The expandable AI Network

Transcription

1 The expandable AI Network

2 Issues of Enterprise Accounting Understanding the Current Problems in Accounting. Increasing Complexity Complexity of cases and of mapping to various internal resources rises. Limited Resources Workload increases while resources cannot be expanded easily. Complex Maintenance Legacy systems with a high need for costly maintenance (ERP, etc.). Quality Requirements Rising booking quality not to achieve with pure human capabilities. Increasing Volume Business growth leads to an expanding volume of invoices & bookings. Expensive Improvements System improvements are only achieved incrementally & under high cost.

3 Challenges of Accounting Automation Automating the Entire Accounting Process Offers a Great Potential but is Highly Complex. Complex Mapping Various systems need to be mapped, includivng law, SAP, supplier & employees to understand varying receipts. Inflexible Legacy Systems Legacy systems, such as SAP ERP are inflexible to advance the individual modules to automation. Changing Environment Ensuring the end-to-end workflow from scanning to booking into SAP under changing circumstances. {;} PDF JSON XML CSV EDI IDoc

4 Solution Overview Automating Financial Accounting & Reporting Continuous Improvement System accuracy learns from human interactions. Scalability Adaptable financial intelligence. Purchasing Sales Trust and Transparency Supported by distributed ledger technology. Auditing Reporting Anomaly Detection Automated information of anomalies in finance. Meaningful Interactions Humans interact in a meaningful manner. Modularization Each AI component can run as a partial system.

5 Component Example: Accounting Automation intac: Intelligent Accounting with Multiple AI Networks Recognition Aggregation Prediction Decision ERP Automated Accounting Automation with human-centered recommendation approach. Process Transparency Transparency allows for Peer Review & Anomaly Detection. Strategic Enabler Saved time applied in various new strategic projects.

6 1. Recognition Module Paper invoice Scan {;} JSON IDoc XML OCR Structure CSV EDI PDF Model Digital Invoice PDF Training

7 2. Aggregation Module Regulations Training Data {;} JSON XML CSV IDoc EDI Aggregated Data Digital Invoice New Data

8 3. Prediction Module Training Data Training New Data Model Prediction

9 4. Decision Module Prediction Business Rules ERP Review Backlog Review Moderation Feedback for Training Intelligent Assignment

10 Lorem ipsum dolor sit Training Process Training Data Test Data Model Hyperparameter Tuning Lorem ipsu

11 Changing the Accounting Routine Traditional vs. Automated Work Overload Routine Tasks Obsolete Data Paper-Based Rising volume/complexity cannot be absorbed by existing resources, mistakes are made. Exhausting routine tasks block human resources. Master data on suppliers is outdated and contains duplicates. Inefficient handling of physical invoices. High Quality Meaningful Tasks Control Ecological Automated accounting sets no limits to business volume. Human resources can be moved to strategic projects, supervision and approval tasks, job satisfaction increases. Data is up-to-date, unambiguous and ready to be analysed. Environmentally friendly digital accounting.

12 Time Saving Processing Time per Invoice A speed-up of over x can be achieved 3-7 days 5 days 1-3 days 2.5 sec Without Automation AI & Human AI Only

13 Cost Savings and Discounts Traditional vs. Automated Average cost reduction per invoice 5 12 Early Payments Discounts Discounts are difficult to capture due to work overload. Automation allows to save 2-3 % of the purchase expenses. savings of 50 % to 90(+) % depending on the efficiency of current processes Traditional intac e.g. with annual accounts payable of , up to can be saved

14 Hosting Lorem ipsum dolor sit intac Fits Your IT Strategy HANA On-premise Both training and prediction on-premise Hybrid GPU cloud training, on-premise predictions Cloud Full enterprise cloud support sit Lorem ipsum dolor

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