Streamlined payments architecture delivers flexible workflow orchestration, system scalability, and improved data reporting.
Client
A leading Australian bank
Goal
Streamline the payment transaction lifecycle to handle increasing volume and complexity
Tools and Technologies
Jenkins, Kubernetes, Spring, Oracle, PostgresSQL, AWS, Docker, Kafka, Java
Business Challenge
Multiple banking channels - mobile, internet and branch - initiate various payment requests (20+ types such as ACH, mandate and book transfer) that need to be processed. Depending on the payment type, the channel was required to invoke one or more services in a specific order as per the associated business rules.
Changes to these payment workflows stemming from introduction of new payment types or revisions of business rules introduced complex and repeated changes to the bank’s systems, hindering scalability.
Solution
- To support lifecycle management of various payment transactions, including defining different payment workflows, we designed an event-driven architecture comprised of 40+ microservices (e.g., limit, eligibility, and fraud checks, etc.) supported by a Kafka message queuing system
- The architecture involved building an orchestration engine (landing service), acting as a front controller for all payment workflow requests from the various banking channels, such as mobile, internet and branch
- The landing service in turn invokes the corresponding service (limit, eligibility, etc.) based on the payment type and business rules associated with it
- Data flow between these microservices (resulting from further invocations) and other downstream systems is facilitated asynchronously with the help of a distributed messaging system (Kafka)
- Using Jenkins, we built a CI/CD pipeline to streamline the workflow by automatically building, testing and deploying code changes as they are committed
Outcomes
- Significantly eased the management of payment workflows, including those related to the addition of new payment types (resulting from an acquisition)
- Enabled systems to scale without introducing complex changes at the channels
- Improved reporting, resulting from faster access to data through dedicated microservices
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Modernized Payments Hub Improves UX and Compliance
Modernized core payments engine elevates client experience through seamless integration and increased functionality.
Client
U.S. operations of a leading Japanese bank
Goal
Modernize payments architecture to streamline processing and improve client experience
Tools and Technologies
Jenkins, Kafka, Spring, Oracle, JBoss, React, Elastic Search, Java, Node.jsBusiness Challenge
The evolving payments landscape, with the introduction of ISO 20022 and the dynamic nature of the regulatory environment, necessitated advancement in the bank’s payment processing capabilities.
The lack of a modern architecture hindered client experience, with multiple channels initiating various payment types that required complex processing.
Solution
Our team built a centralized payments hub to orchestrate data flows between payment initiation systems and product processors. The steps:
- Designed a flexible and scalable microservices-based architecture to facilitate translation, enrichment and processing of payment transactions
- Built a messaging layer to streamline data flows between systems, through support for various modes of interaction, e.g., MQ, API and file (canonical / industry standards such as NACHA, SWIFT, JSON, etc.)
- Introduced an API gateway to handle multiple payment types to enable channel agnostic payment capabilities
- Deployed a modular approach to support existing and new systems with isolation of core and product processors and avoid redundancies in capability builds
- Developed a React-based UI as the touchpoint for integrations between the payments hub and other systems
Outcomes
- A core payments engine capable of seamlessly integrating with multiple, complex systems
- Superior client experience, resulting from a holistic view spanning initiation, payment rails, and clearing
- A modernized payments platform that is ISO 20022-compliant and future-ready for processing and reporting needs
- Faster implementation of functionalities for payment processors
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Automated Scheduling Bots Boost Productivity by 50%
Major productivity gains and increased customer satisfaction from using 24/7 automated bots in place of manual scheduling.
Client
Leading supply chain brokerage
Goal
Automate the manual, supply-chain scheduling process to improve staff productivity and customer satisfaction
Tools and Technologies
UI Path Orchestrator, UI Path Assistant, Microsoft Power BI, Office 365Business Challenge
Performing crucial supply chain logistics, a provider’s operations team was struggling due to the high volume of scheduling appointments with shippers, receivers, and carriers, which involve back and forth emails, phone calls, or manual data entry into multiple Transport Management Systems (TMS).
These appointment-scheduling complexities vary based on the parties involved, from sending an email requesting appointment times to accessing a TMS and selecting what’s available as per their schedule.
Lacking proper analytics, sales representatives were unable to pinpoint peak appointment times, track cancellation rates, or discern customer preferences, often leading to shipment delays and incurred detention charges.
Solution
- Deployed multiple rule-based, automated workflows to pull information from incoming appointment requests (from emails, web forms, etc.) and automatically input it into the various TMS used to book pick-up and delivery appointments
- Developed a Power BI dashboard to visualize appointment trends, peak times, and cancellation rates, providing insights into customer behaviors, including frequent reschedules, preferred times, and typical lead times for booking appointments
- Delivered a reusable solution that could be leveraged for other business areas
Outcomes
- Bots operating 24/7 have led to over 15,000 monthly appointments being scheduled, resulting in a 50% reduction in manual scheduling hours
- The productivity of the operations team has improved by 50%, enabling staff to concentrate on high-value tasks rather than manual appointment-booking
- The increased accuracy in scheduled appointments has significantly decreased detention charges, thereby boosting overall customer satisfaction
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Automated POD improves turnaround time 95%
Optimized Proof of Delivery documentation improves productivity by 87% and billing cycle by 35%.
