We need a strong technical candidate.
"Someone who can gather and integrate the right data. Someone with experience in IBM OpenPages, Archer and legacy IDN NLE data (a plus but optional). It's integrating customer-level transaction data."
IBM OpenPages is an AI-driven, highly scalable governance, risk, compliance (GRC) platform designed to help organizations identify, manage, monitor, and report on risk and regulatory compliance across the enterprise. It centralizes siloed risk management functions into a single environment, serving as the foundation for a company's enterprise risk management(ERM) efforts.
Experience with IBM OpenPages - provides tools to streamline compliance processes, ensuring that financial institutions can adhere to regulatory requirements efficiently. By integrating risk management and compliance functions to aid in the detection and prevention of money laundering activities to support organizations in maintaining financial integrity.
Archer is an integrated risk management (IRM) platform that assists organizations in managing governance, risk, and compliance (GRC) processes. It offers a centralized solution for handle policies, controls, risks, assessments, and efficiencies across various business functions.
Experience with Archer, integrates with external transaction monitoring systems, providing a comprehensive view of compliance risk management. The integration facilitates accurate reporting to regulators and supports informed, risk-based business decisions.
Integrating NLE in AML operations can streamline compliance efforts and improve detection accuracy, ultimately helping financial institutions respond to risks more effectively.
NLE (Natural Language Engineering) supporting AML (Anti-Money Laundering) refers to the application of natural language processing (NLP) and machine learning techniques to help detect, prevent, and report money laundering activities within financial systems.
And here’s how NLE data can support AML efforts:
1. Transaction Monitoring: NLP can analyze vast amounts of unstructured text from transaction descriptions, financial reports, and communications to identify patterns that may indicate suspicious activity.
2. Customer Due Diligence: NLP tools can process documents for know-your-customer (KYC) processes, enabling automated extraction of relevant data and verification of information.
3. Watchlist Filtering: NLE can help financial institutions scan large datasets, like sanctions lists or politically exposed persons (PEP) lists, and flag entities with potential connections to money laundering.
4. Enhanced Suspicious Activity Reports: NLP can assist in generating Suspicious Activity Reports (SARs) by analyzing data and summarizing suspicious transactions, making it easier for compliance teams to investigate further.
5. Pattern Recognition and Anomaly Detection: NLE algorithms can detect unusual behaviors by processing text and transactional data, helping identify and flag potential cases of money laundering
