Interpretable Multimodal Learning for Intelligent Regulation in Online
Payment Systems
- URL: http://arxiv.org/abs/2006.05669v1
- Date: Wed, 10 Jun 2020 06:08:20 GMT
- Title: Interpretable Multimodal Learning for Intelligent Regulation in Online
Payment Systems
- Authors: Shuoyao Wang, Diwei Zhu
- Abstract summary: We propose a novel cross-modal and intra-modal attention network (CIAN) to investigate the relation between the text and transaction.
We also design a CIAN-Explainer to interpret how the attention mechanism interacts the original features.
With the real datasets from the largest online payment system, WeChat Pay of Tencent, we conduct experiments to validate the practical application value of CIAN.
- Score: 7.766921168069532
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: With the explosive growth of transaction activities in online payment
systems, effective and realtime regulation becomes a critical problem for
payment service providers. Thanks to the rapid development of artificial
intelligence (AI), AI-enable regulation emerges as a promising solution. One
main challenge of the AI-enabled regulation is how to utilize multimedia
information, i.e., multimodal signals, in Financial Technology (FinTech).
Inspired by the attention mechanism in nature language processing, we propose a
novel cross-modal and intra-modal attention network (CIAN) to investigate the
relation between the text and transaction. More specifically, we integrate the
text and transaction information to enhance the text-trade jointembedding
learning, which clusters positive pairs and push negative pairs away from each
other. Another challenge of intelligent regulation is the interpretability of
complicated machine learning models. To sustain the requirements of financial
regulation, we design a CIAN-Explainer to interpret how the attention mechanism
interacts the original features, which is formulated as a low-rank matrix
approximation problem. With the real datasets from the largest online payment
system, WeChat Pay of Tencent, we conduct experiments to validate the practical
application value of CIAN, where our method outperforms the state-of-the-art
methods.
Related papers
- Token Communication in the Era of Large Models: An Information Bottleneck-Based Approach [55.861432910722186]
UniToCom is a unified token communication paradigm that treats tokens as the fundamental units for both processing and wireless transmission.<n>We propose a generative information bottleneck (GenIB) principle, which facilitates the learning of tokens that preserve essential information.<n>We employ a causal Transformer-based multimodal large language model (MLLM) at the receiver to unify the processing of both discrete and continuous tokens.
arXiv Detail & Related papers (2025-07-02T14:03:01Z) - TAMO:Fine-Grained Root Cause Analysis via Tool-Assisted LLM Agent with Multi-Modality Observation Data [33.5606443790794]
Large language models (LLMs) have made breakthroughs in contextual inference and domain knowledge integration.
We propose a tool-assisted LLM agent with multi-modality observation data, namely TAMO, for fine-grained root cause analysis.
arXiv Detail & Related papers (2025-04-29T06:50:48Z) - Regulating Ai In Financial Services: Legal Frameworks And Compliance Challenges [0.0]
Article examines the evolving landscape of artificial intelligence (AI) regulation in financial services.
It highlights how AI-driven processes, from fraud detection to algorithmic trading, offer efficiency gains yet introduce significant risks.
The study compares regulatory approaches across major jurisdictions such as the European Union, United States, and United Kingdom.
arXiv Detail & Related papers (2025-03-17T14:29:09Z) - Deep Learning Approaches for Anti-Money Laundering on Mobile Transactions: Review, Framework, and Directions [51.43521977132062]
Money laundering is a financial crime that obscures the origin of illicit funds.
The proliferation of mobile payment platforms and smart IoT devices has significantly complicated anti-money laundering investigations.
This paper conducts a comprehensive review of deep learning solutions and the challenges associated with their use in AML.
arXiv Detail & Related papers (2025-03-13T05:19:44Z) - Redefining Finance: The Influence of Artificial Intelligence (AI) and Machine Learning (ML) [2.3931689873603594]
With rapid transformation of technologies, the fusion of Artificial Intelligence (AI) and Machine Learning (ML) in finance is disrupting the entire ecosystem.
The segments of financial institutions which are getting heavily influenced are retail banking, wealth management, corporate banking & payment ecosystem.
arXiv Detail & Related papers (2024-10-21T12:32:17Z) - Online Multi-modal Root Cause Analysis [61.94987309148539]
Root Cause Analysis (RCA) is essential for pinpointing the root causes of failures in microservice systems.
