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
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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-v2Business 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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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 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.
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.
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Get in touchAutomated 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 ReaderBusiness 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
Gen AI tools help users of retail brokerage trading platform obtain information related to specific needs and complex queries.
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 client 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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The State of Central Bank Digital Currency
Innovations in digital currencies could redefine the concept of money and transform payments and banking systems.
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
Insights into the top challenges and their mitigations in the Cloud journey.
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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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Get in touchQuality 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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Get in touchTest Automation Speeds Model Risk Management System
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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