DeepGenius AI Test Drive for Enterprises - Experience How AI is Applied in Real-World Financial Accounting

AI Test Drive for Business Executives and Enterprise Users

Business Goals:

Apply Artificial Intelligence in financial close processes and intelligently automate the accounting rules to instantly close financial books and disclose financial statements.

Select Data

Extract Financial Data From SAP
Extract Financial Data From Oracle
Extract Financial Data From Microsoft

Run Model(s):

Run AI Model One
Model One Objective: Balance sheet validation: predict whether the given financial transaction is valid or invalid.
Run AI Model Two
Model Two Objective: Within the given transactions, identify which ones are intercompany transactions.
Run AI Model Three
Model Three Objective: Match buyer and seller intercompany transactions simultaneously. For every AP transaction, identify and pair its corresponding AP transaction.
Run AI Model Four
Model Four Objective: Determine if booking/entry transaction amounts between buyer and seller.
Run AI Model Five
Model Five Objective: Eliminate the intercompany transactions (reverse the intercompany transactions) to bring the transaction amount to zero.
Run all Financial Close Processess

Our Enterprise AI Test Drive Architecture

An enterprise AI system should interoperate with your existing legacy systems and ERP applications

Enterprise AI architecture:

Enterprise AI architecture is beyond building machine learning and deep learning models. It involves developing several architectural components and making them communicate with each other. Enterprise AI architecture starts with business process architecture, followed by data architecture, then finally AI and technology architecture.

Enterprise AI Architecture Key Components:

  • Business Process Architecture
  • Data Architecture
    • Big Data Architecture
    • IIoT Architecture
  • AI Architecture
    • Microservices Architecture
  • Application Architecture
    • System Interfaces
    • ERP Applications
  • Technology Architecture
    • Cloud
  • Security Architecture

Microservices Architecture

Adapting microservices for AI Analytics Engineering and model engineering is an important architectural decision. Microservice increases the reusability of code block, and it enables loosely coupled services to be called across AI models and across AI solutions.

Key Microservices:

  • Get Training Data Service
  • Get Test Data Service
    • Get Data From SAP
    • Get Data From Oracle
    • Get Data From YouTube
    • Get Data From Salesforce
    • Other services
  • Get Model Service
  • Run Model Service
  • Save Model Service
  • Save Model Outcome Service
  • Preview Model Outcome Service
  • Other services

Microservice Architecture for Artificial Intelligence

Loosely Coupled Services to Quickly Deploy AI at an Enterprise Scale