Client
Global bank's trading operations
Goal
Resolve trading transaction breaks and related regulatory issues through expandable intra-company digital ledger system
Tools and technologies
Hyperledger Fabric 1.4/2.2, Java 8, Go Language 1.8, Kafka, Node JS, Microservices, OpenShift, Dockers, Kubernetes
BUSINESS CHALLENGE
A highly-manual, paper-dependent, trading and reconciliation process was causing the accumulation of a large number of daily transaction liquidity breaks, which had been cited by federal regulators and risked a billion dollar cost impact. The lack of a robust trade audit and reconciliation process to reduce liquidity breaks and operating costs led the bank to seek an immutable system that could record and unify financial practices and be expanded to other transaction areas.
SOLUTION
Iris' solution comprised a production-ready, configurable platform using microservices and blockchain-based digital ledger architecture. It employed Smart Contracts coded with requisite business rules to facilitate front office trade booking and trade reconciliation processes. RPA was utilized to automate data mapping and testing of transactions. Preventive controls were enabled by recording intra-company transactions at their initiation using uniform booking practices, and consequently guaranteeing the term of the trade. A multi-layered infrastructure was created to support real-time, batch streaming of differing file formats. The UX was enriched through Interactive UI and automated workflows.
OUTCOMES
Iris successfully introduced a global intra-company distributed ledger and trade reconciliation system that did not exist before. With self-executing contracts matching both sides of transactions prior to feeding downstream systems, the platform ensures complete integrity at the source and reduces time and cost for all transactions. The solution also achieved:
- 30% fewer liquidity breaks
- 70% improvement in operational efficiency due to the use of RPA
- 60% reduction in business-rules configuration time, due to the smart contracts
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Get in touchSoftware transformation gets compliance for bank
Client
A global investment bank
Goal
To have a unified functional validation system for FDIC compliance
Tools and technologies
SQL Server, Sybase, Data Lake, UTM, .NET, DTA, Control-M, ALM, JIRA, Git, RLM, Nexus, Unix, WinSCP, Putty, Python, PyCharm, Confluence, Rabacus, SNS, and Datawatch
BUSINESS CHALLENGE
SOLUTION
OUTCOMES
- Faster and more efficient internal analysis with highly accurate QFC open positions
- 100% compliance with timing and format of required daily QFC report submissions to the FDIC
- Significant decrease in exceptions before the platform went go-live and zeroed critical defect delivery post-go-live
- An intuitive UI dashboard reflecting the real-time status of critical underlying data volumes, leakages, job run, and other stats
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Get in touchIT modernization lowers costs for capital markets
Client
A major financial institution
Goal
Re-engineer key compliance application for greater efficiency
Tools and technologies
Oracle 12c, 19c, Elastic, Java and Kafka
BUSINESS CHALLENGE
SOLUTION
OUTCOMES
The solution improved the user experience, turnarounds, data quality, deal compliance, and risk management processes while lowering the costs. Major impacts of the solution include:
- Reduced system issues by 93%, while 7% tackled subsequently
- Reduced enterprise-level license costs significantly
- Improved advanced search features
- Reduced costs of multiple searches per document by $200,000 per year
- Enabled views generation for info responses in less than 3 seconds
- Reduced system maintenance and development efforts by 20%
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Get in touchPlatform re-engineering for operational efficiency
Banking & Financial
Re-engineering data extraction platform for increased efficiency
A legacy data extraction platform was limiting business efficiency and transaction processing capabilities. Iris system modernization and platform re-engineering platform services advanced the operational efficiency manifold.
Client
One of the top 20 brokerage banks in North America
Goal
Modernize an existing, licensed data platform to meet the increasing volume of transactions and product offerings
Tools and technologies
Python, Core Java, Oracle, ETL Framework, Apache Zookeeper, Anaconda, Maven, Bamboo, Sonar, Bitbucket
BUSINESS CHALLENGE
SOLUTION
OUTCOMES
- Quicker, real-time configuration and execution of 500+ jobs for loading trade feed
- Zeroed downtime, even during the trade reference data changes
- 15% faster onboarding of the new feed or data source
- Nearly 20% faster throughput for various critical feeds with parallel processing feature
- Reduced anomalies and duplication with improved consistency
- 35-40% savings in annual third-party platform/ module license fees.
