The State of Central Bank Digital Currency

The State of Central Bank Digital Currency

Innovations in digital currencies could redefine the concept of money and transform payments and banking systems.




    The State of Central Bank Digital Currency

    Do you trust your data?

    Data driven organizations are ensuring that their Data assets are cataloged and a lineage is established to fully derive value out of their data assets.

    Central banking institutions have emerged as key players in the world of banking and money. They play a pivotal role in shaping economic and monetary policies, maintaining financial system stability, and overseeing currency issuance. A manifestation of the evolving interplay between central banks, money, and the forces that shape financial systems is the advent of Central Bank Digital Currency (CBDC). Many drivers have led central banks to explore CBDC: declining cash payments, the rise of digital payments and alternative currencies, and disruptive forces in the form of fin-tech innovations that continually reshape the payment landscape.

    Central banks are receptive towards recent technological advances and well-suited to the digital currency experiment, leveraging their inherent role of upholding the well-being of the monetary framework to innovate and facilitate a trustworthy and efficient monetary system.

    In 2023, 130 countries, representing 98% of global GDP, are known to be exploring a CBDC solution. Sixty-four of them are in an advanced phase of exploration (development, pilot, or launch), focused on lower costs for consumers and merchants, offline payments, robust security, and a higher level of privacy and transparency. Over 70% of the countries are evaluating digital ledger technology (DLT)-based solutions.  

    While still at a very nascent stage in terms of overall adoption for CBDC, the future of currency promises to be increasingly digital, supported by various innovations and maturation. CBDC has the potential to bring about a paradigm shift, particularly in the financial industry, redefining the way in which money, as we know it, exchanges hands.

    Read our perspective paper to learn more about CBDCs – the rationale for their existence, the factors driving their implementation, potential ramifications for the financial landscape, and challenges associated with their adoption.

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      Cloud Migration, Challenges and Solutions

      Cloud Migration, Challenges and Solutions

      Insights into the top challenges and their mitigations in the Cloud journey.




        Cloud Migration, Challenges and Solutions

        Do you trust your data?

        Data driven organizations are ensuring that their Data assets are cataloged and a lineage is established to fully derive value out of their data assets.

        Selecting an appropriate path for an application or a portfolio of applications is one of the most critical decision points in a cloud journey. Assessing the nature and criticality of an existing application is usually the starting place. Another critical factor to consider is the implementation (migration) cost and time for each path to cloud. The four cloud adoption options are re-host, re-platform, re-factor and re-write in the order of increasing cost, effort, cloud benefits, and TCO reduction. Out of these, re-host usually does not involve code change and is relatively simple.

        Mapping cloud operating metrics into a 3x3 matrix is a good starting point on planning for a cloud journey. In this matrix, the cloud operating metrics would move to the right if they are critical for customer intelligence applications; that would be an X factor. Another critical dimension while planning cloud migration is identifying the interface dependencies between selected application(s) and others – both inbound and outbound. These could be synchronous, asynchronous or batch.

        Understanding the application architecture, its internal organization, and inter-dependencies are critical before migration. This can be a very complex and labor-intensive task if done manually and can be error prone. Not fully understanding the existing code can lead to issues related to transactions, data corruption, session handling, and performance.

        To read more on the top challenges and their mitigations in the cloud journey, download the perspective paper here.

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          Quality engineering for Blockchain-DLT platform

          Quality engineering for Blockchain-DLT platform

          Banking & Financial Services

          Next-gen Quality Engineering for Blockchain-DLT platform

          Quality engineering implementation helps a digital financial services client smooth the legacy migration of its Blockchain-DLT (Digital Ledger Technology) platform by advancing automation coverage and patch delivery efficiencies.

          Client

          A leading digital financial services company

          Goal

          Blockchain- DLT platform assurance with improved automation coverage

          Tools and technologies

          Amazon Elastic Kubernetes Service (EKS), Azure Kubernetes Services (AKS), Docker, Terraform, Helm Charts, Microservices, Kotlin, Xray

          BUSINESS CHALLENGE

          The client's legacy DLT platform did not support cloud capabilities with the Blockchain-DLT tech stack. The non-GUI (Graphic User Interface) and CLI (Command Line Interface)-based platform lacked the microservices architecture and cluster resilience. The REST (Representational State Transfer) APIs-based platform did not support platform assurance validation at the backend. Automation coverage for legacy and newer versions of the products was very low. Support for delivery patches was insufficient, impacting the delivery of multiple versions of R3 products each month.

