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
- Vinodh Arjun, Vice President, Head of Cloud Practice
- Prem Swarup, Vice President, Head of Data & Analytics Practice
- Ravi Chodagam, Vice President, Head of Enterprise Services Sales
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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Get in touchGen 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
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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.
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.
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Industrializing business-critical end-user compute-based applications using low-code platforms
Low-code platforms enable rapid conversions to technology-managed applications that provide end users with rich interfaces, powerful configurations, easy integrations, and enhanced controls.
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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Get in touchConversational 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
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Gen 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.
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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.
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 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
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 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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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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