Data systems built on legacy technologies were delaying month-end activities and actionable insights for finance and sales teams. Transformation of data and BI applications to MS Azure delivered "Order to Cash" sales analytics on cloud.
Client
A medical devices and fertility solutions company
Goal
Establish a cloud data warehouse for a single source of truth and timely month-end activities
Tools and Technologies
Azure Data Factory, Azure Data Lake, Power BI, Synapse Analytics
Business Challenge
The client had multiple instances of ERPs, sales systems, and warehouses built on obsolete technology and frameworks. The existing system siloed the data, resulting in inconsistent versions of the truth. The client's finance and sales teams were struggling to reconcile data offline and feed the same back into the ERPs, causing significant delays in month-end activities.
As the record systems were also not synced and legacy reports were built on the interim warehouses, line managers and executive teams were not able to extract actionable and comprehensive insights. A solution to onboard and integrate new datasets on an ongoing basis was required to support the data merger and acquisition process.
Solution
A strategy to transform data and BI applications to MS Azure was finalized. The transformation was executed in phases and included discovery, report rationalization and foundational build of a global system of reporting.
The solution included the data ingestion process with Azure Data Factory, data storage and processing using Azure Data Lake and Synapse Analytics, reports and dashboards with Power BI.
A utility to accelerate the onboarding of new data entities was conceptualized and delivered to onboard and integrate new datasets to support mergers and acquisitions.
Outcomes
Iris data practitioners helped the client overcome key challenges and advanced data warehousing capabilities by:
- Establishing a "single version of the truth" that enabled data-driven decisions and timely completion of month-end and other critical activities
- Delivering analytics for “Order to Cash” processes, including subject areas of sales, inventory, shipping, finance, etc.
- Facilitating the generation of actionable, insightful reports and dashboards, allowing "self-service" consumption for the business leadership
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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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Reporting transformation with data science and AI
A multinational bank leveraged data automation to achieve major gains in reporting efficiency, with 99% accuracy in processing variable inputs, for its global investment fund.
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 net asset value (NAV) 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
The high solution accuracy helped the client’s global NAV reporting team significantly improve precision, efficiency, quality, turnaround time, and flexibility. The delivered solution contributed to:
- 90 - 95% reduction in operational efforts
- 99% accuracy in processing variable inputs
- Zero rework effort and cost
Our highly customizable and scalable solution can be seamlessly integrated with existing reporting applications and MS Outlook while accommodating additional volumes, report types, and business units.
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Data consolidation speeds up drug search
An automated cloud-based consolidation application for R&D data helped a pharmaceutical company improve turnaround time to create Approval for Product Release (APR) documents.
Client
A U.S.-based pharmaceutical multinational corporation
Goal
Reduce turnaround time for APRs
Tools and Technologies
Amazon’s AWS OPCx, Webmethods, natural language processing (NLP), neural networks, and Python programming
Business Challenge
In pharmaceutical R&D, data is generated from several sources: the process, patients, retailers, and caregivers, among others. Pharmaceutical R&D organizations that use the traditional way of creating APRs manually consolidate paper specifications into binders across all R&D functions.
Specific regional rules, compliance mandates, and external regulations were slowing down the client’s workflow. Many spreadsheets in multiple formats were leading to errors from manual entry and duplication of data — the inevitable “swivel effect” that results from data being pulled out from disparate, unconnected software packages.
Iris was approached to improve the process of collecting and using data from multiple sources; the improvement would help the client identify and develop new potential drug candidates faster.
Solution
Iris’s team of 12 specialists designed, developed, tested, and deployed a cloud-based application that integrates data from multiple regions and eight different systems into a single, unified interface for the client’s users. Our application unified the creation and management of the client’s workflows across its lines of business and 20 different product families.
The development environment included Amazon’s AWS OPCx, Webmethods, natural language processing (NLP), neural networks, and Python programming.
Outcomes
Within a year of the application’s release, 2,800 users were using the application, with 55% of APRs turning around in 10 calendar weeks or less. Thanks to the in-memory data grid, the response time of transactions across the board has been brought down to nearly 2 seconds.
The cloud-based application developed by Iris ensures that data is automatically and seamlessly shared between systems that were previously stand-alone and required the tedious manual entry of data.
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A playbook for banks on managing M&A integration
Efficient management of the complexities of disparate systems and data after merger and acquisition (M&A) integration saves time and money.
Client
Banks that have merged or acquired new businesses.
Goal
Manage migration and integration complexity post M&A.
Tools and Technologies
The Iris business acquisition playbook for banks.
Business Challenge
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.
Solution
Iris created a business acquisition playbook for our banking clients outlining steps to insource with a migration and integration strategy. We defined insourcing steps for business and technology teams and created a migration strategy with quantifiable recommendations and a reusable checklist for insourcing activities.
Our solutions enabled clients to deal with post-merger integrations and create a single source of truth for transactional data and positions. We consolidated multiple acquisition playbooks and created a single standardized framework for their lending business. The solution also included data integration management and ensured connectivity for the lending business. The solutions were specifically tailored for applications in the loan origination and servicing space.
Outcomes
Our solution rendered several significant benefits and helped clients:
- Achieve 50% savings in cycle time and cost for post-merger integration of business processes, application, and data
- Capability and readiness assessment and assistance in choosing from insourcing options
- Achieve full migration of data and systems
- Achieve partial migration of systems and data migration and integration
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