Financial service institutions strive to protect assets, identify risks, and reduce losses in the digital age. However, managing risk and regulatory compliance has become increasingly complex and costly.

From 2008 to 2015, regulatory change increased by 492%, increasing regulatory costs. The Risk Management Association found that the compliance cost for firms increased by 10% annually.

Organizations implementing artificial intelligence (AI) powered, cloud-based solutions are finding opportunities to catch fraudulent transactions and data breaches. These AI systems can exceed compliance standards and quickly detect suspicious activity without crippling company IT infrastructures and internal resources.

Successful AI implementation can address financial firms’ challenges, including hefty fines for non-compliance. The Securities and Exchange Commission issued 715 enforcement actions, making violators pay over $4.68 billion in penalties in 2020.

NTM can help your firm use AI technologies to protect customer data and business processes. Our team can help you identify risks in your infrastructure and upgrade it to meet compliance standards. We can let you know how your firm can benefit from AI as it addresses common operational challenges and systematic issues that banks encounter daily.

Effective Regulatory Change Management

Businesses must review many regulatory documents requiring adjustments when dealing with regulatory change management. Any regulatory changes set a chain of sections in other business divisions. For example, an asset manager has to restructure a fund to comply with new regulations, resulting in adjustments to other portfolios.

As part of complying with regulatory requirements, financial services reporting involves a variety of documents and repetitive tasks. Natural Language Processing (NLP) and Intelligent Process Automation (IPA) can automate the process of regulatory change management.

Using NLP and IPA can assist financial institutions and clients in staying current on regulatory changes. By analyzing and classifying documents, NLP and IPA can extract client information, product information, and processes affected by regulatory changes.

With AI’s ability to detect patterns in vast amounts of text, it can anticipate fines and other costs associated with the ever-changing regulatory environment.

Reducing False Positives

Companies can suffer from a high volume of false positives, where systems mistakenly flag a genuine customer’s transaction as suspicious. For example, IBM reports that they typically have high false-positive rates that exceed 90% due to old compliance processes.

Additionally, managers have to review these false alarms. These constant reviews can slow down their workloads and increase the likelihood of human error.

AI and machine learning (ML) can help compliance alert systems capture, extract, and analyze several key data elements. AI and ML can learn from compliance officers’ data to distinguish genuine fraud detection from customer transactions.

These automated actions can help business organizations comply with regulations and lessen the workload for compliance officers to evaluate every false positive alert.

cybersecurity risk management

Detect Fraud Prevention and Anti-Money Laundering Violations

For businesses to comply with regulations, they need the computational power to evaluate and process millions of transactions and customer data points. Consequently, they use anomaly detection algorithms to identify and address suspicious data points.

Machine learning models can learn regular patterns from large amounts of data to create an anomaly detection algorithm. It can identify inconsistent events that do not match the learned patterns.

For instance, the rules may require reporting and analyzing financial activities involving customers in sanctioned countries and transactions exceeding $10,000 under existing anti-money laundering (AML) policies.

However, some customers may devise AML schemes to not alert compliance systems to suspicious transactions, such as wiring money beneath the minimum of $10,000.

As a result, specific signals in the compliance system may not detect these transactions. Since programmers code the alerts and the alerts need regular updates, they may need to catch up with increasing fraudulent activities in the system.

Additionally, there could be a cause for error since a rule can signal unusual activity to a compliance officer based on the value of a single input parameter or feature.

Organizations can use AI applications and graph analytics to detect subtle patterns that can fly undetected by existing rule sets. Artificial intelligence can detect unusual anomalies and identify them as suspicious activities by learning which data sets are abnormal.

AI can monitor policy violations such as insider trading, money laundering, and other illicit activities. IT professionals can train ML algorithms to detect patterns of suspicious behavior over time based on historical data and past incidents reported by employees and customers.

Human Error Mitigation

Keeping track of transactions, the number of customers, and operating activities at large banks is a crucial responsibility of compliance officers. However, the daily volume of financial information can create confusion, resulting in human errors.

In 2020, Citigroup’s credit department employees made a clerical error of sending $900 million to Revlon Inc.’s lenders. Citigroup filed a lawsuit against the lenders in the wake of the mistaken transaction. Unfortunately, the organization could not recover the money from the lenders since the federal judge ruled the transactions were final and complete.

Artificial intelligence and machine learning applications can reveal unusual patterns and reasonable errors that may go undetected by employees. They can process information faster than humans and identify unique trends that could signal potential risks.

For example, if an employee makes several mistakes quickly, the AI can alert supervisors and managers and indicate a need for additional training.

Develop a Risk Management IT Solution with NTM

Using AI and ML tools to analyze unstructured data and connect real-time insights for rapid responses is a critical component of risk management. These tools enable financial services institutions to manage important risk issues with compliance, regulatory reporting, fraud detection, and anti-money laundering.

Automated risk management systems can now detect and alert employees to reasonable errors that could otherwise go undetected.

Any business can start solving the cost of human error using AI and automation with the help of NTM. Our team can scan your IT systems and set them up to future-proof your IT infrastructure and adapt to the ever-changing world of AI technology.

Call us today or complete our online form to help you better understand how your business can protect its big data using AI and ML tools today.


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