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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          Release automation reduces testing time by 80%

          Professional Services

          Release automation reduces testing time by 80%

          DevOps implementation and release automation improved testing time, product quality, and global reach for a leading multi-level marketing company.

          Client
          A leading multi-level marketing company
          Goal
          Shorten the release cycle and improve product quality
          Technology Tools
          Amazon CloudWatch, Elasticsearch, Bitbucket, Jenkins, Amazon ECR, Docker, and Kubernetes
          Business Challenge

          The client's Commercial-off-the-shelf (COTS) applications were built using substandard code branching methods, causing product quality issues. The absence of a release process and a manual integration and deployment process were elongating release cycles. Manual configuration and setup of these applications were also leading to extended downtime. Missing functional, smoke, and regression test cases were adding to the unstable development environment. The database migration process was manual, resulting in delays, data quality issues, and higher costs.

          Solution
          • Code branching and integration strategy for defects / hotfixes in major and minor releases​
          • Single-click application deployment, including environment creation, approval and deployment activities​
          • Global DevOps platform implementation with a launch pad for applications to onboard other countries​
          • Automated configuration and deployment of COTS applications and databases​
          • Automation suite with 90% coverage of smoke and regression test cases​
          • Static and dynamic analysis implementations to ensure code quality and address configuration issues​
          Outcomes

          Automation of release cycles delivered the following benefits to the client:

          • Release cycle shortened from once a month to once per week
          • MTTR reduced by 6 hrs
          • Downtime decreased to <4 hours from 8 hours
          • Product quality and defect leakage improved by 75%
          • Testing time reduced by 80%
          • Reach expanded to global geographies
          • Availability, scalability, and fault tolerance enhanced for microservices-based applications
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          DevOps solution improves scalability by 5x

          Life Sciences

          Automated app & infra deployment improves scalability

          Automated app and infra deployment with DevOps implementation help a leading medical company launch applications in new geographies, improve time-to-market, and reduce the total cost of ownership.

          Client
          North America-based fertility and genomics company
          Goal
          Expand business reach, reduce time-to-market, and support critical compliance
          Technology Tools
          .NET 5, Vue.js, AWS Secrets Manager, AWS Transfer Family, Amazon RDS, Amazon EKS, Amazon Route 53, Amazon CloudFront, Terraform, GitLab
          Business Challenge

          The client wanted to expand its reach to Canada, Europe, and APAC regions to meet the requirements for a 10x increase in their user base. Legacy application infrastructure and code built on the old tech stack, with high technical debt, were slowing down the rollout of new features, making the client less competitive. The infra-deployment process was only partially automated, stretching the time-to-market to three months. The total cost of ownership was relatively high. HIPPA and PII compliance were also not supported.

          Solution

          Iris modernized the application into microservices, built the infrastructure using Terraform and automated its provisioning and configuration.

          • Application developed using .NET 5 and Vue.js
          • Architecture transformed into cloud-native
          • AWS Managed Services, including Secrets Manager, AWS Transfer Family, RDS, EKS, Route 53, CloudFront, and S3, configured using Terraform
          • EKS Cluster and associated components provisioned via Terraform
          • App pushed to container registry using GitLab pipeline
          • Secrets (API keys, database connection strings, etc.) and app images moved to EKS Cluster using S3 Bucket Helm
          • Static code analysis, coverage and vulnerability scans integrated to ensure code quality and reduce configuration issues
          Outcomes
          Our DevOps solution enabled the client to achieve significant benefits, including:
          • Application launch in Canada and Europe; Asia Pacific release in the pipeline
          • HIPPA and PII compliance
          • 5x scalability improvement from weekly average usage
          • Time-to-market reduced from three months to 3 weeks
          • Total cost of ownership lowered by 50%
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          API migration benefits leading logistics company

          Transportation and Logistics

          Phased API migration benefits leading logistics company

          Phased migration of Boomi APIs to Apigee helps a leading logistics company improve performance and scale without business disruption

          Client
          A leader in truck transportation and logistics services
          Goal
          Migrate Boomi-based logistics APIs to improve performance and scalability
          Technology Tools
          Apigee, Boomi, Swagger, JMeter, Postman, GCP
          Business Challenge

          The client's existing Boomi Atom platform with logistics APIs had lifecycle and monitoring issues, with frequent and elongated downtimes, causing customer experience challenges.

          The system did not support the logging of events, and API transactions were untraceable. Identifying the number of customers facing issues and incidents when APIs were not working was difficult. The absence of alerting mechanisms, scalability concerns, and the Boomi platform's high licensing costs were other critical challenges.

