Event partner at Open Banking Expo Canada 2025
Canada’s open banking future is taking shape, and the Iris team is right there, contributing to the momentum.
We are proud to be an event partner at Open Banking Expo Canada 2025, contributing to key conversations on how API ecosystems are reshaping the future of financial services. Connect with our team at the expo on June 17, 2025, at the Metro Toronto Convention Centre in Ontario, Canada.
Iris Software is focused on bringing value and transformative technology to leading global banks and financial services (BFS) providers, including those in the personal banking and payments sectors.
Subramanian Viswanathan, Associate Vice President, Financial Services Practice at Iris Software, will moderate a Powerhouse Debate titled ‘Open Banking & API Banking: Unlocking the future of financial services’ with leaders from BMO, TD, RBC, and Scotiabank. “The future of financial services lies in building connected ecosystems where data moves securely, and value moves instantly.” - Subramanian Viswanathan.
For decades, Iris has accelerated the digital transformation journeys of major global and Canadian banks and financial services and payments enterprises. Connect with our experts to learn how our solutions in AI / ML, Application Modernization, Automation, Cloud, Data Science, DevOps, Enterprise Analytics, Integrations, and Quality Engineering improved clients’ data quality, reliability and scalability; platform and systems efficiency; user interfaces and experiences; insight extraction and decision-making; and regulatory compliance.
For more information on the benefits of our future-ready solutions, visit Iris Software Banking and Financial Services.
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How AI transforms risk management, enhances compliance
Ensure snag-free operations with accurate, timely regulatory reporting and without legacy drags.
How AI transforms risk management, enhances compliance
Ensure snag-free operations with accurate, timely regulatory reporting and without legacy drags.
Risk & Compliance
Experience
Cloud-based
Quantitative Modeling
and ML Ops
A scalable ML Ops toolkit to migrate SAS-based analytical modeling teams in Enterprise Risk to Amazon SageMaker. Automated onboarding, role management, and model lifecycle processes, significantly reducing model development and deployment timeframes while enhancing governance and scalability
Cloud Migration
and Credit Risk
Data Processing
Porting of Credit Risk data from an on-prem Netezza warehouse to AWS, leveraging S3 for data lakes and Redshift for high-performance analytics. A flexible ETL framework using DataSync, Glue ETL, and Apache Airflow, to enable faster month-end processing and supporting advanced risk modeling
Big Data Architecture
for Credit Risk
Databases
Re-engineering of legacy Sybase database for Credit Risk, to a cloud-based, big data architecture. Logical segregation of banking book, trading book, and securities finance transactions enabled parallel processing of month and quarter-end activities, improving performance by 70%
ETL Framework to
Streamline Counterparty
Credit Risk
Domain-specific Language (DSL)-based ETL Framework using domain for seamless data transformation and allowing multiple data feeds with minimal adjustments, resulting in zero downtime amid trade reference data changes, and 15% faster onboarding of new data
Modernization of
Enterprise Risk Limits
and Utilization Monitoring
Transformation of enterprise risk applications for limit management, utilization tracking, and exception handling using modernized dashboards and distributed architecture. High-performance, scalable solution with intuitive visualization and analytics for proactive risk monitoring
Intelligent Employee
Due Diligence
Re-engineering from monolith to microservices-based multilingual platform with automated employee trading analysis for conflict detection, insider trading, and misconduct. Chatbot integration to provide guidance and real-time reporting dashboards for global compliance officers
Advanced Trade
Surveillance and Market
Conduct Monitoring
Surveillance solutions to detect market manipulation, including spoofing, front running, and wash trades. Predictive analytics and behavioral indicators from transactions and communications to enhance the accuracy of alerts and streamline compliance workflows
Dynamic Data
Archival and
Intelligent Retrieval
Data archival framework to optimize cost and scalability, transitioning from a Big Data platform to IBM COS S3, reducing storage costs by 20%. Batch retrieval with validation, detailed reporting, and intuitive dashboards
Control Group System
for Conflict Clearance
and Securities Restrictions
Re-engineering of the Control Group System, replacing legacy platforms with a microservices-based cloud architecture. Streamlined integration with primary data sources, supporting conflict clearance, securities restrictions, and advanced search for compliance teams across global markets
Regulatory Reporting
and Disclosure of
Interest Compliance
In-house Disclosure of Interest solution to centralize company information impacting investment decisions. Ensuring compliance with global regulatory mandates by enabling accurate disclosure of financial holdings, enhancing transparency, and mitigating regulatory risks
Integrated Conduct Risk
Management for Trade and
Communication Surveillance
Unified platform for trade surveillance, electronic communication monitoring, and information barriers for supervisor groups. Automated rule-based workflows and targeted alert generation by leveraging country-specific predictive and behavioral analytics, strengthening global compliance
Value We Provide
Deep Expertise in
Risk & Compliance
Transformation
With extensive experience in modernizing risk and compliance ecosystems, we enable financial institutions to enhance resilience, optimize processes, and meet evolving regulatory requirements. Our solutions drive efficiency across risk modeling, surveillance, and compliance functions while ensuring seamless integration with enterprise-wide systems.
