How financial institutions can streamline compliance with AI
AI systems help make compliance processes more efficient and effective for financial institutions. Automation can reduce problems like human error and regulatory breaches.
By
Yuktesh Kashyap
Published: 25 Oct 2022
With new regulatory mandates knocking at the doorsteps of financial institutions, the need to ensure ongoing compliance is crucial. Many regulatory compliance practices in banking are new and evolving, with stringent implementation timelines that put greater pressure on banks. These institutions, both small and large, face challenges keeping up with the growth of regulations when using legacy approaches that lead them to risks.
To avert compliance risk, financial institutions can adopt AI technologies to streamline compliance processes and improve productivity. Thanks to its ability to process massive data logs and deliver meaningful insights, AI can give financial institutions a competitive advantage with real-time updates for simpler compliance management.
Ways AI provides more speed and security
Automating compliance processes for speedy decision-making. Typically, banks follow manual or traditional processes to collect data from different systems and create regulatory reports in that manner. However, these traditional processes are time consuming as they are not dynamically scalable or easy to integrate with other services. AI-powered solutions empower banks to automate data collection processes, improve the speed and quality of decisions and enhance their readiness to meet regulatory compliance obligations. For example, automating manual risk scoring enables financial institutions to make their systems fault tolerant and compliant with various regulations.
Ensuring faster and secure transactions. AI-based banking solutions use advanced ML techniques for extracting and standardizing data, including payment amount, accounts, history and other transaction details to enable a seamless and automated wire transfer. For instance, AI can suggest certain amounts at ATM transactions for quick withdrawal or suggest a credit card to use for a particular transaction. With AI, banks can optimize various calculations and reduce network latency for faster transactions.
This increased speed also comes with increased security. AI provides real-time fraud prevention to financial institutions by detecting fraudulent payments with real-time financial data analysis. AI's response rate for risk calculation is increasing by the day, empowering banks to get a clear picture of any anomalies or risks beforehand. AI also enables financial institutions to distinguish between fraudulent and legitimate transactions. With the spike in sophisticated cybercriminals, financial institutions need to ensure secure payments with AI systems that help financial data analysis.
Easy monitoring of regulatory change management. According to a Thomson Reuters survey of compliance professionals from 800 financial services firms worldwide, regulatory updates are coming in at an average rate of more than 200 per day. Financial institutions need to keep track of these changes and respond to them to prevent any penalties or risks. Through analyzing and classifying documentation, natural language processing (NLP) can extract relevant information and streamline regulatory change management. NLP-based AI solutions can help seamlessly monitor whether or not agents are following protocols to ensure there are no gaps. These systems can also identify processes impacted by a regulatory change to help financial institutions keep up with the change.
It is extremely important for financial institutions to identify anomalous behaviors and inconsistencies from a number of data points and sources to avoid breaches.
AI technologies reduce risk of regulatory breaches
It is extremely important for financial institutions to identify anomalous behaviors and inconsistencies from a number of data points and sources to avoid breaches. By mitigating these breaches, financial institutions can save themselves from theft, money laundering practices and more instances of monetary fraud. AI will help financial institutions track their transactions and financial history, including structured and unstructured data, to identify such anomalies. These AI-powered data collection and monitoring frameworks use AI-based pattern recognition and anomaly detection to track and flag previously undetected risks and patterns, thereby eliminating manual processes.
Additionally, a large number of banks are experiencing false positives, or alarms for legitimate transactions, in their compliance systems due to inaccurate methods. As their compliance systems generate thousands of false positives per day, they require human oversight, which increases inefficiency and human errors. Self-learning AI systems can quickly detect new fraudulent behaviors and course correct to reduce false positives. Driven by relevant data supplied by algorithms, AI technologies can prove to be effective in identifying frauds and reducing false positives. They mitigate human errors by detecting blind spots, reasonable errors and other aspects that may be missed by humans to keep compliance and regulatory risks at bay.
Provided that the pressure from new compliance and regulatory obligations is only expected to increase in the future, AI technologies greatly reduce workloads and dramatically cut costs for financial institutions by enabling compliance to be more efficient and effective. These institutions can then achieve more than just compliance with the law by actually creating value with increased profits.
About the author Yuktesh Kashyap is the associate vice president of data science at Sigmoid. He has almost a decade of experience in implementing machine learning-based decisions and monitoring solutions in financial services.