          An optimized API governance system was required to provide an abstraction for the backend services, security, and efficiencies around rate limiting, quotas, and analytics.

          Solution

          Iris strategized the smooth transition of 250+ Boomi APIs, starting with 20 in the pilot phase. The entire migration was planned to occur in four waves.

          First, the pseudocode of Boomi APIs was documented and reviewed. The team then developed proxies in Apigee X following a TDD (Test-driven Development) approach. A well-defined logging framework was provided to the client for capturing appropriate parameters for tracking API calls.

          Seamless migration of API keys from Boomi API Management (APIM) to Apigee X apps was performed. Network routing at F5 for the individual proxies was implemented to transfer the traffic from Boomi to Apigee post migration in each wave. Process metering, monitoring, and adherence/compliance hooks were inserted into the system.

          Outcomes

          Our API migration solution delivered the following outcomes:

          • Improved performance and scalability by 30%
          • Centralized logging and alerting for both APIM and backend systems resulting in 40% MTTR (Mean Time for Ticket Resolution)
          • Apigee analytics enablement for API traffic, request latency, response time, target errors, and transaction revenue analysis
          • Enablement of API discovery, monetization, registration, partner onboarding, and governance
          • Ability to integrate the system into the Apigee developer portal
          • Eliminated Boomi licensing cost
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          Order management platform transformation

          Professional Services

          New platform transforms transactions processes

          Platform transformation and multi-cloud integration improve multinational publishing company's order management, time-to-market and performance.

          Client
          Multinational publishing, media, and educational company
          Goal
          Improve order management and transaction processing capabilities
          Technology Tools
          AWS EKS, Kong, Salesforce Commerce Cloud (SFCC), Salesforce CRM, Jenkins, Sumo Logic, Datadog
          Business Challenge

          The client's order management platform was complex and had scalability issues, causing poor customer experience and loss of revenue. The platform was hosted on Oracle cloud, with data stored in different repositories. Services were also hosted in the Oracle cloud, which used the BICC extract to fetch information about order details from Oracle databases. The low performance of customer-facing applications was causing latency and very high transaction processing time.

          Solution

          Team Iris transformed Oracle-based SOA services into six microservices and migrated them to AWS EKS for autoscaling with self-healing and monitoring capabilities.

          We developed services for publishing data to Salesforce CRM for quick order processing and conversions. The BICC system for diversified information and order history was enabled with real-time integration between Oracle Fusion and materialized views for data consumption.

          Post migration, these services were registered in Kong for discovery, and a CI/CD pipeline was created for deployment using Jenkins. Sumo Logic was used for monitoring the logs, and Datadog was used to observe latency, anomalies and other metrics.

          Outcomes

          The order management platform transformation delivered the following benefits to the client:

          • System performance improved by 70%
          • Transaction processing capability increased by 4x
          • Order processing capabilities were enhanced by 200%
          • Total cost of ownership (TCO) was reduced by 30%
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          Custom analytics enable faster business decisions

          Brokerage, Wealth & Asset Management

          Custom analytics enable faster business decisions

          Optimized data and on-demand analytics deliver faster business insights and better user experience for asset management firm

          Client
          U.S.-based asset management company
          Goal
          Streamline and improve data and analytics capabilities for enhanced user experiences
          Technology Tools
          Java, React JS, MS SQL Server, Spring Boot, GitHub, Jenkins
          Business Challenge

          The client captures voluminous data from multiple internal and external sources. The absence of quick, on-demand capabilities for business users was inefficient in generating customized portfolio analytics on attributes such as average quality, yield to maturity, average coupon, etc.

          The client teams were spending enormous amounts of manual effort and elapsed time (approximately 12-15 hours) to respond to requests for proposals from their respective clients.

          Solution

          Iris implemented a data acquisition and analytics system with pre-processing capabilities for grouping, classifying, and handling historical data.

          A data dictionary was established for key concepts, such as asset classes and industry classifications, enabling end users to access data for analytical computation. The analytics engine was refactored, optimized, and integrated into the streamlined investment performance data infrastructure.

          The team developed an interactive self-service capability, allowing business users to track data availability, perform advanced searches, generate custom analytics, visualize information, and utilize the insights for decision-making.

          Outcomes

          The solution brought several benefits to the client, including:

          • Simplified data access to generate custom analytics for end users
          • Eliminated manual processing and the need for complex queries
          • Enhanced the stakeholder experience
          • Reduced response time to client RFPs by over 50%
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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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