With regulations strictly enforcing data handling practices on companies handling personal data, there has never been a higher demand for revolutionary monitoring and auditing tools and sedulous security assessments. By using reliable solutions for data governance that integrate machine learning and data science analytical powers, organizations can gain a better understanding of the information they possess and help facilitate compliance. With the ideas gained from these solutions in hand and compliance with regulations at bay, organizations can begin making data-driven choices that will raise their business to the next level.
In 2019, 20 percent of business executives polled plan to incorporate AI across their businesses, according to PwC. In the next two years, 89 percent of responses from more than 150 compliance officers at banks, insurance companies, and capital market firms anticipate increased compliance spending. The trend focus of expenditure earmarked for regulatory compliance management is moving from people to technology. There is growing confidence that new technology can aid institutions to achieve high returns. The company needs a strategic approach and an informative understanding of how Regtech solutions can be deployed effectively to gain the most value and economic advantages.
Faced with continually changing regulations, financial institutions are facing a growing challenge in keeping pace and interpreting regulatory requirements properly and their impact. 64 percent of respondents feel that they do not have enough time to manage all their compliance needs, according to the 2018 Compliance Pulse Report. Adopting AI technology will benefit institutions significantly, not only in managing regulatory changes but also in leveraging their data in order to remain consistent and gain valuable insights.
Data is at the heart of compliance with regulations. Typically, regulatory delays in reporting are due to weak data and reporting processes. The Basel Committee on Banking Supervision (BCBS) implemented standard 239 (Principles for effective aggregation of risk data and risk reporting) providing institutions with a structure for data policymaking and infrastructure, aggregation of risk data and risk reporting. Regulators expect banks to provide end-to-end lineage of data (the ability to track data from source to report). It is daunting to manage and maintain this lineage as data passes through multiple systems and software. By automating and consolidating regulatory compliance processes, the adoption of Big Data and AI technologies can significantly enhance and enable data-centered compliance.
New regulatory theocracies can result to a more pragmatic approach to regulations; regulatory processes can be simplified to such an extent that regular regulatory reporting is a thing of the past as regulators can obtain data in real time and conduct compliance checks on the fly.
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