An Illustrative Business Use Case and How AmyGB Tackles Such Use Cases

1. An Illustrative Business Use Case and How AmyGB Tackles Such Use Cases For the purpose of this illustration let's choose the Finance and Accounting reconciliation process. In most organizations, the reconciliation process is usually automated, using accounting software. However, since some transactions may not be captured in the system, human involvement is required to identify such unexplained differences. The basic steps involved when reconciling transactions include the following: 1. Compare internal cash register to the bank statement Identify any transactions in the bank statement that are not backed up by any evidence. 2. Identify payments recorded in the internal cash register and not in the bank statement (and vice-versa) The transactions should be deducted from the bank statement balance. An example of such a transaction is checks issued but that have yet to be cleared by the bank. 3. Confirm that cash receipts and deposits are recorded in the cash register and bank statement If there are receipts recorded in the internal register and missing in the bank statement, add the transactions to the bank statement. Consequently, any transactions recorded in the bank statement and missing in the cash register should be added to the register. 4. Watch out for bank errors Possible errors include duplication errors, omissions, transposition, and incorrect recording of transactions. Once the errors have been identified, the bank should be notified to correct the error on their end and generate an adjusted bank statement. 5. Balance both records Once any differences have been identified and rectified, both internal and external records should be equal in order to demonstrate good financial health. Now the next part is how AmyGB tackles such use cases: In each of the above steps there will be plenty of cases where digital records will not be available for an automated software to crunch through. Even if the data is available in a digital format, it will still need to be processed by a human operator since it will be in a multitude of formats and will most likely be unstructured. Think Receipts, Invoices, Purchase Orders, Bills, Salary slips or literally any other document that records a financial transaction. Some samples shown below

2. Clean but unstructured (a)

3. Financial information in document pipelines can come in any format. Also we aren’t interested in extracting just numbers. We would also need to extract subjective information that can be processed using NLP / business intelligence. Hence (a), (b) and (c) are all important in painting a complete picture As you can see above, extracting all this information is in itself a challenge. Sure, OCR technology is mature, but still can't handle background noise, poor quality images and regional languages (indic) with a competitive level of accuracy. Most widely used OCR technology also, is quite erroneous with regards to reading handwritten content. So a significant chunk still ends up in the manual processing pipeline. This is where we add "intelligence" to document processing. The whole journey begins with understanding and mapping current processes. This enables us to identify and craft solutions around major bottlenecks. Change is always slow and painful, so both sides need quick wins while ensuring that a good chunk of the objectives are also met. Hence the need to attack small chunks of the biggest problems first.