Proceedings of the First ACM International Conference on AI in Finance最新文献_第2页

Deep Q-network-based adaptive alert threshold selection policy for payment fraud systems in retail banking 基于深度q网络的零售银行支付欺诈系统自适应报警阈值选择策略

Proceedings of the First ACM International Conference on AI in Finance Pub Date : 2020-10-15 DOI: 10.1145/3383455.3422563

Hongda Shen, Eren Kurshan

{"title":"Deep Q-network-based adaptive alert threshold selection policy for payment fraud systems in retail banking","authors":"Hongda Shen, Eren Kurshan","doi":"10.1145/3383455.3422563","DOIUrl":"https://doi.org/10.1145/3383455.3422563","url":null,"abstract":"Machine learning models have widely been used in fraud detection systems. Most of the research and development efforts have been concentrated on improving the performance of the fraud scoring models. Yet, the downstream fraud alert systems still have limited to no model adoption and rely on manual steps. Alert systems are pervasively used across all payment channels in retail banking and play an important role in the overall fraud detection process. Current fraud detection systems end up with large numbers of dropped alerts due to their inability to account for the alert processing capacity. Ideally, alert threshold selection enables the system to maximize the fraud detection while balancing the upstream fraud scores and the available bandwidth of the alert processing teams. However, in practice, fixed thresholds that are used for their simplicity do not have this ability. In this paper, we propose an enhanced threshold selection policy for fraud alert systems. The proposed approach formulates the threshold selection as a sequential decision making problem and uses Deep Q-Network based reinforcement learning. Experimental results show that this adaptive approach outperforms the current static solutions by reducing the fraud losses as well as improving the operational efficiency of the alert system.","PeriodicalId":447950,"journal":{"name":"Proceedings of the First ACM International Conference on AI in Finance","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116484131","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 15

Connecting the dots: forecasting and explaining short-term market volatility 串连点:预测和解释短期市场波动

Proceedings of the First ACM International Conference on AI in Finance Pub Date : 2020-10-15 DOI: 10.1145/3383455.3422518

Jie Yuan, Zhu Zhang

引用次数: 1

Utilization of deep learning to mine insights from earning calls for stock price movement predictions 利用深度学习从盈利呼吁中挖掘洞察力，以预测股价走势

Proceedings of the First ACM International Conference on AI in Finance Pub Date : 2020-10-15 DOI: 10.1145/3383455.3422524

Zhiqiang Ma, Chong Wang, G. Bang, Xiaomo Liu

引用次数: 0

Classifying high-frequency FX rate movements with technical indicators and inception model 用技术指标和初始模型对高频外汇汇率变动进行分类

Proceedings of the First ACM International Conference on AI in Finance Pub Date : 2020-10-15 DOI: 10.1145/3383455.3422560

Zheng Gong, Carmine Ventre, J. O'Hara

{"title":"Classifying high-frequency FX rate movements with technical indicators and inception model","authors":"Zheng Gong, Carmine Ventre, J. O'Hara","doi":"10.1145/3383455.3422560","DOIUrl":"https://doi.org/10.1145/3383455.3422560","url":null,"abstract":"Recent advances in the adoption of AI to forecast stock price movements highlight (i) new ways to encode financial data and technical indicators conveniently; (ii) a state-of-the-art architecture based on inception networks; and, (iii) the existence of across-asset universal features that can be leveraged to improve performances. We combine these three observations and investigate the extent to which this new pipeline can guarantee good predictive power in FX markets. Ultimately, we wonder if these approaches continue to work in a quote-driven market, wherein the AI cannot rely on the (micro) structure of the limit order book. More precisely, we develop a neural network based model for classifying high-frequency FX price movements. The architecture utilises inception modules to capture useful spatial structures from an image-like matrix that consists of different technical indicators. Gated Recurrent Units (GRU) are also implemented to identify longer time dependencies. We assess the model by testing its out-of-sample classifications on future price movements with ten FX pairs - we show how it outperforms Linear Discriminant Analysis (LDA) model on both accuracy and F1 score. We also found that training a universal model with all FX pairs could further improve the classification performances, which indicates that universal features exist among FX tick data.","PeriodicalId":447950,"journal":{"name":"Proceedings of the First ACM International Conference on AI in Finance","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129701163","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

