Financial data
Financial data refers to any information related to monetary transactions, financial performance, or economic status that is captured, processed, stored, or analyzed by a software application. This data is often used for decision-making, reporting, compliance, and operational purposes in business, accounting, banking, and other financial domains.
Financial data typically includes numerical values, timestamps, categorizations, and descriptive information about financial activities. It is structured to facilitate accurate calculations, analytics, and reporting, and is often governed by strict security and compliance standards.
Examples of financial data include the following:
- Transactional Data: Records of individual financial transactions
- Account data: Information related to financial accounts, such as balances and account histories
- Budgeting and forecasting data: Data used for planning future financial outcomes
- Financial performance metrics: Key indicators of business or personal financial health
- Tax and compliance data: Data used for regulatory reporting and compliance
- Investment data: Information about investment portfolios, market performance, and securities
- Payroll data: Data related to salaries, wages, and employee compensation
- Expense and invoice data: Data capturing expenses and invoices for goods or services.
Financial data is highly sensitive and must be protected with encryption, access control, and compliance with standards like PCI DSS (Payment Card Industry Data Security Standard) and GDPR (General Data Protection Regulation). It is typically stored in structured formats like databases or spreadsheets, with common file types including CSV, JSON, and XML.
Use cases for the Splunk platform
- Getting started with Splunk Essentials for the Financial Services Industry
- Analyzing credit limit increase requests
- Applying Benford's law of distribution to spot fraud
- Applying Zipf's law in fraud detection
- Detecting ATM fraud
- Detecting wire transfer fraud
- Identifying indicators of fraud using geometric principles
- Monitoring ATM usage
- Monitoring new logins to financial applications
- Monitoring payment responses
- Monitoring wire transfers
- Predicting failed trade settlements
- Reporting on key trade statistics in a brokerage
- Tracking a retail banking transaction end-to-end
- Using Amazon SageMaker to predict risk scores
- Using modern methods of detecting financial crime
- Using risk scores to improve decision-making
- Monitoring consumer credit card transactions
- Monitoring mobile device payments
- Monitoring money laundering activities with the Splunk platform
- Monitoring for account abuse with the Splunk platform
- Monitoring for account takeover with the Splunk platform
- Monitoring for account takeover with the Splunk App for Behavioral Analytics
- Monitoring money laundering activities with the Splunk App for Behavioral Analytics
- Monitoring for account abuse with the Splunk App for Behavioral Analytics
- Detecting financial fraud using the Splunk App for Behavioral Profiling