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.




    Asset tokenization transforming global finance

    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.

    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 oof 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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      Meet our team at the AWS Summit in NYC July 2024

      Meet our team at the AWS Summit in NYC July 2024

      Meet our team at the AWS Summit in New York July 2024

      An AWS partner, Iris has a team of professionals attending the Summit who are excited to discuss cloud innovation and our enterprise-empowering, future-ready solutions in Cloud, Data & Analytics, and Generative AI.

      Happening at the Jacob Javits Convention Center on the 10th of July, the 2024 AWS Summit New York promises more than 170 sessions on all things cloud and data - from data lake architecture, data governance, data sharing, data engineering and data streaming to machine learning (ML) and ML Ops, data warehouses, business insights and visualization, and data strategy. It also offers interaction with AWS experts, builders, customers, and AWS partners, including Iris Software. All levels of experience – from foundational and intermediate to advanced or expert - can learn and share insights on cloud migration, generative AI, data analytics, as well as industry solutions, challenges and top providers.

      An Iris team with extensive and wide-ranging technology and domain experience is attending the Summit. Our professionals are ready and excited to discuss the cloud and data solutions and infrastructure modernization we provide to leading companies across many industries, as well as the advances in emerging tech that we leverage to further aid our clients’ business competitiveness, leadership and digital transformation journeys.

      Our Leaders

      Financial Services Client Partners –

      Brokerage & Wealth Management; Capital Markets & Investment Banking; Commercial & Corporate Banking; Compliance – Risk & AML; Retail Banking & Payments

      Enterprise Services Client Partners -
      Insurance, Life Sciences, Manufacturing, Pharmaceutical, Professional Services, Transportation & Logistics

      Contact our team to learn more about our innovative approach and advanced technology solutions in AI / ML, Application Development, Automation, Cloud, DevOps, Data Science, Enterprise Analytics, Integrations, and Quality Engineering, which enhance security, scalability, reliability, cost-efficiency, and compliance. Explore how we can add valuable impact to harnessing and monetizing data, optimizing customer experiences, and empowering developers. For more insights, read our Perspective Papers on Cloud Migration Challenges and Solutions and Succeeding in ML Ops Journeys.

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      Gen AI interface enhances API productivity and UX

      Transportation & Logistics

      Gen AI interface enhances API productivity and UX

      Integrating Generative AI technology and developer portal reduces logistics provider’s API onboarding to 1-2 days.

      Client
      Leading logistics services provider
      Goal
      Improve API functionality and developer team’s productivity and user experience
      Tools and Technologies
      Business Challenge

      A leading logistics provider offers an API Developer Portal as a central hub for managing APIs, enabling collaboration, documentation, and integration efforts, but faces limitations, including:

      • Challenges to comprehend schemas, necessitating continued reliance on developers
      • No means to individually search for API operations on the API Developer Portal
      • Difficulties keeping track of changes in newly-released API versions
      • Potential week-long delays as business analysts or product owners must engage developers to check if existing APIs can support new website functionalities
      Solution

      Integrating Gen AI technology with API, we provided a user-friendly chat interface for business users. Features include:

      • Conversational interface for API interaction, eliminating the need for technical expertise to interact directly with APIs
      • Search mechanism for API operations, query parameters, and request attributes
      • Version comparison and tailored response generation
      • Backend API execution according to user query needs

       

      Outcomes
      • Business users are now empowered with a chat-based interface for querying API details
      • Users can seamlessly explore APIs, streamlining collaboration with the API team and reducing onboarding time to one or two days, ultimately enhancing the customer experience for all stakeholders.
      • Developer productivity improved with the AI-powered tools in the API Developer Portal
      • Functionality is enhanced from the version comparison, individual API operation search, and tailored responses
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      Gen AI summarization solution aids lending app users

      Banking

      Gen AI summarization solution aids lending app users

      Conversational agent built with Gen AI eases commercial lenders’ access to information, use of complex applications, and integration of new users.

      Client
      Commercial banking unit of a large Canadian bank
      Goal
      Help lenders access information for complex lending applications on more timely basis and simplify onboarding of new users
      Tools and Technologies
      PyPDF2, Meta
      Business Challenge

      As a part of the credit adjudication process for a transaction, commercial bankers use an application to create summaries, memos and rating alerts as needed, which are instrumental for ongoing Capital at Risk (CaR) monitoring, Risk Profiling, Risk Adjusted Return on Capital (RAROC) computations, etc.

      There is a significant amount of complexity involved in understanding this application due to the diversity in types of borrowers / loans, nature of collaterals, etc., e.g., How to create a transaction report for my deal? How to update an existing deal?

      All of this information is spread across multiple user guides and FAQ documents that may run into hundreds of pages.

