Client
A leading provider of market data and trading services
Goal
Support complex regulatory reporting with automated solution
Tools and technologies
Java, Spring Boot, Apache Camel, CXF, Drools BRE, Oracle, JBoss Fuse, Elasticsearch, Git, Bitbucket, Sonar, Maven
BUSINESS CHALLENGE
SOLUTION
OUTCOMES
- Generating additional revenue from extending the new reporting services to 17 firms.
- Beating the industry benchmark (~91%), achieving a higher transaction acceptance rate (~97%), and match reconciliation at the trade repository.
- Supporting a high throughput of 6 million transactions per hour which is scalable up to 10 million.
Contact
Our experts can help you find the right solutions to meet your needs.
Get in touchAnti-money laundering software saves $1M
Client
A top 5 global bank
Goal
Create a unified platform for anti-money laundering functions, analytics, and compliance implementations
Tools and technologies
Angular 5, Java, Open Shift, and DevOps
BUSINESS CHALLENGE
The client expanded its fraud and anti-money laundering (AML) monitoring functions, involving multiple lines of business and 15,000 employees. The scaled system led to the lack of standardization of frameworks and resultant adoption of disjointed, manual-intensive, and high-cost AML technology. The ongoing disconnect hindered the efforts of automating, consolidating; and implementing AML functions, enterprise analytics, and regulatory compliance efficiently throughout the organization.
SOLUTION
Iris optimized existing operations and technology investments by developing and implementing a unified point of access for the discrete AML functions, featuring micro-front-end architecture. Engineered to be horizontally scalable through containerization with common authentication and authorization gateways, the single user interface (UI) allows onboarding and control of multiple extended AML functions, including visualization of metrics.
OUTCOMES
- Hassle-free transition from multiple to a single UI
- Unified, streamlined user experiences with more effective sessions
- Creation of standardized deployment procedures for AML rules and applications
- Saving of nearly $1M on infrastructure costs
- Reduced infrastructure maintenance time
- Frictionless migration of applications to the cloud
Contact
Our experts can help you find the right solutions to meet your needs.
Get in touchAnti-money laundering: managing regulatory risks
Client
A leading global bank with operations in over 100 countries
Goal
Address data quality and cost challenges of legacy AML application infrastructure
Tools and technologies
Hadoop, Hive, Talend, Kafka, Spark, ETL
BUSINESS CHALLENGE
The client’s legacy AML application infrastructure was leading to data acquisition, quality assurance, data processing, AML rules management and reporting challenges. High data volume and rules-based algorithms were generating high numbers of false positives. Multiple instances of legacy vendor platforms were also adding to cost and complexity.
SOLUTION
Iris developed and implemented multiple AML Trade Surveillance applications and Big Data capabilities. The team designed a centralized data hub with Cloudera Hadoop for AML business processes and migrated application data to the big data analytical platform in the client’s private cloud. Switching from a rule-based approach to algorithmic analytical models, we incorporated a data lake with logical layers and developed a metadata-driven data quality monitoring solution. We enabled the support for AML model development, execution and testing/validation, and integration with case management. Our data experts also deployed a custom metadata management tool and UI to manage data quality. Data visualization and dashboards were implemented for alerts, monitoring performance, and tracking money laundering activities.
OUTCOMES
The implemented solution delivered tangible outcomes, including:
- Centralized data hub capable of handling 100+ PB of data and ~5,000 users across 18 regional hubs for several countries
- Ingestion of 30+ million transactions per day from different sources
- Greater insights with scanning of 1.5+ Billion transactions every month
- False positives reduced by over 30%
- AML data storage cost reduced to <10 cents per GB per year
- Extended support to multiple countries and business lines across six global regions; legacy instances reduced from 30+ to <10
Related Stories
Gen AI summarization solution aids lending app users
Learn more
Conversational assistant boosts AML product assurance
Learn more
Automated financial analysis reduces manual effort
Learn moreContact
Our experts can help you find the right solutions to meet your needs.
Get in touchCompany
