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Early 90’s was the time when regulatory technology popularly known as RegTech was used by the Financial Institutions to streamline their day-to-day operations and to check controls and weaknesses.
With the advent of Time and Technology, organizations have also started using RegTech to minimize the errors in infinite compliances and to optimize their existing resources. However, since inception the technology results are dependent upon the quality of data and its availability, harmonization issues and its collaboration to get a desired output.
In order to give a desired output, RegTech Industry needs high quality of data and in abundance to process and devise means to minimize the errors and automate it. Some solutions are available to repair data easier but still a lot needs to be done in the long run. With the expansion in AI sector and the futuristic approach of almost all the companies to automate their day-to-day processes, time spent by existing Tech companies in repairing data needs to be reduced. This poses as a major challenge as the data in its nascent stage is required to be complete, accurate, consistent and valid. However, in realty data management itself is a challenge and entails a huge cost in terms of resources and time.
From centuries, the data available with us is in the form of documents which are required to be transformed into Machine Readable language for the BOTs to operate. That’s the reason we are seeing so many versions of documents being filed with regulatory authorities using XBRL (eXtensible Business Reporting Language). XBRL benefits at all stages of business reporting and analysis. Data from different sources can be compiled, processed, automated, compared, produced in various fashions and can be checked by software for accuracy.
So, the first step to overcome the problem of reliability/quality of data collected can be to standardize the data collection and validation process. This itself is a tedious task but with the advancement in AI, ML and other techniques, effective tools can be deployed so that lesser time is engaged in transcribing data and validating it and more on analytics and reporting. Standardization of taxonomies in XBRL or creation of a unique taxonomy that covers all financial reporting standards across countries may be considered to ensure parity globally.
In a nutshell, for making processes more automated for yielding desired results, data must be in abundant supply and of the right quality and may be some officers can be deputed for ensuring that the requisite valid data is entered into the system devoid of any data manipulations
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