> For the complete documentation index, see [llms.txt](https://spathion.gitbook.io/spathion/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://spathion.gitbook.io/spathion/overview/problem-statement.md).

# Problem Statement

**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.

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**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.

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**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.

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* Without real-time data, AI models must rely on outdated datasets, leading to less accurate predictions and limited ability to adapt to changing economic conditions.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

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**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.