Scalable and High-
Performance
Risk Architecture
By leveraging cloud-native, distributed architectures and modern data frameworks, we help organizations transform legacy risk management platforms into scalable, high-performance solutions. Our approach enables faster data processing, enhanced risk analytics, and real-time insights for proactive risk mitigation.
Enhanced Surveillance
and Conduct
Risk Management
Our advanced surveillance solutions integrate behavioral analytics, predictive models, and automated workflows to strengthen monitoring capabilities. We empower financial institutions with targeted, high-quality alerts for insider trading, market abuse, and misconduct, improving regulatory adherence and risk mitigation.
Data-Driven Decision
Making for
Risk and Compliance
Through big data architectures, AI-driven insights, and real-time monitoring capabilities, we enable institutions to make informed, data-driven decisions. Our solutions optimize regulatory reporting, employee due diligence, and trade surveillance by providing actionable intelligence across risk and compliance functions.
Client Success Stories
How Gen AI can enhance software engineering
Contact
Our experts can help you find the right solutions to meet your needs.
Get in touchData migration to cloud expedites credit risk functions
Migrating on-premises models and data to the cloud enhances financial forecasting, sensitivity analysis, and time-to-market.
Client
A leading North American bank
Goal
Migrate credit risk data and SAS-based analytics models from on-premises data warehouse to AWS to enhance functionality
Tools and Technologies
AWS Glue, Redshift, DataSync, Athena, CloudWatch, SageMaker; Apache Airflow; Delta Lake; Power BI
Business Challenge
The credit risk unit of a major bank aimed to migrate SAS-based analytics models containing data for financial forecasting and sensitivity analysis to Amazon SageMaker.
This was to leverage benefits such as enhanced scalability, improved maintenance for MLOps engineers, and better developer experience. It also sought to migrate credit risk data from a Netezza-based on-premises data warehouse to AWS, utilizing a data lake on AWS S3 and a data warehouse on Redshift to support model migration.
Solution
- Decoupled data workload processing from relational systems using the phased approach with a focus on historical migration, transformational complexities, data volumes, and ingestion frequencies of the incremental loads
- Developed a flexible ETL framework using DataSync for extracting data to AWS as flat files from Netezza
- Transformed data in S3 layers using Glue ETL and moved it to the Redshift data warehouse
- Enabled Glue integration with Delta Lake for incremental data workloads
- Built ETL workflows using Step Functions during orchestration and concurrent runs of the workflow; orchestrated the concurrent runs of workflows using Apache Airflow
- Architected data shift from Netezza to AWS, leveraging a flexible ETL framework
Outcomes
- Enhanced financial forecasting and sensitivity analysis operations with analytical models and data migrated to the AWS public cloud
- Expedited time-to-market catering to client’s downstream consumption needs through Power BI and Amazon SageMaker
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Software transformation gets compliance for bank
Risk & Compliance
Software transformation gets FDIC compliance for bank
World’s renowned investment bank gets timely compliant with new QFC (Qualified Financial Contracts) and FDIC (Federal Deposit Insurance Corp.) regulations through holistic system transformation and extensive QA & testing.
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
The client mandated to comply with new QFC (Qualified Financial Contracts) regulations. The client also needed to perform in-depth functional validation across a revamped data platform to ensure it could timely process, review and submit to the FDIC (Federal Deposit Insurance Corp.) required daily reports on the open QFC positions of all its counterparties.
