaction models via a competition [69.08339915577206]
  Given the escalating risks of malicious attacks in the finance sector, understanding adversarial strategies and robust defense mechanisms for machine learning models is critical. We aim to investigate the current state and dynamics of adversarial attacks and defenses for neural network models that use sequential financial data as the input. We have designed a competition that allows realistic and detailed investigation of problems in modern financial transaction data. The participants compete directly against each other, so possible attacks and defenses are examined in close-to-real-life conditions.
  arXiv  Detail & Related papers  (2023-08-22T12:53:09Z)
• A Privacy-Preserving Hybrid Federated Learning Framework for Financial Crime Detection [27.284477227066972]
  We propose a hybrid federated learning system that offers secure and privacy-aware learning and inference for financial crime detection. We conduct extensive empirical studies to evaluate the proposed framework's detection performance and privacy-protection capability.
  arXiv  Detail & Related papers  (2023-02-07T18:12:48Z)
• Having your Privacy Cake and Eating it Too: Platform-supported Auditing of Social Media Algorithms for Public Interest [70.02478301291264]
  Social media platforms curate access to information and opportunities, and so play a critical role in shaping public discourse. Prior studies have used black-box methods to show that these algorithms can lead to biased or discriminatory outcomes. We propose a new method for platform-supported auditing that can meet the goals of the proposed legislation.
  arXiv  Detail & Related papers  (2022-07-18T17:32:35Z)
• Fighting Money Laundering with Statistics and Machine Learning [95.42181254494287]
  There is little scientific literature on statistical and machine learning methods for anti-money laundering. We propose a unifying terminology with two central elements: (i) client risk profiling and (ii) suspicious behavior flagging.
  arXiv  Detail & Related papers  (2022-01-11T21:31:18Z)
• Towards Artificial Intelligence Enabled Financial Crime Detection [0.0]
  We study and analyse the recent works done in financial crime detection. We present a novel model to detect money laundering cases with minimum human intervention needs.
  arXiv  Detail & Related papers  (2021-05-23T06:57:25Z)
• Characterization of the Firm-Firm Public Procurement Co-Bidding Network from the State of Cear\'a (Brazil) Municipalities [58.720142291102135]
  We study the co-biding relationships between firms that participate in public tenders issued by the $184$ municipalities of the State of Cear'a (Brazil) between 2015 and 2019. We identify $22$ groups/communities of firms with similar patterns of procurement activity, defined by their geographic and activity.
  arXiv  Detail & Related papers  (2021-04-17T13:58:30Z)
• PCAL: A Privacy-preserving Intelligent Credit Risk Modeling Framework Based on Adversarial Learning [111.19576084222345]
  This paper proposes a framework of Privacy-preserving Credit risk modeling based on Adversarial Learning (PCAL) PCAL aims to mask the private information inside the original dataset, while maintaining the important utility information for the target prediction task performance. Results indicate that PCAL can learn an effective, privacy-free representation from user data, providing a solid foundation towards privacy-preserving machine learning for credit risk analysis.
  arXiv  Detail & Related papers  (2020-10-06T07:04:59Z)
• A vision for global privacy bridges: Technical and legal measures for international data markets [77.34726150561087]
  Despite data protection laws and an acknowledged right to privacy, trading personal information has become a business equated with "trading oil" An open conflict is arising between business demands for data and a desire for privacy. We propose and test a vision of a personal information market with privacy.
  arXiv  Detail & Related papers  (2020-05-13T13:55:50Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.