A hybrid learning approach to detecting regime switches in financial markets 一种检测金融市场制度转换的混合学习方法

Proceedings of the First ACM International Conference on AI in Finance Pub Date : 2020-10-15 DOI: 10.1145/3383455.3422521

Peter Akioyamen, Yifu Tang, Hussien Hussien

引用次数: 4

Learning sampling in financial statement audits using vector quantised variational autoencoder neural networks 利用向量量化变分自编码器神经网络在财务报表审计中学习抽样

Proceedings of the First ACM International Conference on AI in Finance Pub Date : 2020-10-15 DOI: 10.1145/3383455.3422546

Marco Schreyer, Timur Sattarov, Anita Gierbl, Bernd Reimer, Damian Borth

{"title":"Learning sampling in financial statement audits using vector quantised variational autoencoder neural networks","authors":"Marco Schreyer, Timur Sattarov, Anita Gierbl, Bernd Reimer, Damian Borth","doi":"10.1145/3383455.3422546","DOIUrl":"https://doi.org/10.1145/3383455.3422546","url":null,"abstract":"The audit of financial statements is designed to collect reasonable assurance that an issued statement is free from material misstatement ('true and fair presentation'). International audit standards require the assessment of a statements' underlying accounting relevant transactions referred to as 'journal entries' to detect potential misstatements. To efficiently audit the increasing quantities of such journal entries, auditors regularly conduct an 'audit sampling' i.e. a sample-based assessment of a subset of these journal entries. However, the task of audit sampling is often conducted early in the overall audit process, where the auditor might not be aware of all generative factors and their dynamics that resulted in the journal entries in-scope of the audit. To overcome this challenge, we propose the use of a Vector Quantised-Variational Autoencoder (VQ-VAE) neural networks to learn a representation of journal entries able to provide a comprehensive 'audit sampling' to the auditor. We demonstrate, based on two real-world city payment datasets, that such artificial neural networks are capable of learning a quantised representation of accounting data. We show that the learned quantisation uncovers (i) the latent factors of variation and (ii) can be utilised as a highly representative audit sample in financial statement audits.","PeriodicalId":447950,"journal":{"name":"Proceedings of the First ACM International Conference on AI in Finance","volume":"137 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116605929","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 8

Subgraph anomaly detection in financial transaction networks 金融交易网络中的子图异常检测

Proceedings of the First ACM International Conference on AI in Finance Pub Date : 2020-10-15 DOI: 10.1145/3383455.3422548

Yulong Pei, Fang Lyu, Werner van Ipenburg, Mykola Pechenizkiy

{"title":"Subgraph anomaly detection in financial transaction networks","authors":"Yulong Pei, Fang Lyu, Werner van Ipenburg, Mykola Pechenizkiy","doi":"10.1145/3383455.3422548","DOIUrl":"https://doi.org/10.1145/3383455.3422548","url":null,"abstract":"Effective anomaly detection is crucial for the success of many AI-based solutions in the financial domain, including e.g. fraud detection and risk modeling. Identifying anomaly from financial transaction networks is one of the challenging tasks that can be cast as a special instance of anomaly detection in networks. Existing methods typically attempt to detect only node-level anomalies, and assume prior knowledge to extract representative features for identifying anomalies. However, there exist collective fraudulent behaviors at the level of subgraphs rather than individual node. A ring structure for money laundering and a tree structure for pyramid schemes would be common examples. Also, in practice it is difficult to decide which features are more representative beforehand. In this paper, we introduce SADE (Subgraph Anomaly DEtection) framework that addresses these needs. SADE consists of two steps: 1) role-guided subgraph embedding, and 2) subgraph anomaly detection. Our approach for learning the subgraph embeddings allows to preserve both the local structure of subgraphs and the global structure of entire network by making use of global roles and local connections of nodes. The learnt representation allows effective use of the state of art anomaly detection approaches. Our extensive experiments on synthetic and real-world financial transaction networks demonstrate the effectiveness of SADE in learning subgraph embeddings without requiring any prior knowledge and detecting anomalous subgraphs.","PeriodicalId":447950,"journal":{"name":"Proceedings of the First ACM International Conference on AI in Finance","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130018603","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 6

Unsupervised-learning financial reconciliation: a robust, accurate approach inspired by machine translation 无监督学习财务对账:受机器翻译启发的稳健、准确的方法

