Financial Statement Solution Unpacked: How can AI help? - AI Simplified (part 2)

Financial spreading is the process of which information from a borrower’s financial statements are transferred to the banks financial analysis program for credit assessment. Previously, this process was done manually by having credit analysts input numbers into excel one by one. This leads to obvious issues in accuracy, efficiency, and human resource allocation. Thereafter, Optical Character Recognition (OCR) is used to replace manual efforts. OCR converts images into text and numbers available for electronic editing and machine usage.

What does a typical financial spreading process with OCR look like?

The bank takes the financial statements provided by the borrower and puts it through the OCR algorithm for conversion into analyzable data. The data is further standardized manually by credit analysts, which are then input into the financial analysis software to derive the credit score for the client. Analysts then decide whether the application could pass through the assessment for further processing.

This is inefficient!

First off, the capabilities of traditional OCRs are very restrictive. First off, traditional OCR, as automated as it may seem, can be inaccurate. The accuracy of OCR reading depends highly on the input quality, hence a slight blur in the client’s documents would cause huge discrepancies. Moreover, it is unable to handle documents with complicated data formats, like those of disclosure notes and accounts. Furthermore. OCR solutions cannot interpret the meaning of the text extracted, hence are unable to produce standardized data formats.

The restrictive capabilities of OCR creates a large need for manual input despite the capitalization of such technology. Human effort is needed to crosscheck and validate the extracted data, handle complicated formatting, and standardize the output from OCR processing. Since analytics and insight generation are also outside the capabilities of OCR, manual analysis is to be conducted in order to proceed with credit assessment.

In conclusion, traditional OCR solutions only automate part of the process. Its limitations ultimately require intense manual input. Banks are looking to further streamline the process and improve efficiency without a big change in the process.

How can Financial Statement Robots solve these challenges?

Financial Statement Robots can not only tackle all of the challenges faced in using traditional OCR, but fully enjoy the benefits of a streamlined financial spreading process. Below are some of the more significant technological features of a Financial Statement robot, like those offered by SuperAcc.

HIGH ACCURACY with AI

AI is like a human brain, but with the ability to work on vast amounts of data at high speeds. By using SuperAcc’s AI robot, key items in financial statements can be automatically extracted from pdf/scanned copies at superhuman accuracy.

HIGH FORMATTING CAPABILITIES with DEEP LEARNING

Deep learning is a step further after machine learning, where the algorithm further takes its own outputs as inputs to learn from its performance. After extraction, its deep learning capabilities and classifier models enable streamlined standardization of data, by further associating between synonyms. Its capabilities span across all document types, including complicated disclosure notes.

With the technologies in place, the process would require menial manual input. The robot would highlight areas that require further validation, which is also the only manual input needed throughout. Coupled with that, financial health analysis followed by a business report according to the data can be generated based on the businesses needs, further streamlining the procedure. Furthermore, companies like SuperAcc offer customized solutions. The automation process robots are built based on the existing business processes and analytical metrics of the client company.

So what?

With the shrink in proportion of manual input required, the accuracy and speed of the output generation experience a significant improvement.

With a high proportion of the process automated, several further impacts were realised. The scalability of such robots allow banks to flexibly adjust their capacity to cater to greater demand in the peak season. Consistency is ensured as the subjectivity of human input is removed and replaced by objective algorithms. This new system could also accommodate for business changes without huge costs in training and reengineering.

With higher accuracy, speed, consistency and flexibility, the service quality provided by the credit assessment team raises banks’ competitiveness by providing faster and more accurate loan applications.

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