Use of Artificial Intelligence in Supply Chain Finance

The processing can happen in both real-time or bulk mode. Input data can be structured, semi-structured or even unstructured with easily accessible upload formats like Word, PDFs or even images.

The system can ingest the data via email, file upload, Secure File Transfer (SFTP) or even integrated with a 3rd party application. After the data has been ingested, the system captures the data, which can be either image or text-based.

Once the data has been captured, the system classifies it; in the case of an image, the data is enhanced to text through a Machine Learning/Neuro-Linguistic Processing layer, allowing one to view the captured fields online.

The system then validates the field and data type as well as the field format, performs the necessary de-dupe checks and geography specific checks, verifying all purchasing data, master data and tax data and completes any exception handling.

Post verification, the next step in the process is the approval phase; it handles invoice approvals and auto-initiates workflow post-initial approval; not only that, but it also manages the approval hierarchy.

All this gets clubbed into an integration layer, a cloud-Native API-ready solution that can seamlessly integrate with the business process, supporting a full range of output formats including Json,CSV/XLS, TXT, PDF and API.

These steps help in getting your data validated and remediated through contextual filtering and machine learning, eliminating paper, saving time and cost, improving accuracy and reducing risk.

An extended use of AI in supply chain finance is fraud detection. AI-powered fraud detection systems can analyse data from multiple sources, including invoices, purchase orders, and shipping records, to identify anomalies that may indicate fraudulent activity. It helps prevent fraudulent transactions e.g. by detection of duplicate invoices and hence protects buyers and suppliers from financial losses.