Leveraging Transactional Data for Analytics: Building Economic Indicators from Wire Transfers, Credit Card, and ACH Transactions
Sandeep Yadav
Email: sandeep.yadav@asu.edu
Silicon Valley Bank, USA
ORCID: 0009-0009-2846-0467
Abstract - Traditional financial metrics for startup companies are often scarce due to the limited availability of public financial data. This paper proposes a novel approach to building leading economic indicators for startups by leveraging transactional data from wire transfers, credit card transactions, and Automated Clearing House (ACH) payments. These data sources provide rich, granular insights into the financial activities of startups, offering a proxy for their operational health and growth trajectories.
The methodology involves clustering transactional data based on sender and receiver information to identify spending patterns. Outgoing transactions are categorized into key expenditure categories such as payroll, marketing, legal, computing, and operational costs. By analyzing the distribution and frequency of these expenses, this framework provides a comprehensive view of a startup's resource allocation and financial priorities. In addition, incoming transactions from various channels are used as a proxy for sales or revenue, enabling the development of dynamic indicators of financial performance.
This approach bridges the gap in financial visibility for startups, providing timely and actionable insights into their economic activities. These leading indicators can serve as early warning signals of financial distress or as predictors of growth, offering immense value to investors, analysts, and policymakers. The study highlights the potential of transactional data analytics in enhancing transparency and decision-making in the startup ecosystem, paving the way for more robust analytical frameworks in a traditionally opaque domain.
Index Terms: Transactional Data Analysis, Wire Transfers, Credit Card Transactions, ACH Payments, Startup Financial Health, Spending Patterns, Revenue Proxy, Data Clustering, Expense Categorization, Payroll Analysis, Marketing Expenses, Legal Costs, Computing and Operational Costs, Startup Growth Metrics, Financial Visibility, Early Warning Signals, Startup Ecosystem Analytics
DOI: 10.55041/IJSREM6783
