In a highly regulated financial environment, financial institutions must remain vigilant against money laundering and terrorism financing. A US-based R&D company specializing in real-time data analysis engaged our in-house development team to deliver a custom Anti-Money Laundering (AML) solution along with Know Your Customer (KYC) aspect. The goal: streamline risk management, improve compliance, and enable secure, efficient account onboarding. The system was also designed to support future readiness for AI/ML-based enhancements and data-driven compliance insights.
Project Overview:
The client was looking for a tailored web-based platform that would enhance their AML compliance, particularly during the account opening and customer identity verification processes. It had to support dynamic rule-based decision trees, robust data capture, risk scoring, and second-level CIP (Customer Identification Program) reviews. Additionally, the system needed to offer integration capabilities with another account-opening platform for risk scoring and verification as add-on components. The architecture supports embedded machine learning models and real-time data analytics engines that power adaptive risk detection and intelligent automation.
Challenges:
- Manual customer verification slowed the AML compliance process
- Inability to dynamically screen customer documents and responses in real-time
- Lack of centralized workflows for second-level CIP reviews
- Absence of centralized workflow for AML reviews and compliance checks
- Inefficient customer record tracking and audit justification
Solution:
To achieve the client’s objectives, we developed a web-based AML KYC solution that leverages AI technology to enhance fraud detection, ensure compliance, automate customer identity verification, and improve operational efficiency. Our provided AML solution utilizes machine learning algorithms to analyze transactions as they occur, identifying any unusual patterns in real-time. It flags suspicious activity with high precision, cutting down on false positives, which means less manual review is needed.
Key Features Included:
- Dynamic Decision Trees to adaptively evaluate documents, risk scores, and KBA (Knowledge-Based Authentication) responses
- Custom Account Opening Forms based on the customer relationship type
- Integrated Risk Scoring from credit bureaus and external data providers
- Second-Level CIP Review Workflows to support due diligence for high-risk customers
- Searchable Customer Records with full-text search, approval justification tools, and audit trail
- Built-in Email & Web Browsing to streamline decision-making and compliance communication
- Real-time Data Analytics Layer for monitoring, classification, and reporting
- Enhanced security controls including data encryption, role-based access, and audit logs
Benefits:
- Accelerated customer onboarding with rule-based and intelligent verification
- Reduced false positives and improved risk classification using ML model
- Improved compliance reporting and audit preparedness
- Centralized management of account opening workflows
- KYC solutions for AML compliance support embedded AI/ML and data pipeline integrations
Customer Profile:
Founded in 2006 and headquartered in New York, USA, the client is a research and development firm specializing in real-time data analysis for dynamic Knowledge Management (dKM), Data Surveillance, and Business Intelligence. They offer a suite of products focused on Risk and Knowledge Management.
Technology:
- Frontend & Backend: Java 21, Spring Boot 3.x, Hibernate
- Reporting: JasperReports (latest version)
- Database: Oracle 23c, PostgreSQL 16, SQL Server 2022
- Middleware & Servers: Apache Tomcat 10, WebSphere Liberty
- Integration: RESTful APIs (JSON), Web Services
- AI/ML Capabilities: Embedded Python-based services, ML algorithms for risk scoring, real-time behavior analysis using data pipelines
Industry:
Finance – Risk Management & Compliance
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