While regulations are created to protect consumers and markets, they’re often complex, making them costly and challenging to adhere to.
Highly regulated industries like Financial Services and Life Sciences have to absorb the most significant compliance costs. Deloitte estimates that compliance costs for banks have increased by 60% since the financial crisis of 2008, and the Risk Management Association found that 50% of financial institutions spend 6 to 10% of their revenues on compliance.
Artificial intelligence (AI) and intelligent automation processes, such as RPA (robotic process automation) and NLP (natural language processing) can help drive efficiencies up and costs down in meeting regulatory compliance. Here’s how:
1. Use RPA and NLP to manage regulation changes
In a single year, a financial institution may have to process up to 300 million pages of new regulations, disseminated from multiple state, federal, or municipal authorities across a variety of channels. The manual work of collecting, sorting, and understanding these changes and mapping them to the appropriate business area is extremely time consuming.
While RPA can be programmed to collect regulation changes, the regulations also need to be understood and applied to business processes. This is where sophisticated OCR (optical character recognition), NLP, and AI models come in.
- OCR can transform regulatory texts into machine-readable texts.
- NLP is then used to process the texts, understanding convoluted sentences and complex regulatory terminology.
- Next, AI models can leverage the output to provide options for policy changes based on similar past cases and filter through new regulations to flag those relevant to the business.
All these capabilities can save an analyst a significant amount of time, thereby reducing costs.
2. Streamline regulatory reporting
One of the biggest time drains in regulatory reporting is figuring out what needs to be reported, when, and how. This requires analysts to not only review the regulations, but interpret them, write text on how the regulations apply to their business, and translate it into code in order to retrieve the relevant data.
Alternatively, AI can quickly parse unstructured regulatory data to define reporting requirements, interpret it based on past rules and situations, and produce code to trigger an automated process to access multiple company resources to build the reports. This approach to regulatory intelligence is gaining traction to support Financial Services reporting as well as Life Sciences companies where submissions are required for new product approvals.
3. Shorten the review process for marketing material
The process of selling in highly regulated markets requires marketing material to be compliant. Yet, the process of approving the continuous flow of new marketing materials can be burdensome.
Pharma’s trend toward personalized marketing content is driving up compliance costs at an exponential rate as compliance officers need to ensure that each piece of content is consistent with drug labels and regulations. Because adding manpower to scale these strategies comes with a significant cost increase, AI is now used to scan content and determine compliance more quickly and efficiently. In some cases, AI bots are even being used to edit and write regulation-compliant marketing copy.
4. Reduce errors in transaction monitoring
Traditional rules-based transaction monitoring systems in Financial Services are prone to producing excessive false positives. In some cases, false positives have reached 90%, with each alert requiring review by a compliance officer.
By integrating AI into legacy transaction monitoring systems, erroneous compliance alerts can be minimized and review costs reduced. Issues that are deemed legitimate high-risk can be elevated to a compliance officer while those that are not can be automatically resolved. With compliance officers only working on high-risk flagged transactions, these resources can be redeployed where they can add more value. As new trends are identified, AI can also be used to update traditional rules engines and monitoring systems.
5. Perform background and legal checks
To limit criminal activity and money laundering, banks need to perform due diligence to ensure new customers are law-abiding and remain that way throughout the relationship. Depending on the risk level of certain individuals, background checks can range from two to 24 hours. Much of this time is spent collecting documents, checking databases, and reviewing media outlets. AI and automation can streamline this process. Bots can be used to crawl the web for mention of a client and leverage sentiment analysis to flag negative content. NLP technologies can scan court documents for signs of illegal activity and media mentions most relevant for analysis.