      Solution
      • Ringfenced a knowledge base comprised of the user guides of various functionalities (e.g., facility creation, borrower information, etc.)
      • Built a custom-developed, React-based front-end for the conversational assistant to interact with the users
      • Parsed, formatted and extracted text chunks from these documents using libraries such as PDF Miner, PyPDF2
      • Created vector embeddings using sentence transformer embedding model (all-MiniLM-L6-v2) and stored as indices in the Facebook AI Similarity Search (FAISS) vector database
      • Broke down the user query into vector embeddings, searched against the vector database and leveraged local LLM (Llama-2-7B-chat) to generate summarized responses based on the context passed to it by the similarity search
      Outcomes

      Our custom solution was a conversational agent built using Generative AI, which summarizes relevant information from multiple documents.

      It significantly:

      • Improved existing users’ ability to access relevant information on a timely basis
      • Simplified the migration of bankers and integrations of lending applications resulting from merger or acquisition
      Contact

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      Delivering intelligence with speed and scale

      Delivering intelligence with speed and scale

      Data Science Engineering and Data & ML Ops are key to enable scaling of the intelligence part of the data monetization lifecycle in cloud.




        Delivering intelligence with speed and scale

        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.

        Through a number of digital initiatives over the past decade, organizations have collated a lot of information. In addition to structured data, they are collating unstructured and semi-structured formats, e.g., digitized contracts and audio/video of customer interactions. The opportunities to apply established and emerging AI/ML techniques and models to this wide variety of information and derive intelligence and enhanced insights have significantly increased.

        Cloud and the evolving technologies around Data Engineering, Data & ML Ops, Data Science, and AI/ML (e.g., Generative AI) offer a significant opportunity to overcome the limitations and deliver intelligence with speed and at scale. While the number and sophistication of AI/ML models available have increased and become easier to deploy, train/tune, and use, they need information at scale to be transformed to features. Delivering intelligence in scale would require more than just data lakes and lake-houses. It also requires the overall ability to support multiple modeling/data science teams working on multiple problems/opportunities concurrently. Data Science Engineering and Data & ML Ops are key to enable scaling of the intelligence part of the data monetization lifecycle. Teams need to understand data science/modeling lifecycles to effectively scale intelligence.

        In conclusion, organizations demand intelligence in scale and at speed. Emerging technologies like Generative AI demand more powerful infrastructure (e.g., GPU farms). Cloud technologies and services enable these. With support for Python across the intelligence lifecycle, it has become easier to bring together data engineering and data science teams that are easier to provision and use in cloud.

        To know more about the benefits, challenges, and best practices for scaling various stages of deriving intelligence from data on cloud environments, read the perspective paper here.

        Download Perspective Paper




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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.

            Download Perspective Paper




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              Conversational assistant boosts AML product assurance

              Banking

              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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              Comprehensive solutions with seamless system integration

              Agile, feature-rich and scalable API platforms to stay competitive, secure and compliant

              Comprehensive solutions with seamless system integration

              Agile, feature-rich and scalable API platforms to stay competitive, secure and compliant

              Service Offerings

              Value We Provide

              Enabling Secure, Scalable and Resilient Operations

              Our expertise and domain knowledge enable us to create agile platforms that deliver value across multiple parameters

              Versatile Systems with Seamless Optimization

              Our tech teams create holistic solutions that can be adapted across business volumes, regions and environments

              Flexibility to Maximize User Experience

              Our nimble approach allows clients to customize user features from smooth customer on-boarding to intuitive dashboards and complete life-cycle management

              Success Stories

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                How developer portals help you win in the API economy

                How to win in the API economy with API Developer Portals

                In an increasingly API-driven economy, an all-inclusive API Developer Portal can differentiate an enterprise from its competitors.




                  How developer portals help you win in the API economy

                  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.

                  The evolution and adoption of enterprise digital transformation have made APIs critical for integration within and across enterprises as well as for product/service innovation. As APIs grow in scale and complexity, establishing a developer portal would significantly ease the process of their roll-out and adoption. This perspective paper explores the significance of an API Developer Portal in the modern digital landscape driving the API economy.

                  A Developer Portal makes it easier to understand APIs, reduces integration time, and supports developers in training and resolving API-related issues. This provides significant business value by improving agility and enhancing customer experience. With the help of a Portal, enterprises can efficiently publish and consume APIs and enable their integration with incremental API versions. This will ensure benefit from all digital investments.

                  In an increasingly API-driven economy, an all-inclusive API Developer Portal can differentiate an enterprise from its competitors, help build trust with partners, and achieve long-term success. Depending on the API platforms being used, enterprises could adopt a built-in platform or develop a custom one. Developing a custom API Portal would be easy at the start. However, developing enhanced features would entail a significant investment of time and resources. Hence, to make the right decisions and succeed in the broader API implementation/integration journey, a well-thought-out approach is necessary.

                  To learn more about the key drivers, components and features, implementation options and potential benefits of API Developer Portals, download the perspective paper here.

                  Download Perspective Paper




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