- Standardized and streamlined onboarding processes and turnaround time, scaling the operations efficiencies
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Get in touchSFTR solution strengthened market leadership
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.
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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
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Get in touchReporting transformation with data science and AI
Client
One of the world's leading bank
Goal
Improve efficiency in disclosure and reporting
Tools and technologies
Python – SciPy, Pytesseract, NumPy, Statistics
BUSINESS CHALLENGE
The client relies upon a centralized operations team to produce monthly NAV (Net Asset Value) and other financial reports for its international hedge funds— from data contained in 2,300 separate monthly investment fund performance reports. With batch receipts of rarely consistent file formats – PDF, Excel, emails, and images— the process to read each report, capture key info, and, create and distribute new metrics using the bank’s traditional tools and systems was highly manual, time-consuming, error-prone, and costly.
SOLUTION
Iris developed a Data Science solution that rapidly and accurately extracts tabular data from thousands of variable file documents. Using a statistical, AI-based algorithm featuring unsupervised learning, it auto-detects, construes, and resolves issues for every data point, configuration, and value. Complex inputs are calculated, consolidated, and mapped as per predefined templates and downstream business needs, efficiently generating numerous, distinct, and required period-end financial disclosures.
OUTCOMES
- 90 - 95% reduction in operational efforts
- 99% accuracy in processing variable inputs
- Zero rework effort and cost
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Get in touchHow to transform your risk reporting mechanisms
Client
The client was a brokerage firm with a strong presence in the capital markets.
Goal
Improve risk reporting and calculations.
Tools and technologies
Dot Net, C#, Greenplum, JUX Proprietary Framework and HTML5.
BUSINESS CHALLENGE
SOLUTION
OUTCOMES
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Get in touchThe power of in-sprint automation
Client
A leading securities trading firm.
Goal
Build a cloud-based automation framework to test client’s trading platform.
Tools and technologies
C#, Ranorex, TestRail, Simulators and Selenium.
BUSINESS CHALLENGE
SOLUTION
OUTCOMES
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Get in touchCloud-native app opens new markets
Client
The world’s leading provider of trading services for fixed income products
Goal
Create an IT architecture to support growth across markets and products
Tools and technologies
AWS Cloud, Java, Springboot, React JS, React, Redis, Kafka, C#, Ranorex and Test Rails
BUSINESS CHALLENGE
The client, a market leader in bonds trading, was expanding to new markets, acquiring new businesses, introducing new products and adding features to existing offerings. To support its growth plans, it needed an agile, modern, cloud-based platform.
Some of the business needs the client wanted to address with the new solution were:
- How do we achieve scale with minimal latency in operations and service?
- How do we integrate new businesses seamlessly and without disruption?
- How do we roll out new features faster to improve customer experience and get a competitive edge?
- How can we use data to help customers make better trading decisions?
- How can we monetize the data?
As a solution partner, we had to not only create a new IT architecture for the client’s trading platform but also constantly re-engineer and improve the architecture to quickly meet emerging business needs.
SOLUTION
We deployed a scalable, highly available auctions solution on the AWS cloud using Java, Springboot, React JS, React, Redis, and Kafka.
Optimized algorithms now achieve best matching with minimal latency while offering full price transparency. Artificial intelligence (AI) and machine learning (ML) provide greater insight and real-time price discovery for specific asset classes.The new cloud-based architecture enabled the client to create products and monetize market data. Those products helped customers get accurate data in real-time to take better and faster trading decisions.
Test automation across the trade lifecycle using a combination of C#, Ranorex, Test Rails helped the client update user interfaces (UI) without reducing performance. It also eased integration linkages between the acquired solution’s frontend and the client’s existing backend.
OUTCOMES
The introduction of Agile methodology and the cloud-native application has helped the client significantly speed up time-to-market for new releases – it is now able to make releases several times a year.
The new IT architecture now allows the client to offer trading in Muni bonds (an acquired product) and U.S. treasuries (a new service). The solution also enables the client to support Chinese markets.
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Iris systems transformation improved infrastructure availability and optimized systems maintainability, enhancing customer experience.
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