How AI and automation reduce compliance costs

Regulations are created to safeguard customers and markets, but they may be challenging and costly to follow. The most expensive compliance requirements fall on highly regulated sectors such as financial services and life sciences. According to Deloitte, compliance expenses for banks have risen 60% since the 2008 financial crisis, and 50% of financial institutions spend 6 to 10% of their income on compliance, according to the Risk Management Association.

Artificial intelligence (AI) and intelligent automation procedures like RPA (robotic process automation) and NLP (natural language processing), among others, can help organizations achieve regulatory compliance while saving money. Here's how:

Regulation changes 

A financial institution may need to process up to 300 million pages of new regulations in a single year, disseminated from multiple states, federal, and municipal authorities across numerous channels. The manual labor of gathering, categorizing, and interpreting these changes and mapping them to the relevant business sectors is extremely time-consuming.

RPA can collect regulatory modifications, but the regulations must also be understood and adapted to business processes. This is where sophisticated OCR (optical character recognition), NLP, and AI models come in handy.

OCR can transform regulatory texts into machine-readable data. NLP is then used to analyze the texts, recognizing complicated statements and complex regulatory jargon. AI models can use the outputs to offer alternative decisions based on similar instances, filter new regulations to identify those relevant to the company, and assess fines according to past incidents. These features can save an analyst a lot of time, resulting in cost savings.

Regulatory reporting

One of the most time-consuming aspects of regulatory reporting is figuring out what must be reported, when, and how. This necessitates analysts to study and interpret the rules, write text on how the regulations apply to their business and translate it into code to receive relevant data.

AI can also rapidly categorize and understand unstructured regulatory data to define reporting restrictions, interpret it based on prior rules and situations, and generate code that activates an automated process to access multiple company resources to produce the reports. This method of regulatory intelligence is becoming more popular, especially in the financial services and the life sciences sectors, where new product registrations need to be submitted.

Transaction monitoring

Conventional rules-based transaction monitoring systems are prone to generating excessive false positives in financial services. False positives can reach 90% in some situations, with each alert requiring a compliance officer's attention. AI can help reduce the number of false compliance alerts and minimize review costs by integrating them into legacy transaction monitoring systems. Legitimate high-risk problems can be brought to a compliance officer’s attention, while automation can handle non-issues immediately. These resources can be reallocated where they can produce more value if compliance staff are committed to high-risk identified transactions. AI may also be used to modify existing rule engines and monitoring systems as new patterns emerge, allowing them to adapt.

Background and legal checks

Banks must conduct investigations to ensure new consumers are legitimate and stay legal throughout their relationship, as it is important to curb criminal activity and money laundering. Background checks can take 2 to 24 hours at the bank's discretion, depending on the risk level of certain persons. Much of this time is spent reading papers, checking databases, and examining media outlets. AI and automation automate much of this work. Bots can crawl the web for content regarding a customer and utilize sentiment analysis to identify harmful material. NLP technologies can search court papers for indicators of criminal activity and media mentions that are most relevant to investigate.

Reviewing the marketing material

In highly regulated environments, marketing materials must be compliant too. On the other hand, dealing with the never-ending supply of fresh content may be time-consuming. The pharma sector’s trend toward personalized marketing material is increasing compliance expenses at an alarming rate, as compliance professionals must ensure that every piece of content is in line with drug labels and rules. While scaling these methods requires considerable money, AI is now used to scan content and determine compliance more quickly and efficiently. In certain circumstances, AI bots are even being utilized to construct legal marketing text.