Five ways to reduce compliance costs with AI and automation

While regulations and rules are enacted to protect consumers and markets, they are often complex, making compliance with them costly and challenging.

Strictly regulated industries like financial services and life sciences must bear the heaviest compliance costs. Deloitte estimates that compliance costs in the banking industry have increased by 60% since the 2008 financial crisis, while the Risk Management Institute found that 50% of financial institutions spend only 6% to 10% of their revenue on compliance costs.

Artificial Intelligence (AI) and intelligent automated processes, such as RPA (Robotic Process Automation) and NLP (Natural Language Processing), can help improve efficiency and reduce costs to meet regulatory requirements. Here are five ways on how:

1. Manage regulatory changes with RPA and NLP

In one year alone, a financial institution may need to process up to 300 million pages of New regulations and these regulations are widely disseminated through numerous channels by multiple state, federal or municipal authorities, etc.

Those tasks that require manual participation, such as collecting, classifying, understanding changes, and mapping them to appropriate businesses, are very time-consuming.

Although RPA can collect system changes through programming, it still needs to be understood and applied to business processes. This is where sophisticated OCR (Optical Character Recognition), NLP and AI models come in.

• First, OCR can convert institutional text into machine language.
• Secondly, use NLP to process these machine languages ​​and understand intricate sentences and complex regulatory terminology.
• The AI ​​model can then leverage the output to provide options for policy changes based on similar past cases and filter through new regulations to identify business-relevant regulations.

All these functions or methods can save analysts a lot of time and thus reduce costs.

2. Streamline regulatory reporting

Determining the content, time and method of regulatory reporting is the most time-consuming. This requires analysts to not only read and reread the relevant systems, but also to explain them, write instructions on how they apply to their own business, and translate them into code so that relevant data can be retrieved.

Alternatively, AI can quickly parse unstructured regulatory data to define reporting requirements, interpret it based on past rules and circumstances, and generate code to trigger automated processes to access multiple company resources to build the report. This approach to regulatory intelligence is increasingly gaining acceptance to support companies such as financial services and life sciences that need to submit new product approvals.

3. Shorten the review process of marketing materials

In a strictly regulated market, compliance is required for marketing materials generated during the sales process. However, the process of approving new marketing materials that are constantly emerging can be cumbersome.

The marketing content trend of pharmaceutical companies is developing toward personalization. At the same time, this development is driving up compliance costs at an exponential rate, as compliance officers need to ensure that every piece of content is consistent with drug labeling and legally compliant. As the cost of adding manpower to scale these policies increases significantly, artificial intelligence is now being used to scan content and determine compliance faster and more efficiently. In some cases, AI bots are even used to edit and write regulatory-compliant marketing copy.

4. Reduce false positives in transaction monitoring

In the traditional rule-based transaction monitoring system of financial services, it is easy to trigger a large number of false positives. In some cases, false alarm rates have reached as high as 90%, with each alert requiring verification by a compliance officer.

By integrating AI into traditional transaction monitoring systems, false compliance alerts can be minimized and review costs reduced. High-risk issues identified as legitimate can be referred to the Compliance Officer, while those that are not legitimate can be resolved automatically.

Since compliance officers are only responsible for processing high-risk-flagged transactions, these resources can be redeployed to other areas of greater value. There is another new trend emerging where artificial intelligence can also be used to update traditional rule engines and monitoring systems.

5. Conduct background and legal checks

To limit criminal and money laundering activities, banks need to conduct due diligence to ensure that new customers are law-abiding throughout the relationship. Depending on someone's risk level, a background check may take anywhere from 2 – 24 hours. Much of that time was spent gathering documents, checking databases and reviewing media outlets.

Artificial intelligence and automation can streamline this process. Bots can be used to scrape customer mentions across the web and use sentiment analysis to flag negative content. Use NLP technology to scan court documents for signs of illegal activity and relevant media exposure.

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