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This guide provides a comprehensive tutorial on using Python libraries such as pandas, openpyxl, and matplotlib for financial data processing, including reading/writing Excel files, data manipulation, and visualization techniques.
This article presents a comprehensive technical overview of how large AI models are reshaping financial data production, governance, multimodal document understanding, lakehouse storage, private‑domain model deployment, data‑centric engineering methods, and multi‑agent intelligent advisory within the finance sector.
This presentation by Zhou Jie, a senior data R&D expert at Ant Financial, outlines the scope of data governance, examines the challenges of ensuring high‑quality financial data, and shares practical architectures, solutions, and case studies to help attendees understand data quality risks and mitigation strategies.
This article introduces the Ant Group and Tsinghua University’s Financial Big Data Anti‑Fraud Technology Whitepaper, outlining the new legal context, fraud characteristics, and a three‑stage detection framework that leverages multi‑dimensional graphs, trustworthy AI, and data security to improve pre‑, during‑, and post‑transaction risk management.
This article provides a comprehensive overview of privacy computing—covering its background, key techniques such as MPC, TEE, federated learning, homomorphic encryption, and differential privacy—and examines how these technologies are applied in open financial ecosystems, including use cases, challenges, and future directions.
The article details Snowball's large‑scale financial knowledge graph, covering its background challenges, two‑layer ontology and data design, data sourcing and pipeline, graph database selection, search and NLP services, domain‑specific pre‑training models, and future industrial considerations.
The LDBC has approved the world’s first financial graph database benchmark, FinBench, a collaborative effort led by Ant Group to provide a rigorous, open‑source testing suite that simulates real‑world financial workloads and fills a critical gap in graph database evaluation.
This presentation explores how to scientifically define, apply, evaluate, and interpret data in financial risk management, covering data alignment with business goals, feature selection, model metrics such as KS and PSI, handling pandemic effects, and methods for model explainability.
This presentation explores how to scientifically define, apply, evaluate, and interpret data in financial risk management, covering data alignment with business goals, feature selection, model metrics like KS and PSI, handling pandemic impacts, and methods for model explanation and improvement.
This article provides a Python tutorial that explains how to generate Eastmoney‑specific security IDs, request minute‑level K‑line data for a given stock code via the Eastmoney API, process the response into a pandas DataFrame, and continuously fetch and save data until market close.
This article explains how financial institutions can leverage massive structured and unstructured data by building a financial knowledge graph, detailing AI core technologies, schema design, extraction methods, storage solutions, and a range of practical applications such as intelligent tagging, recommendation, policy analysis, and executive relationship mining.
This tutorial explains how to load a CSV file of financial time‑series data into pandas, perform basic exploratory analysis, compute absolute and relative changes, generate log returns, visualize results, and apply weekly or monthly resampling while avoiding look‑ahead bias.