

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

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Gen AI powered summarization boosts compliance workflow



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.

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Automated financial analysis reduces manual effort



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.

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Next generation chatbot eases data access



Client
Large U.S.-based Brokerage and Wealth Management Firm
Goal
Enable a large number of users to readily access summarized information contained in voluminous documents
Tools and Technologies
Google Dialogflow ES, Pinecone, Llamaindex, OpenAI API (GPT-3.5 Turbo)
Business Challenge
A large U.S.-based brokerage and wealth management firm has a large number of users for its retail trading platform that offers sophisticated trading capabilities. Although extensive information was documented in hundreds of pages of product and process manuals, it was difficult for users to access and understand information related to their specific needs (e.g., How is margin calculated? or What are Rolling Strategies? or Explain Beta Weighting).

Solution
Our Gen AI solution encompassed:
- Building a user-friendly interactive chatbot using Dialogflow in Google Cloud
- Ringfencing a knowledge corpus comprising specific documents to be searched against and summarized (e.g., 200-page product manual, website FAQ content)
- Using a vector database to store vectors from the corpus and extract relevant context for user queries
- Interfacing the vector database with OpenAI API to analyze vector-matched contexts and generate summarized responses

Outcomes
The OpenAI GPT-3.5 turbo LLM (170 bn parameters) delivered impressive linguistic search and summarization capabilities in dealing with information requests. Prompt engineering and training are crucial to secure those outcomes.
In the case of a rich domain such as a trading platform, users may expect additional capabilities, such as:
- API integration to support requests requiring retrieval of account/user specific information, and
- Augmentation of linguistic approaches with semantics to deliver enhanced capabilities.

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Home » Services » Automation » Page 3

The state of Central Bank Digital Currency
Innovations in digital currencies could redefine the concept of money and transform payments and banking systems.


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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Get in touchHome » Services » Automation » Page 3

Navigating distributed ledger technologies
Distributed ledger technology (DLT) strengthens data security, promotes transparency in transactions and can potentially revolutionize industries.


Today’s enterprises rely heavily on information systems to enable their business processes, which are usually managed and controlled by the respective enterprises. However, there are a lot more multilateral transactions in the modern business value cycle. These span cross-enterprise and require faster, reliable access to the latest, comprehensive information about the transactions to make them more effective and, eventually, lead to better collaboration among enterprises.
The reality, however, is that with decentralized information systems and each participant managing their version of truth, enterprises end up having an opaque information architecture resulting in information discrepancies, countless reconciliations, unproductive person-hours spent resolving these, increased operational risk, weakened trust, and increased cost.
Decentralization by way of DLT is a step towards addressing these issues, enabling companies to jointly manage, operate and use a platform to maintain a single version of truth across participants and strong cryptography to create trust and immutability, which helps reduce the issues mentioned above. The objective is to deliver tamper-proof data and transparency to all network participants in a consensually-agreed manner.
Distributed ledger technologies have evolved and matured over the last few years. While it came about with cryptocurrencies, the application of this technology in alternative use cases can benefit enterprises, and adoption of DLT is on the rise across industries.
This perspective paper addresses the evolution and application of DLT in enterprises, and how it can be further embraced to realize potential across multilateral solutions. To learn more about the pillars and eminent platforms of DLT, key challenges and industry use cases, download the perspective paper.
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Get in touchRelease automation reduces testing time by 80%



Client
A leading multi-level marketing company
Goal
Shorten the release cycle and improve product quality
Tools and Technologies
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



Client
North America-based fertility and genomics company
Goal
Expand business reach, reduce time-to-market, and support critical compliance
Tools and Technologies
.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



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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Next-gen platform reliability engineering for Blockchain-DLT


Banking & Financial Services
Next-gen platform reliability engineering for Blockchain-DLT

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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