Proceedings of the First ACM International Conference on AI in Finance Pub Date : 2020-10-15 DOI: 10.1145/3383455.3422517

Peter A. Chew

{"title":"Unsupervised-learning financial reconciliation: a robust, accurate approach inspired by machine translation","authors":"Peter A. Chew","doi":"10.1145/3383455.3422517","DOIUrl":"https://doi.org/10.1145/3383455.3422517","url":null,"abstract":"Financial reconciliation (cross-checking independent sources of data) is a time-honored and widespread function in finance and audit. Its objectives are to ensure completeness, timeliness, and accuracy of recording of transactions. With the proliferation of data over recent decades, the ways in which reconciliation is approached have evolved. There are now a number of software products that promise to automate the process of detailed transaction matching. However, the 'state of the art' in these products is rules-based reconciliation. Rules-based systems in any domain tend to be brittle in that any change in the data streams causes the rules to have to be debugged and rebuilt, itself often a time-consuming process. And to make matters worse, this might be caused not just by data schema changes, but the contents of what is in the data fields themselves. Either of these can occur if a third party such as a bank changes its internal processes. In a sense, automated reconciliation is where machine translation was almost 70 years ago; IBM's 1954 Georgetown experiment approached Russian-to-English translation using rules, but it took another 4 decades for researchers in data-driven Artificial Intelligence (AI) to realize why machine translation did not initially live up to its promises and develop a truly robust methodology for machine translation based on unsupervised learning. It turns out that financial reconciliation can be cast as a machine translation problem based on unsupervised learning. To our knowledge, we are the first to propose this. Here, we demonstrate via experiments on real-life (albeit small-scale) financial data that this way of approaching the problem demonstrates promise in terms of accuracy, as well as solving the problem of lack of robustness inherent in rules-based approaches.","PeriodicalId":447950,"journal":{"name":"Proceedings of the First ACM International Conference on AI in Finance","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128050272","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Dealing with transaction costs in portfolio optimization: online gradient descent with momentum 投资组合优化中的交易成本处理:带动量的在线梯度下降

Proceedings of the First ACM International Conference on AI in Finance Pub Date : 2020-10-15 DOI: 10.1145/3383455.3422531

Edoardo Vittori, Martino Bernasconi de Luca, F. Trovò, Marcello Restelli

{"title":"Dealing with transaction costs in portfolio optimization: online gradient descent with momentum","authors":"Edoardo Vittori, Martino Bernasconi de Luca, F. Trovò, Marcello Restelli","doi":"10.1145/3383455.3422531","DOIUrl":"https://doi.org/10.1145/3383455.3422531","url":null,"abstract":"Outperforming the markets through active investment strategies is one of the main challenges in finance. The random movements of assets and the unpredictability of catalysts make it hard to perform better than the average market, therefore, in such a competitive environment, methods designed to keep low transaction costs have a significant impact on the obtained wealth. This paper focuses on investing techniques to beat market returns through online portfolio optimization while controlling transaction costs. Such a framework differs from classical approaches as it assumes that the market has an adversarial behavior, which requires frequent portfolio rebalancing. This paper analyses critically the known online learning literature dealing with transaction costs and proposes a novel algorithm, namely Online Gradient Descent with Momentum (OGDM), to control (theoretically and empirically) the costs. The existing algorithms designed for this setting are either (i) not providing theoretical guarantees, (ii) providing a bound to the total regret, conditionally on unrealistic assumptions or (iii) computationally not efficient. In this paper, we prove that OGDM has nice theoretical, empirical, and computational performances. We show that it has regret, considering costs, of the order [EQUATION], T being the investment horizon, and has Θ(M) per-step computational complexity, M being the number of assets. Furthermore, we show that this algorithm provides competitive gains when compared empirically with state-of-the-art online learning algorithms on a real-world dataset.","PeriodicalId":447950,"journal":{"name":"Proceedings of the First ACM International Conference on AI in Finance","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131470645","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 6

A multi-faceted approach to large scale financial forecasting 大规模财务预测的多方面方法

Proceedings of the First ACM International Conference on AI in Finance Pub Date : 2020-10-15 DOI: 10.1145/3383455.3422551

Antony Papadimitriou, Urjitkumar Patel, Lisa Kim, G. Bang, Azadeh Nematzadeh, Xiaomo Liu

引用次数: 3