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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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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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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      Test Automation Speeds Model Risk Management System

      Test Automation Speeds Model Risk Management System

      Banking

      Test Automation Speeds Model Risk Management System

      Automated testing for a top international bank's model risk management system brings efficiency and reliability.

      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

      Contact

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      Anti-money laundering software saves $1M

      Anti-money laundering software saves $1M

      Banking

      Unified AML proves to be a game changer

      Global bank overcomes Anti-Money Laundering monitoring challenges and saves $1M in infrastructure costs with a unified front end.

      Client

      A top 5 global bank

      Goal

      Create a unified platform for anti-money laundering functions, analytics, and compliance implementations

      Tools and technologies

      Angular 5, Java, Open Shift, and DevOps

      BUSINESS CHALLENGE

      The client expanded its fraud and anti-money laundering (AML) monitoring functions, involving multiple lines of business and 15,000 employees. The scaled system led to the lack of standardization of frameworks and resultant adoption of disjointed, manual-intensive, and high-cost AML technology. The ongoing disconnect hindered the efforts of automating, consolidating; and implementing AML functions, enterprise analytics, and regulatory compliance efficiently throughout the organization.

      SOLUTION

      Iris optimized existing operations and technology investments by developing and implementing a unified point of access for the discrete AML functions, featuring micro-front-end architecture. Engineered to be horizontally scalable through containerization with common authentication and authorization gateways, the single user interface (UI) allows onboarding and control of multiple extended AML functions, including visualization of metrics.

      OUTCOMES

      The solution amplified efficiencies and reduced costs through the automated system and seamless exchanges of information. Significant outcomes included:
      • Hassle-free transition from multiple to a single UI
      • Unified, streamlined user experiences with more effective sessions
      • Creation of standardized deployment procedures for AML rules and applications
      • Saving of nearly $1M on infrastructure costs
      • Reduced infrastructure maintenance time
      • Frictionless migration of applications to the cloud

      Contact

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      Reporting transformation with data science and AI

      Reporting transformation with data science and AI

      Banking

      Data science and AI transform disclosure and reporting

      A multinational bank leveraged data automation to achieve major gains in reporting efficiency, with 99% accuracy in processing variable inputs, for its global investment fund.

      Client

      One of the world's leading bank

      Goal

      Improve efficiency in disclosure and reporting

      Tools and technologies

      Python – SciPy, Pytesseract, NumPy, Statistics

      BUSINESS CHALLENGE

      The client relies upon a centralized operations team to produce monthly NAV (Net Asset Value) and other financial reports for its international hedge funds— from data contained in 2,300 separate monthly investment fund performance reports. With batch receipts of rarely consistent file formats – PDF, Excel, emails, and images— the process to read each report, capture key info, and, create and distribute new metrics using the bank’s traditional tools and systems was highly manual, time-consuming, error-prone, and costly.

      SOLUTION

      Iris developed a Data Science solution that rapidly and accurately extracts tabular data from thousands of variable file documents. Using a statistical, AI-based algorithm featuring unsupervised learning, it auto-detects, construes, and resolves issues for every data point, configuration, and value. Complex inputs are calculated, consolidated, and mapped as per predefined templates and downstream business needs, efficiently generating numerous, distinct, and required period-end financial disclosures.

      OUTCOMES

      The high solution accuracy helped the client’s global NAV reporting team significantly improve precision, efficiency, quality, turnaround time, and flexibility. The delivered solution contributed to:
      • 90 - 95% reduction in operational efforts
      • 99% accuracy in processing variable inputs
      • Zero rework effort and cost
      Our highly customizable and scalable solution can be seamlessly integrated with existing reporting applications and MS Outlook while accommodating additional volumes, report types, and business units.

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      A playbook for banks on managing M&A integration

      A playbook for banks on managing M&A integration

      Banking

      A playbook for banks on M&A integration

      Efficient management of the complexities of disparate systems and data after an acquisition saves time and money.

      Client

      Banks that have merged or acquired new businesses.

      Goal

      Manage migration and integration complexity post M&A.

      Tools and technologies

      The Iris business acquisition playbook for banks.

