Asset tokenization transforming global finance

Real world asset tokenization can transform financial markets

Integration with Distributed Ledger Technologies is critical to realizing the full potential of tokenization.




    The global financial markets create and deal in multiple asset classes, including equities, bonds, forex, derivatives, and real estate investments. Each of them constitutes a multi-trillion-dollar market. These traditional markets encounter numerous challenges in terms of time and cost which impede accessibility, fund liquidity, and operational efficiencies. Consequently, the expected free flow of capital is hindered, leading to fragmented, and occasionally limited, inclusion of investors.

    In response to these challenges, today's financial services industry seeks to explore innovative avenues, leveraging advancements such as Distributed Ledger Technology (DLT). Using DLTs, it is feasible to tokenize assets, thus enabling issuance, trading, servicing and settlement digitally, not just in whole units, but also in fractions.

    Asset tokenization is the process of converting and portraying the unique properties of a real-world asset, including ownership and rights, on a Distributed Ledger Technology (DLT) platform. Digital and physical real-world assets, such as real estate, stocks, bonds, and commodities, are depicted by tokens with distinctive symbols and cryptographic features. These tokens exhibit specific behavior as part of an executable program on a blockchain.

    Many domains, especially financial institutions, have started recognizing the benefits of tokenization and begun to explore this technology. Some of the benefits are fractional ownership, increased liquidity, efficient transfer of ownership, ownership representation and programmability.

    With the recent surge in the adoption of tokenization, a diverse array of platforms has emerged, paving the way for broader success, but at the same time creating fragmented islands of ledgers and related assets. As capabilities mature and adoption grows, interconnectivity and interoperability across ledgers representing different institutions issuing/servicing different assets could improve, creating a better integrated market landscape. This would be critical to realizing the promise of asset tokenization using DLT.

    Read our Perspective Paper for more insights on asset tokenization and its potential to overcome the challenges, the underlying technology, successful use cases, and issues associated with implementation.

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      How Low-code Empowers Mission-critical End Users

      How Low-code Empowers Mission-critical End Users

      Low-code platforms enable rapid conversions to technology-managed applications that provide end users with rich interfaces, powerful configurations, easy integrations, and enhanced controls.




        How Low-code Empowers Mission-critical End Users

        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.

        Many large and small enterprises utilize business-managed applications (BMAs) in their value chain to supplement technology-managed applications (TMAs). BMAs are applications or software that end users create or procure off-the-shelf and implement on their own; these typically are low-code or no-code software applications. Such BMAs offer the ability to automate or augment team-specific processes or information to enable enterprise-critical decision-making.

        Technology teams build and manage TMAs to do a lot of heavy lifting by enabling business unit workflows and transactions and automating manual processes. TMAs are often the source systems for analytics and intelligence engines that drive off data warehouses, marts, lakes, lake-houses, etc. BMAs dominate the last mile in how these data infrastructures support critical reporting and decision making. 

        While BMAs deliver value and simplify complex processes, they bring with them a large set of challenges in security, opacity, controls collaboration, traceability and audit. Therefore, on an ongoing basis, business-critical BMAs that have become relatively mature in their capabilities must be industrialized with optimal time and investment. Low-code platforms provide the right blend of ease of development, flexibility and governance that enables the rapid conversion of BMAs to TMAs with predictable timelines and low-cost, high-quality output. 

        Read our Perspective Paper for more insights on using low-code platforms to convert BMAs to TMAs that provide end users with rich interfaces, powerful configurations, easy integrations, and enhanced controls.

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          Conversational assistant boosts AML product assurance

          Anti-Money Laundering & Know-Your-Customer

          Conversational assistant boosts AML product assurance

          Gen AI-powered responses improve the turnaround time to provide technical support for recurring issues, resulting in a highly efficient product assurance process.

          Client
          A large global bank
          Goal
          Improve turnaround time to provide technical support for the application support and global product assurance teams
          Tools and Technologies
          React, Sentence–Bidirectional Encoder Representations from Transformers (S-BERT), Facebook AI Similarity Search (FAISS), and Llama-2-7B-chat
          Business Challenge

          The application support and global product assurance teams of a large global bank faced numerous challenges in delivering efficient and timely technical support as they had to manually identify solutions to recurring problems within the Known Error Database (KEDB), comprised of documents in various formats. With the high volume of support requests and limited availability of teams across multiple time zones, a large backlog of unresolved issues developed, leading to higher support costs.