          SOLUTION

          Iris developed multiple CorDapps to support automation around DLT-platform functionalities and enhanced the CLI-based & cluster utilities in the existing R3 automation framework. The team implemented the test case management tool Xray to improve test automation coverage for legacy and newer versions of the Corda platform, enabling smooth and frequent patch deliveries every month. The quality engineering process was streamlined for the team's Kanban board by modifying the workflows. Iris also introduced the ability to execute a testing suite that could run on a daily or as-needed basis for AKS, EKS, and Local MAC/ Windows/ Linux cluster environments.

          OUTCOMES

          The Blockchain-DLT reliability assurance solution enabled the client to attain:

          • Improved automation coverage of the DLT platform with 900 test cases with a pass rate of 96% in daily runs
          • Compatibility across AWS-EKS, Azure-AKS, Mac, Windows, Linux, and local clusters
          • Increased efficiency in deliverables with an annual $35K savings in the test case management area

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          Test Automation Speeds Model Risk Management System

          Test Automation Speeds Model Risk Management System

          Banking

          Test Automation Speeds Model Risk Management System

          Automated testing for a top international bank's model risk management system brings efficiency and reliability.

          Client

          Top international bank

          Goal

          Fully automate the model risk management system framework to improve quality and confidence in testing results

          Tools and technologies

          Java, Selenium, Maven, TestNG, Git

          BUSINESS CHALLENGE

          The client's existing model risk framework was inefficiently handling functional testing aspects and risk scenarios due to lack of an end-to-end testing framework. Built on redundant, hard-to-debug, and non-scalable code, the system was unreliable for model risk testing. Test cases and controls were maintained and executed in Excel, eliminating parallel workflow abilities, tempering testing results, contributing to increased testing efforts and even delaying production launches in some cases. Scalability of testing using automation, running data-driven, end-to-end test flows, and restoring confidence in test results were the client's prime challenges.

          SOLUTION

          Iris built a lightweight and scalable new framework, providing 100% automated regression testing of functional test cases. Using simplified, customizable code that separated automation utilities and test functions, Iris' solution brought multiple improvements. Among them was faster test execution due to significantly reduced manual efforts. It also resulted in better quality and stability from the early identification of testing issues, enabling immediate corrective actions to occur. Another advantage of the solution was adaptability to multiple application areas due to ease of maintainability and traceability of code employed.

          OUTCOMES

          The client experienced several positive effects from the new, fully-automated solution:

          • Acquired a 100% stable, scalable, reusable test framework
          • ROI of 72%; payback period of less than 8 months
          • 20% reduction in testing efforts for faster time to market
          • Significant decrease in time required for ongoing maintenance of test scripts

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          Data warehouse enhances client communications

          Data warehouse enhances client communications

          Asset Management

          Investment data warehouse enhances client communications

          Account management and marketing teams of an investment bank acquired improved multi-channel client communications and portfolio management capabilities with a data warehouse and a single source of truth.

          Client

          A U.S.-based investment bank

          Goal

          Improve data collation and information quality for enhanced marketing and client reporting functions

          Tools and technologies

          Composite C1, Oracle DB, PostgreSQL, Vermilion Reporting Suite, Python, MS SQL Server, React.js

          BUSINESS CHALLENGE

          The client’s existing investment data structure lacked a single source of truth for investment and performance data. The account management and marketing teams were making significant manual efforts to track portfolio performance, identify opportunities and ensure accurate client reporting. The time-consuming and manual processes of generating marketing exhibits and client reports were highly error-prone.

          SOLUTION

          Iris implemented a comprehensive investment data infrastructure for a single source of truth and improved reporting capabilities for marketing content and client report generation. An automated Quality Assurance process was instituted to validate the information in critical marketing materials, such as fact sheets, snapshots, sales kits, and flyers, against the respective data source systems. Retail and institutional portals were developed to provide a consolidated view of portfolios, with the ability to drill down to underlying assets, AUM (Assets Under Management) trends, incentives, commissions, and active opportunities.

          OUTCOMES

          The new data infrastructure delivered a holistic, on-demand view of investment details, including performance characteristics, breakdowns, attributions, and holdings, to the client's marketing team and account managers with: 

          • ~95% reduction in performance data and exhibit information discrepancies
          • ~60% improvement in operational efficiency in core marketing and client reporting functions

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          Brokerage platform transformation improves UX

          Brokerage platform transformation improves UX

          Brokerage & Wealth

          Brokerage platform transformation achieves superior UX

          A monolithic registered investment advisor (RIA) platform transformed into microservices helps a leading brokerage firm achieve enhanced operations and user experience.

          Client

          A leading U.S. brokerage firm with $1+ trillion in assets and serving 6,000+ RIAs

          Goal

          Resolve online platform accessibility, functionality and timeliness issues

          Tools and technologies

          Angular 9, Jenkins, Pivotal Cloud Foundry, Oracle, Kubernetes, Spring, Docker

          BUSINESS CHALLENGE

          Client’s existing brokerage platform supporting over 6,000 Registered Investment Advisors (RIAs) and containing information about assets valued at more than $1 trillion had accessibility issues. The high cost of owning and maintaining outdated technologies and time-to-market for new features were adding to the business challenges.