Existing online RCA methods handle only single-modal data overlooking, complex interactions in multi-modal systems.
We introduce OCEAN, a novel online multi-modal causal structure learning method for root cause localization.
arXiv Detail & Related papers (2024-10-13T21:47:36Z) - Contractual Reinforcement Learning: Pulling Arms with Invisible Hands [68.77645200579181]
We propose a theoretical framework for aligning economic interests of different stakeholders in the online learning problems through contract design.
For the planning problem, we design an efficient dynamic programming algorithm to determine the optimal contracts against the far-sighted agent.
For the learning problem, we introduce a generic design of no-regret learning algorithms to untangle the challenges from robust design of contracts to the balance of exploration and exploitation.
arXiv Detail & Related papers (2024-07-01T16:53:00Z) - Coordinated Flaw Disclosure for AI: Beyond Security Vulnerabilities [1.3225694028747144]
We propose a Coordinated Flaw Disclosure framework tailored to the complexities of machine learning (ML) issues.
Our framework introduces innovations such as extended model cards, dynamic scope expansion, an independent adjudication panel, and an automated verification process.
We argue that CFD could significantly enhance public trust in AI systems.
arXiv Detail & Related papers (2024-02-10T20:39:04Z) - Explainable Automated Machine Learning for Credit Decisions: Enhancing
Human Artificial Intelligence Collaboration in Financial Engineering [0.0]
This paper explores the integration of Explainable Automated Machine Learning (AutoML) in the realm of financial engineering.
The focus is on how AutoML can streamline the development of robust machine learning models for credit scoring.
The findings underscore the potential of explainable AutoML in improving the transparency and accountability of AI-driven financial decisions.
arXiv Detail & Related papers (2024-02-06T08:47:16Z) - A Hypothesis on Good Practices for AI-based Systems for Financial Time
Series Forecasting: Towards Domain-Driven XAI Methods [0.0]
Machine learning and deep learning have become increasingly prevalent in financial prediction and forecasting tasks.
These models often lack transparency and interpretability, making them challenging to use in sensitive domains like finance.
This paper explores good practices for deploying explainability in AI-based systems for finance.
arXiv Detail & Related papers (2023-11-13T17:56:45Z) - Don't Treat the Symptom, Find the Cause! Efficient
Artificial-Intelligence Methods for (Interactive) Debugging [0.0]
In the modern world, we are permanently using, leveraging, interacting with, and relying upon systems of ever higher sophistication.
In this thesis, we will give an introduction to the topic of model-based diagnosis, point out the major challenges in the field, and discuss a selection of approaches from our research addressing these issues.
arXiv Detail & Related papers (2023-06-22T12:44:49Z) - Causal Semantic Communication for Digital Twins: A Generalizable
Imitation Learning Approach [74.25870052841226]
A digital twin (DT) leverages a virtual representation of the physical world, along with communication (e.g., 6G), computing, and artificial intelligence (AI) technologies to enable many connected intelligence services.
Wireless systems can exploit the paradigm of semantic communication (SC) for facilitating informed decision-making under strict communication constraints.
A novel framework called causal semantic communication (CSC) is proposed for DT-based wireless systems.
arXiv Detail & Related papers (2023-04-25T00:15:00Z) - Semantic Information Marketing in The Metaverse: A Learning-Based
Contract Theory Framework [68.8725783112254]
We address the problem of designing incentive mechanisms by a virtual service provider (VSP) to hire sensing IoT devices to sell their sensing data.
Due to the limited bandwidth, we propose to use semantic extraction algorithms to reduce the delivered data by the sensing IoT devices.
We propose a novel iterative contract design and use a new variant of multi-agent reinforcement learning (MARL) to solve the modelled multi-dimensional contract problem.
arXiv Detail & Related papers (2023-02-22T15:52:37Z) - 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) - Reconfigurable Intelligent Surface Assisted Mobile Edge Computing with
Heterogeneous Learning Tasks [53.1636151439562]
Mobile edge computing (MEC) provides a natural platform for AI applications.
We present an infrastructure to perform machine learning tasks at an MEC with the assistance of a reconfigurable intelligent surface (RIS)
Specifically, we minimize the learning error of all participating users by jointly optimizing transmit power of mobile users, beamforming vectors of the base station, and the phase-shift matrix of the RIS.
arXiv Detail & Related papers (2020-12-25T07:08:50Z)
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.