The project entailed immediate availability and processing of accurate QFC information at the close of each business day to swiftly assess data and note exceptions and exclusions for early corrective action. It also aimed to help the client meet stringent deadlines with varied report formats. Any breach or delay in compliance could attach hefty fines and reputational damage to the bank.
Solution
Iris revamped the entire system and performed end-to-end quality assurance and testing across the new regulatory reporting platform. This meant validating the transformed multi-layer database, user interface (UI), business process rules, and downstream applications.
We identified and solved workflow design gaps affecting data reporting on all open positions, agreements, margins, collaterals, and corporate entities, thus enhancing the capability for addressing irregularities. Our experts established an integrated and collaborative system, commanding transaction and reference data within a single platform by incorporating 166 distinct controls pertaining to data completeness, accuracy, consistency, and timeliness within a strategic framework.
Outcomes
Our quality assurance and testing solution delivered the following impacts:
- 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 critical defect delivery drastically reduced post-implementation
- An intuitive UI dashboard reflecting the real-time status of critical underlying data volumes, leakages, job run, and other stats
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Platform re-engineering for operational efficiency
A legacy data extraction platform was limiting business efficiency and transaction processing capabilities. Iris system modernization and platform re-engineering 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
The client had a licensed data platform for enterprise-wide risk and compliance operations. Spiked volumes with various financial product offerings and trades were restricting the processes and limiting the analytical capabilities on the existing platform.
The system upgrade was required to support related, complex credit risk calculations. These calculations serve as a ground for several thousand bankers/ traders to make loan and investment decisions for customers. System modernization would also cater to the internal transaction and regulatory reporting requirements.
Solution
Iris system re-engineering experts designed and implemented a scalable and highly configurable data extraction platform having global data architecture. This ETL framework-based platform enables faster, more efficient onboarding, consolidation, and processing of the numerous variable product and trading data input sources.
The re-engineered platform was enabled with value-adds and tools to automate, tabulate, compare, reserve, validate and test data. We integrated the data extraction platform seamlessly with downstream risk applications and system adaptability to accommodate operational/business needs.
Outcomes
Our data platform re-engineering solution enabled the client to achieve enormous benefits, including user experience, data quality, and risk management capabilities. Key outcomes of the solution constitute:
- Quicker, real-time configuration and execution of 500+ jobs for loading trade feed
- Downtime reduced to a minimum 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 operational efficiencies
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SFTR solution strengthened market leadership
Risk & Compliance
Securities financing transactions regulation compliance made easy
A global market data and trading services provider strengthens its EU market leadership with a regulatory solution by supporting a throughput of 6 million transactions per hour.
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
The client offers an automated, integrated solution to its clients in the European Union (EU) for complying with the Securities Financing Transactions Regulation (SFTR).
Effective in recent years, SFTR requires timely and detailed reporting based on multitudes of data, systems, collateral, and lifecycle events. The voluminous data is captured from hundreds of millions of daily transactions made to multiple trade repositories registered by the European Securities and Markets Authorities (ESMA).
Non-compliance at any stage is risky, potentially very costly, for all trade counterparties, i.e., broker-dealers, banks, asset managers, institutional investors.
Solution
Experienced in diverse technologies, big data, and capital markets, team Iris developed a streamlined, end-to-end data reporting platform with complex trade matching and monitoring systems. Improving speed, accuracy, and flexibility, the new architecture supports high trade concurrency and acceptance rates with parallel processing of millions of transactions.
The delivered solution also enabled optimal load balancing and matched the reconciliation at the trade repository. Built with microservices to accommodate future scalability, standardization, data quality, and security requirements, the system implemented functional enhancements. A Unique Transaction Identifier (UTI) subsystem was also developed for sharing and matching counterparty transactions, enabling plug-and-play setup for new repositories, and supporting any changes in outbound or inbound data report formats required by ESMA or clients. Improved dashboards and search pages helped the end-users in better configuration and tracking of their transactions.
Outcomes
The nimble delivery and successful roll-out of the new SFTR platform delivered the desired strategic competitive advantage to the client for maintaining its EU market leader position. The consolidated solution also helped in:
- 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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