      BUSINESS CHALLENGE

      In a low-interest rate regime, achieving scale is the only way for banks to stay profitable. The top 25 banks are growing at a rate faster than rest of the pack. The search for profitability from scale is predicated upon their ability to ensure that operational costs do not grow linearly with business. A significant part of this growth will come inorganically. Apart from M&As, brownfield expansion comes with banks selling off their books of business for reasons ranging from realigned strategic priorities to the more mundane need of raising cash. Any IT costs in absorbing the new book of work will negate the advantages of size.

      Solution

      Iris has been working with banking clients to create a business acquisition playbook outlining steps to insource with a migration and integration strategy. We have enabled clients to deal with post-merger integrations and create a single source of truth for transactional data and positions. The Iris team delivered solutions specifically tailored for applications in the loan origination and servicing space. We have helped our banking clients:
      • Consolidate multiple acquisition playbooks to create a single standardized framework for their lending business
      • Define insourcing steps for business and technology teams and create a migration strategy with quantifiable recommendations and a reusable checklist for insourcing activities.
      • Assess capability and readiness and help them choose from insourcing options:
      • Achieve full migration of data and systems
      • Achieve partial migration of systems and data migration and integration
      • Manage data integration and connectivity for lending business.

      Outcomes

      We have helped clients achieve 50% savings in cycle time and cost for post-merger integration of business processes, application and data.

      Related Stories

      Conversational assistant boosts AML product assurance

      Gen AI-powered responses enhance the operational efficiency of the AML global product assurance team and reduce cost.

      Learn more

      Automated financial analysis reduces manual effort

      Analysts in commercial lending and credit risk units are able to source intelligent information across multiple documents.  

      Learn more

      Test Automation Speeds Model Risk Management System

      Automated testing for a top international bank’s model risk management system speeds efficiency and reliability

      Learn more

      Contact

      Our experts can help you find the right solutions to meet your needs.

      Get in touch

      Anti-money laundering: managing regulatory risks

      Anti-money laundering: managing regulatory risks

      Banking

      Big Data platform improves global AML compliance

      A multinational bank leverages big data platform to improve Anti Money Laundering (AML) compliance and prevents global clients and franchises from financial crimes.

      Client

      A leading global bank with operations in over 100 countries

      Goal

      Address data quality and cost challenges of legacy AML application infrastructure

      Tools and technologies

      Hadoop, Hive, Talend, Kafka, Spark, ETL

      BUSINESS CHALLENGE

      The client’s legacy AML application infrastructure was leading to data acquisition, quality assurance, data processing, AML rules management and reporting challenges. High data volume and rules-based algorithms were generating high numbers of false positives. Multiple instances of legacy vendor platforms were also adding to cost and complexity.

      SOLUTION

      Iris developed and implemented multiple AML Trade Surveillance applications and Big Data capabilities. The team designed a centralized data hub with Cloudera Hadoop for AML business processes and migrated application data to the big data analytical platform in the client’s private cloud. Switching from a rule-based approach to algorithmic analytical models, we incorporated a data lake with logical layers and developed a metadata-driven data quality monitoring solution. We enabled the support for AML model development, execution and testing/validation, and integration with case management. Our data experts also deployed a custom metadata management tool and UI to manage data quality. Data visualization and dashboards were implemented for alerts, monitoring performance, and tracking money laundering activities.

      OUTCOMES

      The implemented solution delivered tangible outcomes, including:

      • Centralized data hub capable of handling 100+ PB of data and ~5,000 users across 18 regional hubs for several countries
      • Ingestion of 30+ million transactions per day from different sources
      • Greater insights with scanning of 1.5+ Billion transactions every month
      • False positives reduced by over 30%
      • AML data storage cost reduced to <10 cents per GB per year
      • Extended support to multiple countries and business lines across six global regions; legacy instances reduced from 30+ to <10

      Related Stories

      Conversational assistant boosts AML product assurance

      Gen AI-powered responses enhance the operational efficiency of the AML global product assurance team and reduce cost.

      Learn more

      Automated financial analysis reduces manual effort

      Analysts in commercial lending and credit risk units are able to source intelligent information across multiple documents.  

      Learn more

      Test Automation Speeds Model Risk Management System

      Automated testing for a top international bank’s model risk management system speeds efficiency and reliability

      Learn more

      Contact

      Our experts can help you find the right solutions to meet your needs.

      Get in touch
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