Atlas-X Equity Financing: Unlocking New Methods to Securely Obfuscate Axe Inventory Data Based on Differential Privacy

• URL: http://arxiv.org/abs/2404.06686v1
• Date: Wed, 10 Apr 2024 02:19:37 GMT
• Title: Atlas-X Equity Financing: Unlocking New Methods to Securely Obfuscate Axe Inventory Data Based on Differential Privacy
• Authors: Antigoni Polychroniadou, Gabriele Cipriani, Richard Hua, Tucker Balch,
• Abstract summary: Atlas-X Axe Obfuscation enables a bank to obfuscate its published axe list on a daily basis while under continual observation. To our knowledge, it is the first differential privacy solution to be deployed in the financial sector.
• Score: 6.146022401628768
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Banks publish daily a list of available securities/assets (axe list) to selected clients to help them effectively locate Long (buy) or Short (sell) trades at reduced financing rates. This reduces costs for the bank, as the list aggregates the bank's internal firm inventory per asset for all clients of long as well as short trades. However, this is somewhat problematic: (1) the bank's inventory is revealed; (2) trades of clients who contribute to the aggregated list, particularly those deemed large, are revealed to other clients. Clients conducting sizable trades with the bank and possessing a portion of the aggregated asset exceeding $50\%$ are considered to be concentrated clients. This could potentially reveal a trading concentrated client's activity to their competitors, thus providing an unfair advantage over the market. Atlas-X Axe Obfuscation, powered by new differential private methods, enables a bank to obfuscate its published axe list on a daily basis while under continual observation, thus maintaining an acceptable inventory Profit and Loss (P&L) cost pertaining to the noisy obfuscated axe list while reducing the clients' trading activity leakage. Our main differential private innovation is a differential private aggregator for streams (time series data) of both positive and negative integers under continual observation. For the last two years, Atlas-X system has been live in production across three major regions-USA, Europe, and Asia-at J.P. Morgan, a major financial institution, facilitating significant profitability. To our knowledge, it is the first differential privacy solution to be deployed in the financial sector. We also report benchmarks of our algorithm based on (anonymous) real and synthetic data to showcase the quality of our obfuscation and its success in production.

