Gen AI Sample

Success story

SAMPLE GEN AI

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 client is a large global bank with a widespread customer base. The application support and global product assurance teams 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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Join us at Reuters Future of Insurance USA 2024

Join us at Reuters Future of Insurance USA 2024

Join us at Reuters Future of Insurance USA 2024

Leap ahead in Insurtech innovation and tech transformation; meet Venkat Laksh, Iris’ insurance expert, at #FOIUSA 2024.

Reuters Events hosts its annual Future of Insurance USA Conference on May 15-16, 2024, in Chicago, Illinois. The theme is Reset Strategy, Harness AI, Outsmart Disruption. Each thematic directive is a highly relevant priority for the 500+ strategic leaders and technology experts that are expected to attend the event. Venkat Laksh, Iris Software’s Global Lead – Insurance, will be one of the attendees.

With ongoing cost, regulatory and competitive pressures affecting insurance companies, the focus on innovation and technology, particularly AI, is unrelenting. It is the basis of near- and long-term strategies to enhance insurance product offerings, customer experience, operational efficiency, risk reduction and business growth.  These concepts comprise the vast majority of the agenda topics for the Conference’s 100+ speakers, and will no doubt be at the forefront of peer networking conversations.  

They are also the focus of the Insurtech services and solutions that Iris successfully provides to top property, casualty and life insurance carriers.

  • Revolutionizing insurance enterprises with technology
  • Implementing Generative AI
  • Reimagining insurance product design
  • Transforming with Data & AI
  • Transitioning from legacy systems to cloud
  • Elevating customer experience with innovation
  • Ensuring flexible data foundations for AI success
  • Linking and winning in digital transformation and innovation

Talk about your Insurtech priorities with Venkat Laksh, Iris’ insurance expert, at Reuters Future of Insurance USA 2024 and learn how insurers are applying Iris solutions in AI/ML, Application Modernization, Automation, Cloud, Data Science, Enterprise Analytics, and Integrations to leap ahead in their digital transformation goals.

You can also contact Venkat and learn more about our InsurTech Services and Solutions that help future-proof insurance enterprises here: Insurance Technology Services | Iris Software.

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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
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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.

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Life Insurance & Annuity Conference 2024

Life Insurance & Annuity Conference 2024

Life Insurance & Annuity Conference 2024

Meet our insurance expert, Venkat Laksh, at the Life Insurance & Annuity Conference 2024 to learn how advanced technologies power insurers’ growth.

With the theme, Powering Growth, the 2024 Life Insurance & Annity Conference, jointly hosted by LIMRA, LOMA, ACLI, and SOA, will be held at the Marriott Rivercenter in San Antonio, Texas, from April 15 to 17, 2024.

The Conference will feature peer networking and expert insights, with more than 30 workshops that will provide deep dives into topics of major significance to the insurance industry, including: technological and product innovation; financial crime and compliance; consumer, regulatory and resource demands; and market growth.

While you’re at this year’s Life Insurance & Annuity Conference, look up Venkat Laksh, Iris Software’s global lead in insurance, or contact him anytime afterward, to learn how insurers are applying our InsurTech Solutions - in AI/ML, Application Modernization, Automation, Cloud, Data Science, Enterprise Analytics, and Integrations – to power their growth, by optimizing business competencies, enhancing customer experiences, meeting challenges, and securing digital transformation.

You can also contact Venkat or learn more about Iris’ InsurTech Services and Solutions that help future-proof insurance enterprises here: Insurance Technology Services | Iris Software.

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Automated financial analysis reduces manual effort

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.

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Next generation chatbot eases data access

BROKERAGE & WEALTH

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

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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      Meet us at InsureTech Connect Vegas 2023

      Meet us at InsureTech Connect Vegas 2023

      Meet us at InsureTech Connect Vegas 2023

      Meet our insurance expert, Venkat Laksh, at InsureTech Connect 2023 and learn how insurers use our InsurTech Solutions in automation, AI, data & analytics, and cloud, to secure the digital future of their businesses.

      With the theme, “The Future of Insurance is Here,” InsureTech Connect’s annual conference, ITC Vegas, noted as the world’s largest gathering of insurance innovation, will be at Mandalay Bay in Las Vegas, Nevada, from October 31 to November 2, 2023.

      The Conference features 14 educational tracks that will showcase insurance industry leaders, top use cases, and actionable insights relevant to the 9,000+ insurers, innovators and entrepreneurs from around the world who are expected to attend. Technology applications and advancements will be a major focus in each session.

      Meet up with Venkat Laksh, Iris Software’s global lead in insurance, at ITC Vegas 2023 or afterward to learn how insurers are applying our InsurTech Solutions in automation, AI, data science, enterprise analytics and cloud, to amplify their business competencies and secure their digital futures.

      You can also contact Venkat or learn more about the InsurTech services and solutions that help future-proof insurance enterprises here: Insurance Technology Services.

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      Connect at the NMSDC 2023 Conference & Exchange

      Connect at the NMSDC 2023 Conference & Exchange

      Connect at the NMSDC 2023 Conference & Exchange

      Meet Venkat Laksh, Global Lead - Insurance, at the National Minority Supplier Development Council 2023 Conference in Baltimore to learn how our advanced tech solutions and success as an MBE benefit our clients.

      As a long-time, certified Minority Business Enterprise (MBE) and strategic partner of the National Minority Supplier Development Council (NMSDC), we are pleased to again participate in its annual Conference & Exchange. This year, it’s at the Baltimore, Maryland Convention Center from October 23-25, 2023.

      Venkat Laksh, Global Lead - Insurance, will represent Iris. Connect with him there, or at any time, to learn how our advanced technology solutions and services benefit our clients’ digital transformation journeys as well as support their CSR and DEI commitments.

      Iris’ successful growth journey over the past 32 years and our experience delivering Automation, Cloud, Data & Analytics, and Integrations, leveraging emerging tools like artificial intelligence (AI), machine learning (ML), and natural language processing (NLP), to Fortune 500 and other companies in varied industries, including Financial Services and Insurance, sync perfectly with NMSDC’s mission and the Conference agenda. You can find those here: About (nmsdcconference.org).

      The NMSDC Conference & Exchange provides several days of networking and educational opportunities for C-suite executives, supplier diversity and procurement professionals, and MBEs. Take the opportunity to connect with Venkat Laksh at the 2023 Conference or afterward to discuss how Iris’ capabilities can help your enterprise realize the benefits of future-ready technology.

      You can also visit Industry-specific tech services to learn more and contact us.

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