The Double-Edged Sword of Big Data and Information Technology for the Disadvantaged: A Cautionary Tale from Open Banking

• URL: http://arxiv.org/abs/2307.13408v2
• Date: Tue, 1 Aug 2023 10:46:20 GMT
• Title: The Double-Edged Sword of Big Data and Information Technology for the Disadvantaged: A Cautionary Tale from Open Banking
• Authors: Savina Dine Kim and Galina Andreeva and Michael Rovatsos
• Abstract summary: Open Banking has ignited a revolution in financial services, opening new opportunities for customer acquisition, management, retention, and risk assessment. We investigate the dimensions of financial vulnerability (FV), a global concern resulting from COVID-19 and rising inflation. Using a unique dataset from a UK FinTech lender, we demonstrate the power of fine-grained transaction data while simultaneously cautioning its safe usage.
• Score: 0.3867363075280543
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This research article analyses and demonstrates the hidden implications for fairness of seemingly neutral data coupled with powerful technology, such as machine learning (ML), using Open Banking as an example. Open Banking has ignited a revolution in financial services, opening new opportunities for customer acquisition, management, retention, and risk assessment. However, the granularity of transaction data holds potential for harm where unnoticed proxies for sensitive and prohibited characteristics may lead to indirect discrimination. Against this backdrop, we investigate the dimensions of financial vulnerability (FV), a global concern resulting from COVID-19 and rising inflation. Specifically, we look to understand the behavioral elements leading up to FV and its impact on at-risk, disadvantaged groups through the lens of fair interpretation. Using a unique dataset from a UK FinTech lender, we demonstrate the power of fine-grained transaction data while simultaneously cautioning its safe usage. Three ML classifiers are compared in predicting the likelihood of FV, and groups exhibiting different magnitudes and forms of FV are identified via clustering to highlight the effects of feature combination. Our results indicate that engineered features of financial behavior can be predictive of omitted personal information, particularly sensitive or protected characteristics, shedding light on the hidden dangers of Open Banking data. We discuss the implications and conclude fairness via unawareness is ineffective in this new technological environment.

Related papers

• SEFraud: Graph-based Self-Explainable Fraud Detection via Interpretative Mask Learning [19.774363067972725]
  Graph-based fraud detection has widespread application in modern industry scenarios, such as spam review and malicious account detection. Previous works have attempted to generate explanations for specific instances using post-hoc explaining methods such as a GNNExplainer. We propose SEFraud, a novel graph-based self-explainable fraud detection framework that simultaneously tackles fraud detection and result in interpretability.
  arXiv  Detail & Related papers  (2024-06-17T10:18:53Z)
• 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)
• Privacy-Preserving Federated Learning over Vertically and Horizontally Partitioned Data for Financial Anomaly Detection [11.167661320589488]
  In real-world financial anomaly detection scenarios, the data is partitioned both vertically and horizontally. Our solution combines fully homomorphic encryption (HE), secure multi-party computation (SMPC), differential privacy (DP) Our solution won second prize in the first phase of the U.S. Privacy Enhancing Technologies (PETs) Prize Challenge.
  arXiv  Detail & Related papers  (2023-10-30T06:51:33Z)
• Heterogeneous Information Network based Default Analysis on Banking Micro and Small Enterprise Users [18.32345474014549]
  We consider a graph of banking data, and propose a novel HIDAM model for the purpose. To enhance feature representation of MSEs, we extract interactive information through meta-paths and fully exploit path information. Experimental results verify that HIDAM outperforms state-of-the-art competitors on real-world banking data.
  arXiv  Detail & Related papers  (2022-04-24T11:26:12Z)
• 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)
• Counterfactual Explanations as Interventions in Latent Space [62.997667081978825]
  Counterfactual explanations aim to provide to end users a set of features that need to be changed in order to achieve a desired outcome. Current approaches rarely take into account the feasibility of actions needed to achieve the proposed explanations. We present Counterfactual Explanations as Interventions in Latent Space (CEILS), a methodology to generate counterfactual explanations.
  arXiv  Detail & Related papers  (2021-06-14T20:48:48Z)
• Trustworthy Transparency by Design [57.67333075002697]
  We propose a transparency framework for software design, incorporating research on user trust and experience. Our framework enables developing software that incorporates transparency in its design.
  arXiv  Detail & Related papers  (2021-03-19T12:34:01Z)
• Curse or Redemption? How Data Heterogeneity Affects the Robustness of Federated Learning [51.15273664903583]
  Data heterogeneity has been identified as one of the key features in federated learning but often overlooked in the lens of robustness to adversarial attacks. This paper focuses on characterizing and understanding its impact on backdooring attacks in federated learning through comprehensive experiments using synthetic and the LEAF benchmarks.
  arXiv  Detail & Related papers  (2021-02-01T06:06:21Z)
• Explainable Deep Behavioral Sequence Clustering for Transaction Fraud Detection [3.9505606841402607]
  We propose a Deep learning based behavior data representation method for Clustering (FinDeepBehaviorCluster) to detect fraudulent transactions. To utilize the behavior sequence data, we treat click stream data as event sequence, use time attention based Bi-LSTM to learn the sequence embedding in an unsupervised fashion, and combine them with intuitive features generated by risk experts to form a hybrid feature representation. Our experimental results show that the proposed FinDeepBehaviorCluster framework is able to catch missed fraudulent transactions with considerable business values.
  arXiv  Detail & Related papers  (2021-01-12T04:12:18Z)
• Overcoming Failures of Imagination in AI Infused System Development and Deployment [71.9309995623067]
  NeurIPS 2020 requested that research paper submissions include impact statements on "potential nefarious uses and the consequences of failure" We argue that frameworks of harms must be context-aware and consider a wider range of potential stakeholders, system affordances, as well as viable proxies for assessing harms in the widest sense.
  arXiv  Detail & Related papers  (2020-11-26T18:09:52Z)
• Measurement-driven Security Analysis of Imperceptible Impersonation Attacks [54.727945432381716]
  We study the exploitability of Deep Neural Network-based Face Recognition systems. We show that factors such as skin color, gender, and age, impact the ability to carry out an attack on a specific target victim. We also study the feasibility of constructing universal attacks that are robust to different poses or views of the attacker's face.
  arXiv  Detail & Related papers  (2020-08-26T19:27:27Z)

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.