Related papers

• Towards Collaborative Anti-Money Laundering Among Financial Institutions [14.148199080030574]
  Rule-based methods were first introduced and are still widely used in current detection systems. In practice, money laundering activities usually span multiple financial institutions. We propose the first algorithm that supports performing anti-money laundering over multiple institutions.
  arXiv  Detail & Related papers  (2025-02-27T10:22:55Z)
• FinTSB: A Comprehensive and Practical Benchmark for Financial Time Series Forecasting [58.70072722290475]
  Financial time series (FinTS) record the behavior of human-brain-augmented decision-making. FinTSB is a comprehensive and practical benchmark for financial time series forecasting.
  arXiv  Detail & Related papers  (2025-02-26T05:19:16Z)
• Private, Auditable, and Distributed Ledger for Financial Institutes [1.8911961520222993]
  This paper proposes a framework for a private, audit-able, and distributed ledger (PADL) that adapts easily to fundamental use-cases within financial institutes. PADL employs widely-used cryptography schemes combined with zero-knowledge proofs to propose a transaction scheme for a table' like ledger. We show that PADL supports smooth-lined inter-assets auditing while preserving privacy of the participants.
  arXiv  Detail & Related papers  (2025-01-07T14:21:24Z)
• Safety vs. Performance: How Multi-Objective Learning Reduces Barriers to Market Entry [86.79268605140251]
  We study whether there are insurmountable barriers to entry in emerging markets for large language models. We show that the required number of data points can be significantly smaller than the incumbent company's dataset size. Our results demonstrate how multi-objective considerations can fundamentally reduce barriers to entry.
  arXiv  Detail & Related papers  (2024-09-05T17:45:01Z)
• Advancing Anomaly Detection: Non-Semantic Financial Data Encoding with LLMs [49.57641083688934]
  We introduce a novel approach to anomaly detection in financial data using Large Language Models (LLMs) embeddings. Our experiments demonstrate that LLMs contribute valuable information to anomaly detection as our models outperform the baselines.
  arXiv  Detail & Related papers  (2024-06-05T20:19:09Z)
• Prime Match: A Privacy-Preserving Inventory Matching System [15.320275576536854]
  In the financial world, banks often undertake the task of finding such matches between their clients. If no match is found, the parties have to buy or sell the stock in the public market, which introduces additional costs. We provide a solution, Prime Match, that enables clients to match their orders efficiently with reduced market impact.
  arXiv  Detail & Related papers  (2023-10-14T17:03:44Z)
• Federated Learning Incentive Mechanism under Buyers' Auction Market [2.316580879469592]
  Auction-based Federated Learning (AFL) enables open collaboration among self-interested data consumers and data owners. We adapt the procurement auction framework, aiming to explain the pricing behavior under buyers' market. In order to select clients with high reliability and data quality, and to prevent from external attacks, we utilize a blockchain-based reputation mechanism.
  arXiv  Detail & Related papers  (2023-09-10T16:09:02Z)
• The Double-Edged Sword of Big Data and Information Technology for the Disadvantaged: A Cautionary Tale from Open Banking [0.3867363075280543]
  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.
  arXiv  Detail & Related papers  (2023-07-25T11:07:43Z)
• Quantum computational finance: martingale asset pricing for incomplete markets [69.73491758935712]
  We show that a variety of quantum techniques can be applied to the pricing problem in finance. We discuss three different methods that are distinct from previous works.
  arXiv  Detail & Related papers  (2022-09-19T09:22:01Z)
• A Stock Trading System for a Medium Volatile Asset using Multi Layer Perceptron [0.6882042556551609]
  We propose a stock trading system having as main core the feed-forward deep neural networks (DNN) to predict the price for the next 30 days of open market. The results were promising bringing a total profit factor of 3.2% in just one month from a very modest budget of $100.
  arXiv  Detail & Related papers  (2022-01-17T16:08:40Z)
• Supporting Financial Inclusion with Graph Machine Learning and Super-App Alternative Data [63.942632088208505]
  Super-Apps have changed the way we think about the interactions between users and commerce. This paper investigates how different interactions between users within a Super-App provide a new source of information to predict borrower behavior.
  arXiv  Detail & Related papers  (2021-02-19T15:13:06Z)
• Segmenting Bank Customers via RFM Model and Unsupervised Machine Learning [0.0]
  In recent years, one of the major challenges for financial institutions is the retention of their customers. In this paper, we used RFM technique and various clustering algorithms applied to the real customer data of one of the largest private banks of Azerbaijan.
  arXiv  Detail & Related papers  (2020-08-19T20:41:18Z)
• Super-App Behavioral Patterns in Credit Risk Models: Financial, Statistical and Regulatory Implications [110.54266632357673]
  We present the impact of alternative data that originates from an app-based marketplace, in contrast to traditional bureau data, upon credit scoring models. Our results, validated across two countries, show that these new sources of data are particularly useful for predicting financial behavior in low-wealth and young individuals.
  arXiv  Detail & Related papers  (2020-05-09T01:32:03Z)
• Reinforcement-Learning based Portfolio Management with Augmented Asset Movement Prediction States [71.54651874063865]
  Portfolio management (PM) aims to achieve investment goals such as maximal profits or minimal risks. In this paper, we propose SARL, a novel State-Augmented RL framework for PM. Our framework aims to address two unique challenges in financial PM: (1) data Heterogeneous data -- the collected information for each asset is usually diverse, noisy and imbalanced (e.g., news articles); and (2) environment uncertainty -- the financial market is versatile and non-stationary.
  arXiv  Detail & Related papers  (2020-02-09T08:10:03Z)

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.