By Swapnil Mishra - June 24, 2022 4 Mins Read
Increased use of AI can drive efficiencies and reduce costs in compliance management. Here’s what that means for CIOs in highly regulated industries.
Complying professionals can utilize automation tools rather than investing in additional solutions to decrease capital expenditures, expedite compliance, and increase flexibility.
These solutions enable businesses across various industries to automate repetitive procedures, speed up business processes to increase efficiency and production, lower costs, and eliminate errors. Enterprises can expand the possibilities of automation with cognitive capabilities by combining RPA and AI, thereby increasing business value and competitiveness.
Here are five ways in which the convergence of two cutting-edge technologies is reshaping enterprises and assisting them in realizing the enormous value in a crowded, competitive environment:
A financial institution may be required to process up to 300 million pages of new regulations broadcast by numerous state, federal, or local authorities through a variety of channels in a single year. While RPA can be programmed to collect rules changes, the regulations must be comprehended and applied to business operations. This is where sophisticated OCR, NLP, and AI models are employed.
OCR can transform regulatory texts into machine-readable texts. The papers are sorted using Natural Language Processing (NLP) to comprehend intricate sentences and sophisticated regulation terminology. Next, AI models can use the data to propose alternatives for policy adjustments based on similar events from the past and to indicate new regulations pertaining to the business. All of these features can save a significant amount of time for an analyst, hence decreasing expenses.
Among the most time-consuming aspects of regulatory reporting is determining what, when, and how must be reported. This necessitates that analysts not only analyze the regulations but also interpret them, compose prose describing how the requirements relate to their organization, and translate it into code to retrieve the appropriate data. Alternately, AI can rapidly read unstructured regulatory data to determine reporting needs, interpret it based on historical regulations and scenarios, and generate code to initiate an automated process that accesses numerous firm resources to make the reports. This approach to regulatory information is gaining hold in the few industries where filings for new product approvals are necessary.
The practice of selling in highly regulated marketplaces necessitates suitable marketing materials. However, the approval process for the constant influx of new marketing materials can be complicated.
Compliance expenses are escalating exponentially, as compliance officers must guarantee that each piece of tailored marketing content is compliant with medicine labels and regulations. AI is currently utilized to scan information and evaluate compliance more rapidly and efficiently, as adding personnel to scale these tactics would result in a considerable cost increase. In some instances, AI bots are employed to edit and compose marketing material that complies with regulations.
Traditional rules-based transaction monitoring systems in Financial Services are susceptible to generating excessive false positives. By incorporating AI into historical transaction monitoring systems, it can minimize false compliance warnings and cut review costs. Those issues judged legitimately high-risk can be escalated to a compliance officer, while others can be resolved automatically. With compliance officers focusing solely on high-risk transactions identified for review, these resources can be redeployed where they would provide more excellent value. AI can also be used to update classic rules engines and monitoring systems as new trends are recognized.
To prevent criminal behavior and money laundering, banks must conduct due diligence on new customers to guarantee they are law-abiding and will stay so throughout the relationship. Background checks can take between two and twenty-four hours, depending on the risk level of a specific individual. Most of this time is spent gathering documents, searching databases, and examining media outlets. Artificial intelligence and automation can streamline this process. Utilizing sentiment analysis, bots can crawl the web for mentions of a client and flag unfavorable information. NLP technology can examine court records for indications of unlawful conduct and the most pertinent media mentions for analysis.
Swapnil Mishra is a global news correspondent at OnDot Media, with over six years of experience in the field. Swapnil has established herself as a trusted voice in the industry, specializing in technology journalism encompassing enterprise tech. Having collaborated with various media outlets, she has honed her skills in writing about executive leadership, business strategy, industry insights, business technology, supply chain management, blockchain and data management. As a journalism graduate, Swapnil possesses a keen eye for editorial detail and a mastery of language, enabling her to deliver compelling and informative news stories. She has a keen eye for detail and a knack for breaking down complex technical concepts into easy-to-understand language.
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