Can Artificial Intelligence (AI) help Banks with Regulatory and Statutory Compliance?

Following the 2008 global financial crisis, there has been a 500% increase in banking and financial sector regulations. For larger financial companies, the cost of maintaining regulatory compliance is around $10,000 for each employee, and global banks are spending close to $200 million each year on compliance.

Additionally, banks are paying fines of up to $10 billion (in 2019) for non-compliance with anti-money laundering (or AML) regulations. Banks are incurring higher operating costs of up to 60% to keep up with their regulatory and statutory compliance.

U.S.-based financial companies must deal with regulations from at least 10 financial regulators, including the Securities and Exchange Commission and the Federal Reserve. Effectively, banks and financial companies must comply with multiple rules floated by these regulators to safeguard their consumers.

Financial compliance and regulation-related problems are associated with data problems. To this effect, Artificial Intelligence (AI) has the potential to play a prominent role in better data management that can ensure improved regulatory compliance. Let us see how.

In the post-2008 world, financial regulators and supervisors are challenged to implement robust regulatory policies to safeguard banking customers and investors. This involves constant efforts on their part to ensure proper compliance and transparency in financial transactions and processes.

On their part, human regulators are required to perform multiple functions, including:

• Document their completed work periodically to financial authorities.
• Monitor the constant changes in regulatory requirements and frameworks.
• Take legal action against financial defaulters and prevent fraud attempts.
• Keep track of the growing number of financial companies and individuals (under their regulatory domain).

The need for regulatory technology is linked to the limitations of human effort and other financial constraints companies face. With its ability to analyze and process large volumes of financial data, AI technology is best suited to make regulatory compliance easier and more complete. Further, AI-based regulatory technology can help financial firms identify their regulatory requirements and trace them end-to-end with their risk and compliance taxonomy.

How can AI technology help in automating and improving regulatory compliance? Let us look at a few use cases in the next section.

Using AI-based automation, this technology can reduce the burden of financial services companies and transform regulatory compliance in the long run. Here are 5 business areas where AI is set to play a crucial role:

Financial regulators need to manually handle thousands of compliance documents to implement regulatory changes, which also requires coordination among various business functions. Along with a load of financial documents, financial companies need to comply with repetitive tasks, all of which can now be automated using AI capabilities like natural language processing (NLP) and intelligent automation.

NLP can extract relevant information and streamline regulatory change management by analyzing and classifying documentation. This enables financial companies to understand the constantly evolving regulatory environment better.

Going by this Forbes report, false positives in the regulatory process in the banking domain can exceed 90%, mostly due to their traditional methods. Regulatory compliance executives have to review these false positives, costing time and effort and adding to inefficiencies and human errors. Additionally, human regulators need to address the growing number of ‘suspicious’ money laundering activities or fraud transactions, most of which are caused by suspicious activity reports (SARs) filed by regulated financial institutions.

All of these add to the growing number of false positives. AI-based solutions assist regulators in sifting through filed SARs by correlating the data with the bank’s client profiles, recorded transactions, and regulatory lists. An example is the Australia-based financial agency, AUSTRAC, leveraging AI tools to detect and prevent suspicious activities.

With the explosion of unstructured data in the financial domain, AI-based solutions enable banks to extract more actionable sense from this data, thereby reducing manual human efforts. This includes:

• Monitoring authoritative sources automatically for any changes in compliance-related rules.
• Performing regulatory research designed to emulate human intelligence.
• Measuring the impact of regulatory changes on internal bank policies and controls.
• Aligning organizational policies and processes to external regulatory obligations and rules.

Besides, AI-based automation can prevent financial losses of billions of dollars caused each year due to human errors. For example, a clerical human error by a Citigroup employee almost led to sending over $1 billion to the lenders of Revlon Inc. Errors like that could be “caught” and mitigated.

Effective May 2018, the European Union’s GDPR requires financial companies to provide their customers with a clear explanation of the data collected and how they plan to use the data. AI solutions can compare the company’s privacy documents to check if they comply with the existing GDPR norms.

Additionally, most regulations are long documents that must be read thoroughly. Most compliance regulators cannot review these lengthy documents or keep track of the latest changes. AI applications can simplify this process by extracting the relevant sections of the included text and delivering accurate insights on the data to drive more careful and relevant action.

AI tools are increasingly being used to detect and prevent financial fraud in money laundering, terror financing, and ATM thefts. According to a report by United Nations, money laundering accounts for nearly 2—5% of the GDP globally every year.

AI-enabled anomaly detection helps identify anomalies in financial data patterns and flag any event that may seem out of the normal. Besides that, AI and machine learning algorithms are effective in reporting any transaction of above $10,000. Among the successful case studies, United Overseas Bank, in partnership with Deloitte, was able to leverage machine learning to improve its fights against money laundering.