Client
Leading supply chain brokerage
Goal
Automate Proof of Delivery documentation process to increase efficiency and accuracy in data upload, validation and invoicing
Tools and Technologies
UI Path Orchestrator, UI Path Document Understanding, Microsoft Power BI, Oracle Transportation ManagementBusiness Challenge
Proof of Delivery (POD) is a document that confirms an order has arrived at its destination and was successfully delivered before the invoice can be billed for payment.
Lack of an electronic POD system leads to inefficient, manual processing due to varied legal and contractual documentation requirements, resulting in longer billing cycles. Diverse formats and layouts from different carriers complicate data extraction from paper-based PODs.
Solution
- Developed a Document Processing Bot with UI Path AI Center, leveraging Document Understanding and Optical Character Recognition for managing various carrier documents
- Optimized data models for major carriers, focusing on the top five document types that represent 80% of the volume
- Implemented UI Path Action Center's "Human in the Loop" to handle exceptions and conducted 6-8 weeks of rigorous training on the Document Understanding model to ensure accuracy and meet confidence targets
Outcomes
- Achieved a 95% reduction in POD turnaround time, dropping from 48 hours to 2 hours, significantly boosting customer satisfaction
- Enhanced productivity by 87.5%, confirming receipt and condition of freight efficiently
- Reached 80% process accuracy, with continuous enhancement via automatic retraining
- Cut the billing cycle by 35%, allowing immediate use of data for customer invoicing
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Unified automation strategy enhances efficiency
Automated platform engineering solution improves reliability and lowers costs by 20-50% each for a leading payroll and HR solutions provider.
Client
Leading payroll and HR solutions provider
Goal
Develop automation strategy and framework that accommodates growth and ensures efficiency
Tools and Technologies
Ansible, AWS, Dynatrace, Gremlin, Groovy, Jenkins, Keptn, KICS, Python, TerraformBusiness Challenge
The SRE (Site Reliability Engineering) shared services team faced a diverse set of needs relating to automation of infrastructure and services provisioning, configuration, and deployment.
The team was encountering resource constraints, as limited in-house expertise in certain automation tools and technologies was causing delays in meeting critical automation requirements. They also needed to ensure system reliability and were challenged to scale automation solutions to accommodate increasing demands as operations grow.
Solution
- Development of a comprehensive automation strategy to align with objectives, encompassing Terraform, Ansible, Python, Groovy, and other relevant technologies in the AWS environment
- Leveraging our expertise to bridge the knowledge gap, provide training, and augment the client team in handling complex automation tasks
- Implementation of a chaos engineering framework using Gremlin, Dynatrace, Keptn, and EDA tools, to proactively identify weaknesses and enhance system resilience
- Creation of a scalable automation framework that accommodates growing needs and ensures long-term efficiency
Outcomes
- A unified automation strategy that streamlined processes, reduced manual effort, and enhanced overall efficiency by 30%
- The implementation of chaos engineering and self-healing practices, which increased reliability between 20% and 50%
- A reduction in manual interventions along with improved efficiency that will result in cost savings of 25% - 50%
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Quality engineering optimizes a DLT platform
Banking & Financial Services
Quality engineering optimizes a DLT platform
Reliability, availability, scalability, observability, and resilience ensured; release cycles and testing time improve 75% and 80%.
Client
A leading provider of financial services digitization solutions
Goal
Reliability assurance for a digital ledger technology (DLT) platform
Tools and Technologies
Kotlin, Java, Http Client, AWS, Azure, GCP, G42, OCP, AKS, EKS, Docker, Kubernetes, Helm Chart, TerraformBusiness Challenge
A leader in Blockchain-based digital financial services required assurance for non-GUI (Graphic User Interface), Command Line Interface (CLI), microservices and Representational State Transfer (REST) APIs for a Digital Ledger Technology (DLT) platform, as well as platform reliability assurance on Azure, AWS services (EKS, AKS) to ensure availability, scalability, observability, monitoring and resilience (disaster recovery). It also wanted to identify capacity recommendations and any performance bottlenecks (whether impacting throughput or individual transaction latency) and required comprehensive automation coverage for older and newer product versions and management of frequent deliveries of multiple DLT product versions on a monthly basis.
Solution
- 130+ Dapps were developed and enhanced on the existing automation framework for terminal CLI and cluster utilities
- Quality engineering was streamlined with real-time dashboarding via Grafana and Prometheus
- Coverage for older and newer versions of the DLT platform was automated for smooth, frequent deliverables for confidence in releases
- The test case management tool, Xray, was implemented for transparent automation coverage
- Utilities were developed to execute a testing suite for AKS, EKS, local MAC/ Windows/ Linux cluster environments to run on a daily or as-needed basis
Outcomes
- Automation shortened release cycles from 1x/month to 1x/week; leads testing time was reduced by 80%
- Test automation coverage with 2,000 TCs was developed, with pass rate of 96% in daily runs
- Compatibility was created across AWS-EKS, Azure-AKS, Mac, Windows, Linux and local cluster
- Increased efficiency in deliverables was displayed, along with an annual $350K savings for TCMs
- An average throughput of 25 complete workflows per second was sustained
- Achieved a 95th percentile flow-completion time that should not exceed 10 seconds
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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-chatBusiness 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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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 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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