AI has emerged in the financial services industry as a powerful disruptor. And most players have hopped on to the AI bandwagon already. Very soon, it is expected that widespread adoption of cognitive systems and Artificial Intelligence will boost global revenues. An IDC report predicts revenue growth in 2020 could reach $47 billion. Financial organizations are increasingly adopting a learning-based approach to machine learning to increase their approach to surveillance and risk management based on algorithmic rules. In a remarkably short time, business applications have made exponential progress. The one exception is to collect and automate high-quality, reliable data from various sources that are continually changing.
Not surprisingly, this hinders well-tuned business processes' end-to-end performance. Data extraction, reworking, handling errors, and managing exceptions can easily account for 80 percent of an automated process's operating costs. However, such inefficiency does not make headlines, because companies see the cost as necessary, even as they are trying to save every dollar. While it has also made progress, such as XBRL (eXtensible Business Reporting Language), the world of financial services also faces these challenges. XBRL is a global standard for the exchange of business information, including the creation, extraction and exchange of financial and business information, as well as a taxonomy for reporting on US GAAP.
XBRL's biggest challenge is that it has little or no standard structure. The reason is that there are unique reporting needs in its many constituents and it must accommodate all of them. This underlines the ongoing challenge of financial world source data extraction. While major technology improvements have occurred, solutions for data extraction are still inadequate. The development cycle of software merely is not flexible or iterative enough to handle documents that vary widely in shape, format, and quality. Evidently, a new paradigm is needed for a business.