Currently, the financial statements – which include balance sheets, income statements, cash-flow statements, changes in equity, and rent rolls and which mainly come in scanned or PDF machine readable formats – require subject matter experts to review and reconcile these statements, extract relevant data, and include it in their credit rating, loan origination, and other financial processes. This manual extraction and reconciliation are expensive to scale and prone to human errors, thereby creating potential risks for downstream processes.
Our team of data scientists and subject matter experts (SMEs) first worked to understand the problem in depth. Next, Scry trained Collatio® – Financial Spreading on approximately 10,000 financial spreading documents to convert this data into an electronic format, recreate appropriate tables, reverse engineer various formulas in these documents, and then map the appropriate key-value pairs from each financial spread onto a template that can be fed to the credit model, loan origination, or other processes. Now that this has been trained, this AI-based product first classifies the incoming document as to whether it is one of the five document types mentioned above and then it extracts the data from these documents and reconciles it. This software uses more than 40 pre-trained proprietary algorithms, a pre-built financial ontology and appropriate business rules. It can ingest financial spreading documents in PDF, image, Excel, and CSV input formats and it provides workflow-based user interface for analysts to review and verify all the output information.
- 80% – Reduction in time and cost
- 90%+ – Accuracy achieved
- 100% – Customizable user interface for real time analysis