          Solution

          Our team developed a conversational assistant using Gen AI by:

          • Building an interactive customized React-based front-end
          • Ringfencing a corpus of problems and solutions documented in the KEDB
          • Parsing, formatting and extracting text chunks from source documents and creating vector embeddings using Sentence–Bidirectional Encoder Representations from Transformers (S-BERT)
          • Storing these in a Facebook AI Similarity Search (FAISS) vector database
          • Leveraging a local Large Language Model (Llama-2-7B-chat) to generate summarized responses
          Outcomes

          The responses generated using Llama-2-7B LLM were impressive and significantly reduced overall effort. Future enhancements to the assistant would involve:

          • Creating support tickets based on information collected from users
          • Categorizing tickets based on the nature of the problem
          • Automating repetitive tasks such as access requests / data volume enquiries / dashboard updates
          • Auto-triaging support requests by asking users a series of questions to determine the severity and urgency of the problem

          Gen AI For Software Engineers

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          AI-powered summarization boosts compliance workflow

          Insurance

          AI-powered summarization boosts compliance workflow

          Gen AI-enabled conversational assistant substantially simplifies access to underwriting policies and procedures across multiple, complex documents.

          Client
          A leading specialty property and casualty insurer
          Goal
          Improve underwriters’ ability to review policy submissions by providing easier access to information stored across multiple, voluminous documents.
          Tools and Technologies
          Azure OpenAI Service, React, Azure Cognitive Services, Llama-2-7B-chat, OpenAI GPT 3.5-Turbo, text-embedding-ada-002 and all-MiniLM-L6-v2
          Business Challenge

          The underwriters working with a leading specialty property and casualty insurer have to refer to multiple documents and handbooks, each running into several hundreds of pages, to understand the relevant policies and procedures, key to the underwriting process. Significant effort was required to continually refer to these documents for each policy submission.

          Solution

          A Gen-AI enabled conversational assistant for summarizing information was developed by:

          • Building a React-based customized interactive front end
          • Ringfencing a knowledge corpus of specific documents (e.g., an insurance handbook, loss adjustment and business indicator manuals, etc.)
          • Leveraging OpenAI embeddings and LLMs through Azure OpenAI Service along with Azure Cognitive Services for search and summarization with citations
          • Developing a similar interface in the Iris-Azure environment with a local LLM (Llama-2-7B-chat) and embedding model (all-MiniLM-L6-v2) to compare responses
          Outcomes

          Underwriters significantly streamlined the activities needed to ensure that policy constructs align with applicable policies and procedures and for potential compliance issues in complex cases.

          The linguistic search and summarization capabilities of the OpenAI GPT 3.5-Turbo LLM (170 bn parameters) were found to be impressive. Notably, the local LLM (Llama-2-7B-chat), with much fewer parameters (7 bn), also produced acceptable results for this use case.

          Gen AI For Software Engineers

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          Automated financial analysis reduces manual effort

          Commercial & Corporate Banking

          Automated financial analysis reduces manual effort

          Analysts in a large North American bank's commercial lending and credit risk operations can source intelligent information across multiple documents.

          Client
          Commerical lending and credit risk units of large North American bank
          Goal
          Automated retrieval of information from multiple financial statements enabling data-driven insights and decision-making
          Tools and Technologies
          OpenAI API (GPT-3.5 Turbo), LlamaIndex, LangChain, PDF Reader
          Business Challenge

          A leading North American bank had large commercial lending and credit risk units. Analysts in those units typically refer to numerous sections in a financial statement, including balance sheets, cash flows, and income statements, supplemented by footnotes and leadership commentaries, to extract decision-making insights. Switching between multiple pages of different documents took a lot of work, making the analysis extra difficult.

          Solution

          Many tasks were automated using Gen AI tools. Our steps:

          • Ingest multiple URLs of financial statements
          • Convert these to text using the PDF Reader library
          • Build vector indices using LlamaIndex
          • Create text segments and corresponding vector embeddings using OpenAI’s API for storage in a multimodal vector database e.g., Deep Lake
          • Compose graphs of keyword indices for vector stores to combine data across documents
          • Break down complex queries into multiple searchable parts using LlamaIndex’s DecomposeQueryTransform library
          Outcomes

          The solution delivered impressive results in financial analysis, notably reducing manual efforts when multiple documents were involved. Since the approach is still largely linguistic in nature, considerable Prompt engineering may be required to generate accurate responses. Response limitations due to the lack of semantic awareness in Large Language Models (LLMs) may stir considerations about the usage of qualifying information in queries.

          Gen AI For Software Engineers

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          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.

            Download Perspective Paper




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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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              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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              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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