Leveraging Microsoft Fabric to Combat Financial and Economic Crimes
In the fight against financial and economic crimes such as corruption, embezzlement, fraud detection, money laundering, trade-based money laundering, VAT fraud, and profit shifting by mining companies, advanced data analysis and network graph analysis are crucial. Microsoft Fabric, with its medallion architecture, provides a robust framework for investigating agencies to store, process, and analyze data effectively. This article explores how medallion architecture can be implemented using bronze, silver, and gold layers to stop illicit financial flows.
What is Medallion Architecture?
Medallion Architecture is a data architecture pattern designed to organize and manage data in a structured and efficient manner. It is particularly useful for large-scale data processing and analytics. The architecture is typically divided into three layers: Bronze, Silver, and Gold. Each layer serves a specific purpose in the data processing pipeline, ensuring data quality, consistency, and usability for various analytical and reporting needs.
In Microsoft Fabric, the medallion architecture can be implemented using three workspaces: the Bronze Layer for Raw Intelligence, the Silver Layer for Cleaned Intelligence, and the Gold Layer for Highly Curated Intelligence.
Medallion Architecture: A Three-Layer Approach
Bronze Layer: Raw Intelligence
Objective: Store raw, unprocessed data in its original state as a single source of truth.
Data Sources:
Data sources for Raw Intelligence can be structured data and come from various sources such as Customs and Border Control Systems, Mines Operations, operational and transaction data from the Government, and Tax data from Revenue Collections.
Structured Data:
Customs and Border Control Systems: This includes shipments, customs, GPS tracking, weighbridges, customs declarations, and audits data.
Mines Operations: Important details here are production details, operational details, sales revenue, commodity prices, transactions, profit and loss statements, tax payments, balance sheets, export data, operational expenses, capital expenditures, and beneficial ownership data.
Operational and Transaction Data: This data can include transactions, employees, vendors, expenses, audit logs, gifts and benefits, whistleblower reports, accounts, projects, procurement, and invoices.
Tax Data: The key points are tax filings, taxpayers, audit logs, income sources, deductions, and refund claims.
Unstructured Data: This category includes various types of data that do not have a predefined data model or structure. Examples include:
Contracts: Legal agreements between parties, often stored as text documents or PDFs.
Invoices: Billing documents detailing transactions, typically in PDF or image formats.
Permits: Official documents granting permission, usually stored as scanned images or PDFs.
Manifests: Lists of cargo often in text or spreadsheet formats.
Lakehouse Storage:
A Lakehouse can be used to store both structured and unstructured data efficiently.
Structured Data: Store structured data in Lakehouse for each domain, such as Customs, Mines, Bribery, Tax Fraud, and VAT Refund Fraud.
Unstructured Data: Store unstructured data in corresponding Lakehouse as files.
Silver Layer: Cleaned Intelligence
Objective: Clean, deduplicate, enrich, and normalize data to improve quality and develop a single source of truth.
Data Validation and Cleansing: Data from the Bronze Layer should be fed into the Silver Layer using ETL processes. Spark notebooks will be utilized for data validation, cleansing, and merging.
Incremental ETL Processes: To ensure efficient data processing, incremental ETL processes should be implemented using Microsoft Fabric Data Factory. This approach allows for continuous and efficient updating of data in the Silver Layer without the need for full data reloads.
Transactions Data: The transactions data should be made up of structured data from the Bronze Layer. The merged data should be added to the transaction database (Lakehouse), containing combined data from:
Unstructured Data Extraction: Companies, People, and Transactions should be extracted from unstructured data using Large Language Models (LLMs) like OpenAI, Llama, or Gemini. This extraction will be performed using Spark notebooks with Python.
Data Processing Steps:
These processes will result in the creation of three distinct Lakehouse's:
These Lakehouse's will serve as the foundation for advanced analytics, reporting, and decision-making, ensuring that the organization has access to high-quality, reliable data.
Gold Layer: Highly Curated Intelligence
Objective: The Gold Layer aims to provide highly aggregated and semantically meaningful datasets for analytics, reporting, and machine learning. It also facilitates the generation of nodes and edges using network analysis and the creation of AI skills for multi-agents.
Dimensional Modeling and Power BI:
Machine Learning:
Training Models: Data in the Gold Layer should be used to train machine learning models to detect profit shifting, trade-based money laundering, and tax evasion red flags.
Key Algorithms:
Isolation Forest: Helps find unusual patterns that might indicate fraud.
Autoencoders: Used to detect anomalies by learning to recreate data and spotting differences.
Random Forest: A flexible tool used for making predictions by combining the results of many decision trees.
K-Means Clustering: Groups similar companies, people, or transactions together, making it easier to spot patterns and outliers.
Network Analysis:
Network Graphs: The Gold Layer should provide data to generate network graphs for detecting profit shifting, trade-based money laundering, tax evasion, and other financial crimes using NetworkX.
These algorithms will help identify influential companies, people, and transactions:
Betweenness Centrality: Identifies key intermediaries in a network.
Closeness Centrality: Measures how quickly money flows from companies, people, or transactions.
Degree Centrality: Counts the number of direct connections a company, person, or transaction has.
Eigenvector Centrality: Identifies influential companies, people, or transactions in a network.
PageRank: Ranks companies, people, or transactions based on their importance.
Label Propagation: Detects communities within a network.
Strongly Connected Components: Identifies clusters of companies with mutual reachability.
Filtered K-Nearest Neighbors: Finds similar companies, people, or transactions based on specific criteria.
Louvain: Detects communities (companies, people, or transactions) by optimizing modularity.
Conductance Metric: Measures the quality of clusters (companies, people, or transactions).
K-Means Clustering: Groups companies, people, or transactions into clusters based on similarity.
AI Agents:
AI Skill Creation: The Gold Layer should also provide data to create AI skills that can be integrated into AI agents, which can be utilized by professionals from various domains, such as investigators or executives. Examples include:
Anomaly Detection Agent: This agent uses AI skills to find unusual patterns in data. It helps spot irregularities that might indicate problems or fraud.
Network Analysis Agent: This agent uses AI skills to study relationships and interactions in data. It looks at how different things are connected and how they affect each other, giving insights into the network's structure.
Financial Statement Fraud Agent: This agent uses AI skills to find inconsistencies in financial statements. It checks for unusual entries or discrepancies that could suggest fraud or financial manipulation.
Conclusion
The implementation of Medallion Architecture using Microsoft Fabric's Bronze, Silver, and Gold layers provides a comprehensive and structured approach to combating financial and economic crimes. By organizing data into these layers, investigative agencies can ensure data quality, consistency, and usability, which are essential for effective analysis and decision-making. The Bronze Layer captures raw data, the Silver Layer refines and cleanses it, and the Gold Layer offers highly curated intelligence for advanced analytics, reporting, and machine learning. This multi-layered approach not only enhances the ability to detect and prevent illicit financial activities but also empowers investigators and decision-makers with reliable and actionable insights. As financial crimes become increasingly sophisticated, leveraging advanced data architectures like Medallion Architecture will be crucial in staying ahead and ensuring financial integrity.