❓Problem Statement

Spathion addresses several critical issues in the current landscape of economic data collection and utilization

1. Trade Finance bottlenecks

• Trade finance companies often wait 3-5 days to verify trade data due to meticulous due diligence.

• This process involves cross-checking multiple documents and data sources to ensure authenticity and compliance.

• Manual checks extend the verification period and incur substantial costs, averaging $100,000 annually per company.

2. E-Invoicing Challenges for B2B Businesses

• B2B businesses spend an average of 240 hours per year manually uploading e-invoices to tax databases.

• This time-consuming process is due to manual and semi-automated methods required for compliance with e-invoicing mandates.

• Employees must meticulously enter invoice data into various tax portals, dealing with complex requirements across different jurisdictions.

• These inefficiencies lead to significant resource drains and risks of human error, highlighting the need for more automated and streamlined e-invoicing solutions.

3. Lack of Web2 data oracle

• No data oracle currently offers real-time economic data from Web2 businesses for building enterprise dApps, posing a significant challenge for developers and businesses.

• Real-time economic data is crucial for applications like automated financial analysis, dynamic risk assessment, and real-time decision-making, but its absence hinders the integration of Web2 businesses into the Web3 ecosystem.

• There is a pressing need for a robust data oracle to bridge this gap, enabling the creation of efficient, data-driven dApps and driving innovation across multiple industries with accurate, up-to-date information.

4. Real-Time Economic Data for AI Models

• AI models lack access to real-time economic data, hindering their accuracy and timeliness in providing insights for applications like economic forecasting and financial analysis.

• Without real-time data, AI models must rely on outdated datasets, leading to less accurate predictions and limited ability to adapt to changing economic conditions.

• This gap restricts businesses and financial institutions from fully leveraging AI for decision-making and operational optimization; real-time economic data would enhance AI model performance and foster innovation across sectors.

5. Significant Loss in Indirect Tax Revenue for Tax Departments

• Tax departments globally lose an estimated 30-40% of potential indirect tax revenue annually due to tax evasion, fraud, and inefficiencies.

• Reliance on manual and outdated tax collection systems exacerbates the problem, making accurate tracking and verification difficult.

• Implementing modern, automated tax administration solutions with improved data accuracy and transparency could recover significant lost revenue and enhance fiscal health.