          SOLUTION

          Iris transitioned the client’s monolith applications to microservices to transform the RIA platform. An open-source, cloud technical stack was leveraged to develop a single-page, micro-UI-based application. BFF (Backend for Frontend) design was applied, and Angular 9 was used to achieve superior compatibility on mobile devices. Widgets were introduced to enable seamless transitions within third-party applications. Consolidated user views were created to track assets and their performance for a unified experience for the RIAs.

          OUTCOMES

          The RIA platform transformation enabled the client to achieve significant functional enhancements, including:
          • Fully functional mobile views
          • 100+ integrated third-party applications
          • Instant and seamless access to client accounts
          • Downtime for hot deployments of fixes brought to zero
          • Technical debt decreased by 45%
          • Release timelines shortened by 80%
          • Issue resolution time reduced by 90%

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          Cloud Data Lakehouse for Single Source of Truth

          Cloud Data Lakehouse for Single Source of Truth

          Life Sciences

          Cloud Data Lakehouse for Single Source of Truth

          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 transformation 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 to cash business processes, including subject areas of sales, inventory, shipping, finance, etc., on Power BI
          • Facilitating the generation of actionable, insightful reports and dashboards, allowing "self-service" consumption for the business leadership

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          IT modernization lowers costs for capital markets

          IT modernization lowers costs for capital markets

          Capital Markets

          IT modernization lowers costs for capital markets

          Age-old applications built on legacy technologies were affecting the client’s investment banking functions. Iris modernized the existing application architecture and helped the client save tremendous costs and turnaround time.

          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

          The client’s existing database application architecture was resulting in poor performance that was adversely affecting 10,000 investment banking functions. The age-old application posture was disrupting the compliance process for the global M&A, IPO, and other financing deals. The client’s customers were encountering problems such as regulations access, inconsistent or unusable data/deal search results, lagging database updates, delayed response time. The need for a capable system to support duplicate searches, and increasing ad-hoc IT redress efforts and costs were other prime concerns.

          SOLUTION

          Our tech experts re-engineered the entire compliance application and set up a new software system. We upgraded selective elements to improve the infrastructure performance and usage feasibility. The modernized system with new technology architecture streamlined processes and enabled faster, more advanced interactions between the information databases, search queries, and retrieval mechanisms. Complex procedures and the process of viewing features were also simplified.

          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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          Platform re-engineering for operational efficiency

          Platform 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

          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
          • 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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          Tech stack automation expedites script development by 3x

          Tech stack automation expedites script development by 3x

          Manufacturing

          Tech stack automation expedites script development by 3x

          Manual processes across the multi-technology stack were severely affecting the script development cycles in terms of time, effort and cost. Iris application agnostic Test Automation framework and DevOps integration helped the client reduce the script development time and cost significantly.

          Client

          A leading building supplies manufacturing company

          Goal

          To support 30+ applications stack for UI, E2E, APIs, performance, mobile automation along with DevOps pipeline integration

          Tools and technologies

          .NET Core, PeopleSoft, Salesforce, WMS, JavaScript, Angular, Foxpro, C#, Selenium, SpecFlow, RestSharp, Nunit, Mobile Center/Emulators, Allure, Jira, Azure Pipeline, GitHub

          BUSINESS CHALLENGE

          The client had technology stacks comprising of diverse technologies that were difficult to manage. Substantial manual effort and time were spent on integrating the checkpoints, elongating the development process. Validating end-to-end business flows across different applications was the prime challenge. Reporting processes were also scattered across the entire application stack, making it vulnerable.

          SOLUTION

          Iris developed a robust application agnostic Test Automation framework to support the client’s multiple-technology stacks. Following the Behavior-driven Development (BDD) approach to align the acceptance criteria with the stakeholders, we built business and application layers of the common utilities in the core framework. Our experts identified E2E business flows to validate the downstream impact of the change and automated the entire stack through the shift-left approach. Azure DevOps integration enabled a common dashboard for reporting. The client attained complete version control to track production health and enforce strong validations.

          OUTCOMES

          Iris Automation solution enabled the client to surpass several business goals. The key outcomes of the delivered solution included:

          • ~65% Increase in automation coverage
          • 100+ Pipelines for in-scope applications across multiple environments
          • 3700+ Test Automation scripts execution per sprint cycle achieved across applications
          • 3X Faster script development of behavior-driven test cases 
          • Multi-day manual test effort reduced to a few hours of automated regression 
          • 70% Reduction in effort

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