Compliance

Automation Compliance

Intelligent automation streamlines processes that were otherwise comprised of manual tasks or based on legacy systems, which can be resource-intensive, costly, and prone to human error. The applications of IA span across industries, providing efficiencies in different areas of the business.

  • Automotive: The automotive industry is impacted greatly by the improvements manufacturers can make by using intelligent automation. With IA, manufacturers are able to more effectively predict and adjust production to respond to changes in supply and demand. They can streamline workflows to increase efficiency and reduce the risk of error in production, support, procurement and other areas. With the use of robots they are able to reduce the need for manual labor and improve defect discovery, providing a higher quality product to customers at a lower cost to the business. For example, a Volkswagen engine production plant in Germany uses “collaborative robots" that work with the production team to handle a physically demanding step in the engine-assembly process. This helps prevent injury, speed processes, ensure accuracy, and ease the physical burden on employees.
  • Life Sciences: Drug production is highly regulated and requires precise calibration of equipment and measurement of product. It also requires a tremendous amount of data collection, collation, processing, and analysis. A drug trial cannot be considered successful without trusted analysis and results. A manual approach could lead to calculation errors and take volumes of resources and considerable manpower to accomplish.
  • Healthcare: The healthcare industry is using intelligent automation with natural language processing (NLP) to provide a consistent approach to data collection, analysis, diagnosis and treatment. The use of chatbots in remote healthcare appointments requires less human intervention and often a shorter time to diagnosis.
  • Insurance: With IA, the Insurance industry can virtually eliminate the need for manual rate calculations or payments and can simplify paperwork processing such as claims and appraisals. Intelligent automation also helps insurance companies adhere to compliance regulations more easily by ensuring that requirements are met. In this manner, they are also able to calculate the risk of an individual or entity and calculate the appropriate insurance rate.

Robotic process automation combined with AI can help automate compliance and free up executives to focus on high-risk issues.

Companies intent on driving efficiencies in compliance management have increasingly turned to robotic process automation (RPA), which helps deliver operational and cost efficiency, while improving quality by reducing human errors. But using RPA alone has its limitations.

It mimics human behavior, but it cannot learn from mistakes or evolve with changing business environments. As a result, technology-savvy organizations are gradually looking to enhance their automation efforts with artificial intelligence (AI) tools, such as machine learning and natural language processing (NLP). By 2022, 80% of RPA-centric automation implementations will derive their value from complementary technologies, according to Gartner. Gartner predicts organizations that combine AI and RPA technologies with redesigned processes will cut nearly a third of their operational costs by 2024.

Together, these technologies pose a great opportunity for adoption in a wide range of areas in the compliance function, which we explore in detail below. The results are increased efficiency and better quality that bring relief amid new regulations, conflicting laws across jurisdictions, hefty penalties for noncompliance, and pressures to incorporate more data than ever in compliance monitoring.

Automation hotspots in compliance

Based on client engagement experience, there are eight key areas in a compliance function that are prime candidates for automation, with varying degrees of “intelligence.”

  1. Vendor due diligence. This often involves many laborious tasks that are ideal for RPA implementation. Bots can be set up to automate clearly defined checkpoints (e.g., check against a pre-established list of banned vendors). Machine learning technologies can be used to integrate a much wider range of data sources, (e.g., sanction data and court records) and perform in-depth analysis to uncover risks that otherwise may not be obvious.
  2. Email and social media monitoring. RPA software can be set up to regularly scan corporate emails and public social media posts with predefined keyword searches intended to identify risk activities and relationships. However, deploying NLP can greatly enhance risk detection. NLP can be used with sentiment analysis tools that evaluate the emotion, tone and intent of messages. These tools can produce real-time heat maps of employee engagement.
  3. Anti-bribery and anti-corruption (ABAC). Standard, rules-based ABAC tests can be programmed in bots to analyze data and identify red flags in transactions (e.g., round dollar payments). But the range of data sources that can be accessed or analyzed can be limited if using RPA alone. Using machine learning, the bots’ risk assessment abilities can be greatly enhanced by integrating a much broader set of data sources and generating scores to indicate the level of potential risks.
  4. Complaint management. Companies can greatly enhance the handling of calls to ethics hotlines. Voice analysis detects negative emotions from both the customer and service representative, providing real-time feedback to employees that helps them resolve the call, or flags the conversation for escalation to a supervisor. Successful complaint resolution mitigates legal risks while trends can be detected by categorizing and analyzing complaints.
  5. Data protection and privacy. Technologies that automate the discovery, inventory and classification of sensitive data help reduce noncompliance risk. However, technologies often need machine learning algorithms to handle complex aspects of the tasks, especially when it comes to discovery and classification. Gartner predicts more than 40% of privacy compliance technology will rely on AI over the next three years, up from 5% in 2020.
  6. Time and expense compliance. Expense management tools are using RPA to automate simple checklist type of tasks such as matching credit card receipts to approved types of charges. Adding machine learning allows companies to detect irregular expenses and patterns, flagging them for human review. For example, machine learning algorithms can be developed to categorize expense policy violators based on their risk levels and send email warnings tailored to the problem severity.
  7. Regulatory changes. New regulatory or legal requirements can be managed more effectively with automation. For example, a global effort to phase out interbank offer rates (IBORs) means any contracts or transactions linked to IBORs maturing after 2021 will require contract amendments or fallback language. Many document intelligence tools can be used to automate a lot of the work and convert legacy contracts into digital formats that contract management software can process.
  8. Regulatory and management reporting. RPA bots can be used in straightforward data collection and cleansing tasks. It’s been a common practice to use AI and advanced analytics technologies to provide deep insights and uncover hidden risks in regulatory and management reporting. The sheer volume of reporting required by a compliance function makes this the most promising area for joining AI and advanced analytics with RPA.
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