1. Automated Monitoring and Reporting
AI systems can continuously monitor transactions and operations to ensure that they comply with relevant regulations. These systems can automatically generate reports and documentation required by various regulatory bodies, reducing manual effort.
A recent white paper from Moody’s notes that organizations are still in the early stages of using AI for risk management and compliance: Only 21% of IT leaders globally, particularly those in financial services, were in the trial or pilot phase.
However, early adopters have found that AI has helped them save money, reduce manual errors and improve efficiency (for example, by automating repetitive tasks such as monitoring for money laundering). A number of vendors, including Oracle and Google, are bolstering their services with AI-powered solutions that can help organizations better identify and mitigate AI and ML risks.
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2. Improved Risk Assessment
AI can analyze vast amounts of data to identify potential compliance risks that might not be evident through traditional methods. For instance, AI can identify whether a pre-emptive action should be taken earlier to mitigate a given risk.
“Using what are known as natural language processing (NLP) and large language models (LLMs), banks can uncover common patterns in fraudulent transaction requests. In-depth analysis of fraudulent transaction data, meanwhile, can help financial organizations build robust learning models for ML algorithms capable of finding problems as early as possible,” write CDW’s Larry Burt, a public cloud specialist in the Small Business Technology Solutions Group, and Rajiv Jain, field CIO/CTO for the financial services vertical, in a 2023 article.
“The use of AI solutions to quickly contrast and compare large volumes of current and historical financial data can help banks reduce potential liability by meeting due diligence requirements,” they write.
3. Enhanced Data Management
Since compliance requires handling large amounts of data and ensuring its accuracy and integrity, it’s no wonder that 63% of organizations using or piloting AI are doing so for data analysis and interpretation, according to Moody’s. AI can help organize, manage and safeguard this crucial information.
That said, in order to leverage AI fully, organizations must mature their data strategy so that data is managed and clean at every step. As financial services consider AI for compliance, they must ensure that their internal approach to data will not add barriers to AI adoption.
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4. Regulatory Change Management
The financial services industry experiences frequent regulatory changes, and keeping up with the latest guidance can be challenging. AI systems can be trained to track changes in legislation and formal rules, helping companies adjust their compliance strategies in real time.
In a recent article, Adrian Crockett with Microsoft Cloud wrote: “With hybrid intelligence, there are some tasks where a human does not need to be involved. End-of-day reporting to regulatory authorities, for example, is a human-capital intensive task in which augmented intelligence is being employed now to improve efficiency. But where a firm might have a huge volume of data, hybrid intelligence might be the better option. The key consideration is knowing when and where a human needs to be in the loop.”
5. Fraud Detection and Prevention
By integrating AI into their compliance systems, financial institutions can enhance their ability to detect and prevent fraud, a critical aspect of regulatory compliance.
As Databricks notes, AI can sift through structured and unstructured data to establish patterns and detect deviations from them, flagging inconsistencies for further scrutiny. “Anomaly detection algorithms can help businesses identify and react to unusual data points in multiple scenarios,” it says in a blog post. “A bank security system may employ anomaly detection for the identification of fraudulent transactions or non-compliant practitioners.”
Even though AI’s impact on the risk management and compliance space continues to evolve, financial services shouldn’t shy away from trying these solutions, especially in use cases where it aligns with an organization’s strategic mission.
