Common Category: Banking
Automation testing improves efficiency for bank
Model risk system’s automation testing improves efficiency
An international bank attains 100% stable, scalable, and reusable with automated Model Risk Management System (MRMS) testing.
A top international bank
To acquire a scalable, more efficient testing automation framework for the Model Risk Management System (MRMS)
Tools and technologies
Java, Selenium, Maven, TestNG, and Git
The client’s existing Model Risk Management System (MRMS) application framework was inefficient in handling functional testing aspects and risk scenarios. Built on redundant, hard to debug, and non-scalable code, the system was unreliable for model risk testing. The test cases and controls were maintained and executed in Excel, affecting the parallel workflow abilities and tempering the testing results for the bank. The manual process and repetitious code were affecting the quality of deliverables, and maintaining the system was costing much. Running automated, data-driven/ lengthy, and end-to-end test flows— was the prime challenge for the client.
Our automation and testing experts developed a lightweight and scalable framework that enabled 100% automated regression testing of the functional test cases for the model risk applications. Catering to the client’s specific needs, we built the new framework using a simplified, customizable code that separated the automation utilities and test functions. The solution eliminated manual effort aiming at faster test case execution and catching the testing issues at integration points. The traceable and easy-to-maintain code allowed seamless adaptability of multiple application areas.
The delivered solution helped the client to:
- Acquire a 100% stable, scalable, reusable, and fully automated test framework
- Reduce the maintenance time up to 90%
- Save $18,000 and 720 labor hours per year
- Identify approximately 20% more system defects of the existing system and address those post automation
Anti-money laundering software saves $1M
Unified AML proves to be a game changer
Global bank overcomes Anti-Money Laundering monitoring challenges and saves $1M in infrastructure costs with a unified front end.
A top 5 global bank
Create a unified platform for anti-money laundering functions, analytics, and compliance implementations
Tools and technologies
Angular 5, Java, Open Shift, and DevOps
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.
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.
The solution amplified efficiencies and reduced costs through the automated system and seamless exchanges of information. Significant outcomes included:
• 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
A playbook for banks on managing M&A integration
A playbook for banks on M&A integration
Efficient management of the complexities of disparate systems and data after an acquisition saves time and money.
Banks that have merged or acquired new businesses.
Manage migration and integration complexity post M&A.
Tools and technologies
The Iris business acquisition playbook for banks.
In a low-interest rate regime, achieving scale is the only way for banks to stay profitable. The top 25 banks are growing at a rate faster than rest of the pack. The search for profitability from scale is predicated upon their ability to ensure that operational costs do not grow linearly with business.
A significant part of this growth will come inorganically. Apart from M&As, brownfield expansion comes with banks selling off their books of business for reasons ranging from realigned strategic priorities to the more mundane need of raising cash. Any IT costs in absorbing the new book of work will negate the advantages of size.
Iris has been working with banking clients to create a business acquisition playbook outlining steps to insource with a migration and integration strategy. We have enabled clients to deal with post-merger integrations and create a single source of truth for transactional data and positions. The Iris team delivered solutions specifically tailored for applications in the loan origination and servicing space.
We have helped our banking clients:
- Consolidate multiple acquisition playbooks to create a single standardized framework for their lending business
- Define insourcing steps for business and technology teams and create a migration strategy with quantifiable recommendations and a reusable checklist for insourcing activities.
- Assess capability and readiness and help them choose from insourcing options:
- Achieve full migration of data and systems
- Achieve partial migration of systems and data migration and integration
- Manage data integration and connectivity for lending business.
We have helped clients achieve 50% savings in cycle time and cost for post-merger integration of business processes, application and data.
Anti-money laundering: managing regulatory risks
Data & Analytics, Risk & Compliance
Anti-money laundering programs: managing data, regulatory risks
A global multinational bank successfully managed large data volumes in its anti-money laundering program and protected clients and franchisees from regulatory risks.
A leading multinational bank.
Identify and mitigate risks related to anti-money laundering (AML) regulations.
Tools and technologies
Cloudera, Hadoop, Talend, Spark MLlib, MicroStrategy, Datameer and Sqoop.
Our client, a multinational bank, had a comprehensive global program for anti-money laundering (AML) to protect its clients and franchisees from the risks of money laundering, terrorist financing and other financial crimes.
To be able to do so effectively, they needed to deal with mounting volumes of data, which their existing applications could not handle. Their systems also generated a high number of false positives that increased the need for manual intervention.
We worked with the client’s global anti-money laundering program to develop a solution that provided them with consistent controls to identify AML risks and comply with relevant laws.
We incorporated a modern data lake architecture, a centralized data hub that allowed the processing of increasing volumes of data from around the world. The solution we built was capable of handling data in petabytes.
We helped the multinational bank build a data lake that could hold 8 petabytes of data, much more than its existing data applications allowed. Next, Iris cross-trained the client’s global anti-money laundering team to ensure efficient use of the data lake in line with its global anti-money laundering program.
Global regions covered
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