{"id":5082,"date":"2022-09-05T11:29:35","date_gmt":"2022-09-05T11:29:35","guid":{"rendered":"https:\/\/xtract.io\/blog\/?p=5082"},"modified":"2025-01-21T08:50:06","modified_gmt":"2025-01-21T08:50:06","slug":"simplify-financial-processes-with-financial-data-extraction-tools","status":"publish","type":"post","link":"https:\/\/www.xtract.io\/blog\/simplify-financial-processes-with-financial-data-extraction-tools\/","title":{"rendered":"Simplify financial processes with financial data extraction tools"},"content":{"rendered":"\n

<\/span>What is financial data extraction?<\/span><\/h2>\n\n\n\n

Financial data extraction is the process of extracting financial data from unstructured or semi-structured sources. This process is tedious and time-consuming compared to extracting data from structured sources, as they are innately machine-readable. <\/p>\n\n\n\n

Meaningful insights derived from this extracted data are employed to make critical financial and business decisions. <\/p>\n\n\n\n

<\/span>Data challenges for financial services <\/span><\/h2>\n\n\n\n

Financial teams face several data challenges due to the increase in the unstructured data that needs processing. Let\u2019s take a closer look at these challenges and learn how to overcome them. <\/p>\n\n\n\n

Extracting data from disparate sources<\/h3>\n\n\n\n

Imagine the financial ecosystem within a huge corporation. There are several sources from which financial data<\/a> flows \u2014 balance sheets, invoices, cash flow statements, etc. These documents come in different and in unstructured or semi-structured formats. The financial team needs to extract and collate the data from these sources and develop insightful reports for decision-making.<\/p>\n\n\n\n

Ensuring high data quality <\/h3>\n\n\n\n

Though data is the new oil, it is available like water. Data is everywhere, but it is up to us to get a constant flow of high-quality data into our stream. Financial analysts and hedge fund managers are now looking for alternative data sources to help their business other than internal company data. These external data must undergo quality checks to avoid missteps or loss of time during the decision-making process. <\/p>\n\n\n\n

Handling large volumes of data<\/h3>\n\n\n\n

It\u2019s pretty clear that data is available in massive volumes and keeps growing every second.  Similarly, the amount of financial data processed every year is only increasing. This increase in workload is the main reason why process automation is crucial. Manually extracting and analyzing massive volumes of data is not considered smart work as it is time-consuming, expensive, and prone to errors.<\/p>\n\n\n\n

Adhering to regulatory requirements<\/h3>\n\n\n\n

In today\u2019s world, even a medium-sized organization operates from multiple global locations. The financial data and reports of each of these office branches should match the regulatory compliance of their region. This can be painstaking, as you should be up-to-date on these countries’ frequently changing regulatory norms. <\/p>\n\n\n\n

Integrating data into various software<\/h3>\n\n\n\n

Tableau, SAP, CRM, and ERP are some commonly used software in companies that need a periodic inflow of processed financial data. Manual extraction means your finance team should also manually input the data into each software delaying access to the data. This leads the software to work with historical data instead of real-time data. <\/p>\n\n\n\n

<\/span>How does the financial data extraction tool work?<\/span><\/h2>\n\n\n\n

Technology has given us the solution for most of our business (and life) problems. So, it\u2019s no surprise that the challenges mentioned above can be resolved using a financial data extraction tool that simplifies the whole process.<\/p>\n\n\n\n

The financial data extraction tools use Artificial Intelligence(AI) & Natural Language Processing (NLP) to extract data from disparate sources. As a subset of these technologies, deep learning and computer vision are used to improve process efficiency.<\/p>\n\n\n\n

Typically a financial data extraction tool performs the following steps, <\/p>\n\n\n\n

Extraction <\/h3>\n\n\n\n

Financial data extraction tools use taxonomy mapping and ML models to identify data fields, process semi-structured documents, extract the desired data, and present it in an easily digestible format. Now, you can also quickly extract data in batches (i.e., for specific periods) or in real-time.<\/p>\n\n\n\n

Evaluation<\/h3>\n\n\n\n

The quality of the extracted data is evaluated using the human-in-the-loop feedback mechanism of the financial data extraction tools. Further, these tools ensure that the financial data follows all the regulatory and compliance requirements. <\/p>\n\n\n\n

Integration<\/h3>\n\n\n\n

You can seamlessly integrate financial data extraction tools into your existing software (cloud-based or not), which will ensure a continuous flow of high-quality financial data into them. This integration will tremendously improve the time-to-insights, in turn accelerating business decisions. <\/p>\n\n\n\n

Observation<\/h3>\n\n\n\n

Once your extraction process is complete, the tool provides you with a quality report that evaluates the extraction process’s efficiency. It contains:<\/p>\n\n\n\n