Prachit Parikh is passionate about solving business challenges using technology. As a Technical Business Analyst, he is known for leveraging the power of technology, including artificial intelligence, as a bridge to drive business growth and operational efficiency. Regulatory reporting in banking has always been prone to human errors and rife with inefficiencies. Parikh discusses how AI can shape the future of regulatory reporting in banking and ensure compliance, providing a more streamlined data collection process, enhanced analytics for risk management, and more transparency and accountability during audits.
A recent report by Forbes Business suggests that “current generative AI and other technologies have the potential to automate work activities that absorb 60 to 70 percent of employees’ time today.” Generative AI learns patterns from large amounts of data. When applied to bank regulatory reporting, financial data can be aggregated, cleaned, and validated in less time and with less administrative effort. AI algorithms can be programmed to track data as it’s generated and provide continuous, real-time monitoring of key metrics needed to discover inconsistencies or anomalies in data.
AI in Banking: Power to Revolutionize Regulatory Reporting
Faster report generation
Using AI technologies like Natural Language Generation (NLG) to transform raw data into structured narratives that are readable and compliant will automatically generate reports that meet regulatory standards. This process in itself will reduce the time and effort needed for report creation, allowing compliance officers to focus on higher-level tasks. And, as the regulatory environment evolves with new rules and amendments frequently introduced, AI-driven reporting systems can be designed to adapt to regulatory changes automatically, again reducing manual labor and increasing efficiency.
Better risk management
Using AI’s predictive analysis tools, banks can explore different financial scenarios and forecast stress testing outcomes, determining whether a bank has enough capital to withstand a negative economic shock or other critical components of regulatory requirements. Prachit Parikh even sees how machine learning models can be trained to detect unusual or suspicious transaction patterns, reducing the risk of non-compliance due to fraud or other violations. The system can flag these outliers for further investigation, streamlining the compliance process when anomalies are discovered.
AI simplifies complex regulatory reporting
Throughout his career as a developer of innovative tools and systems to streamline business processes, identify and reduce gaps in cross-functioning teams, and implement IT strategies that align with business and regulatory goals, Prachit Parikh has always been excited about the possibilities of AI in reducing the complexity in regulatory reporting. AI-powered systems can automatically aggregate and organize vast amounts of data from various departments, reducing human errors and better ensuring that banks meet stringent regulatory standards confidently.
Improved response to compliance inquiries
Regulatory reporting agencies require an audit trail to verify that all reporting procedures were followed correctly and to guarantee that the data used for reporting is accurate and hasn’t been tampered with. These same audit trails are necessary to avoid legal penalties and reputational damage, especially in cases of hidden money laundering or terrorist financing. AI in regulatory reporting not only improves transparency and accountability, but can also speed up the response time. When a compliance query is submitted, AI-driven chatbots can quickly retrieve relevant data based on the specific information requested, significantly reducing the time banks spend responding to regulator questions.
Reduced costs through operational efficiency
When administrative tasks are reduced, businesses always experience reduced costs. Automated data processing, report generation, and compliance monitoring all work to lower the need for manual labor, leading to significant cost savings. Banks can reallocate administrative resources to focus on growth and innovation rather than purely compliance-related tasks. When banks experience both increased accuracy and speed in regulatory reporting, the result is the avoidance of costly fines and penalties associated with non-compliance.
Future challenges to consider
While the benefits of integrating AI into regulatory reporting are numerous, several challenges must be addressed. With AI handling sensitive financial data, it’s crucial to ensure robust data privacy and security measures are in place to protect customer information. As a business IT developer, Prachit Parikh also sees where ethics must be considered to ensure AI-based models are free from bias, especially when dealing with sensitive regulatory information.
As AI advances, its potential to reshape regulatory reporting in banking will only grow. With automated data processing, enhanced analytics, and real-time monitoring, AI can make reporting faster, more accurate, and cost-effective. However, successful integration requires banks to address concerns about transparency, security, and regulatory approval.
The future of regulatory reporting in banking lies in leveraging AI to build a more resilient, efficient, and compliant system. As regulatory demands evolve, banks that proactively adopt AI for compliance will be better positioned to navigate these challenges and stay ahead in an increasingly complex regulatory landscape.
Published by Elle G
