IT modernization lowers costs for capital markets

IT modernization lowers costs for capital markets

Capital Markets

IT modernization lowers costs for capital markets

Age-old applications built on legacy technologies were affecting the client’s investment banking functions. Iris modernized the existing application architecture and helped the client save tremendous costs and turnaround time.

Client

A major financial institution

Goal

Re-engineer key compliance application for greater efficiency

Tools and technologies

Oracle 12c, 19c, Elastic, Java and Kafka

BUSINESS CHALLENGE

The client’s existing database application architecture was resulting in poor performance that was adversely affecting 10,000 investment banking functions. The age-old application posture was disrupting the compliance process for the global M&A, IPO, and other financing deals. The client’s customers were encountering problems such as regulations access, inconsistent or unusable data/deal search results, lagging database updates, delayed response time. The need for a capable system to support duplicate searches, and increasing ad-hoc IT redress efforts and costs were other prime concerns.

SOLUTION

Our tech experts re-engineered the entire compliance application and set up a new software system. We upgraded selective elements to improve the infrastructure performance and usage feasibility. The modernized system with new technology architecture streamlined processes and enabled faster, more advanced interactions between the information databases, search queries, and retrieval mechanisms. Complex procedures and the process of viewing features were also simplified.

OUTCOMES

The solution improved the user experience, turnarounds, data quality, deal compliance, and risk management processes while lowering the costs. Major impacts of the solution include:

  • Reduced system issues by 93%, while 7% tackled subsequently
  • Reduced enterprise-level license costs significantly
  • Improved advanced search features
  • Reduced costs of multiple searches per document by $200,000 per year
  • Enabled views generation for info responses in less than 3 seconds
  • Reduced system maintenance and development efforts by 20%

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SFTR solution strengthened market leadership

SFTR solution strengthened market leadership

Risk & Compliance

Securities Financing Transactions Regulation (SFTR) compliance made easy

A global trading company solidifies its EU market leadership with regulatory solution and supporting to a throughput of 6 million transactions per hour

Client

A leading provider of market data and trading services

Goal

Support complex regulatory reporting with automated solution

Tools and technologies

Java, Spring Boot, Apache Camel, CXF, Drools BRE, Oracle, JBoss Fuse, Elasticsearch, Git, Bitbucket, Sonar, Maven

BUSINESS CHALLENGE

The client offers an automated, integrated solution to its clients in the European Union (EU) for complying with the Securities Financing Transactions Regulation (SFTR). Effective in recent years, SFTR requires timely and detailed reporting based on multitudes of data, systems, collateral, and lifecycle events. The voluminous data is captured from hundreds of millions of daily transactions made to multiple trade repositories registered by the European Securities and Markets Authorities (ESMA). Non-compliance at any stage is risky, potentially very costly, for all trade counterparties, i.e., broker-dealers, banks, asset managers, institutional investors.

SOLUTION

Experienced in diverse technologies, big data, and capital markets, team Iris developed a streamlined, end-to-end data reporting platform with complex trade matching and monitoring systems. Improving speed, accuracy, and flexibility, the new architecture supports high trade concurrency and acceptance rates with parallel processing of millions of transactions. The delivered solution also enabled optimal load balancing and matched the reconciliation at the trade repository. Built with microservices to accommodate future scalability, standardization, data quality, and security requirements, the system implemented functional enhancements. A Unique Transaction Identifier (UTI) subsystem was also developed for sharing and matching counterparty transactions, enabling plug-and-play setup for new repositories, and supporting any changes in outbound or inbound data report formats required by ESMA or clients. Improved dashboards and search pages helped the end-users in better configuration and tracking of their transactions.

OUTCOMES

The nimble delivery and successful roll-out of the new SFTR platform delivered the desired strategic competitive advantage to the client for maintaining its EU market leader position. The consolidated solution also helped in:
  • Generating additional revenue from extending the new reporting services to 17 firms.
  • Beating the industry benchmark (~91%), achieving a higher transaction acceptance rate (~97%), and match reconciliation at the trade repository.
  • Supporting a high throughput of 6 million transactions per hour which is scalable up to 10 million.

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The power of in-sprint automation

The power of in-sprint automation

Automation

The power of in-sprint automation

A large securities firm sped up time-to-market with end-to-end test automation on the cloud.

Client

A leading securities trading firm.

Goal

Build a cloud-based automation framework to test client’s trading platform.

Tools and technologies

C#, Ranorex, TestRail, Simulators and Selenium.

BUSINESS CHALLENGE

The client had a legacy trading platform that had grown and evolved over time. The platform consisted of a stack of 33 applications, built on a variety of technologies and architectures. Testing new features and additions was proving to be a big challenge. A simple change in one feature would warrant a verification of the complete application. To ensure that any change does not affect other functionality, the client needed to do extensive regression testing and verification. This was a cumbersome process with over 20,000 or 30,000 test cases being checked and executed manually. The trading firm had to deploy over 20 people to carry out this exercise. The client had tried to automate the testing process with a variety of tools but was not able to get the efficiencies it wanted. In addition, the client had multiple squads working on different apps, functionality and features. Each squad used its own automation suite. It was becoming a challenge to co-ordinate the work of the different squads and ensure that changes made by a squad did not impact the overall functionality of the platform. Iris’s brief was to design and deploy a common cloud-based test automation framework for the client’s trading platform to ensure that it could launch new features faster.

SOLUTION

Using its cloud-based ready-to-deploy test automation framework, Iris sped up the deployment of new features for the client’s trading platform. The cloud solution, based on Amazon Web Services (AWS), featured continuous testing of multiple products on a common framework layer. It allowed for complete capacity planning of spinned cloud instances and need-based shutdowns. Iris executed the project using acceptance test driven development (ATDD), a methodology that involves collaboration between customers, business teams and development teams. The teams jointly created the user stories and put down the acceptance criteria for any feature or functionality. Then tests were designed within the common framework to check if the feature met the acceptance criteria. What was unique about the approach? Typically, automation is introduced towards the end of a development cycle. You would find that, in most projects, developers bring in automation in Sprint 4 for features developed in Sprint 1, 2 and 3. As a result, return on investment isn’t maximized. Our team introduced ‘in-sprint’ automation, enabling 90% test automation with every sprint. This resulted in more efficient and faster testing, and cost savings for the client.

OUTCOMES

The client’s deployment speed improved significantly with 90% faster execution in each sprint cycle and 80% faster script development. The cloud-based solution is 100% configurable for on-demand execution on AWS, which reduced the client’s cloud infrastructure costs by 70%. The new ability for complete capacity planning through the use of infrastructure-as-code (IaC) for spinning up cloud instances helped the client achieve end-to-end (E2E) automation of regression/ functional test cases.

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Contact

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

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Cloud-native app opens new markets

Cloud-native app opens new markets

Cloud

Cloud-native app opens new markets

A prominent bonds trading network expands its market reach with new products and geographies.

Client

The world’s leading provider of trading services for fixed income products

Goal

Create an IT architecture to support growth across markets and products

Tools and technologies

AWS Cloud, Java, Springboot, React JS, React, Redis, Kafka, C#, Ranorex and Test Rails

BUSINESS CHALLENGE

The client, a market leader in bonds trading, was expanding to new markets, acquiring new businesses, introducing new products and adding features to existing offerings. To support its growth plans, it needed an agile, modern, cloud-based platform.

Some of the business needs the client wanted to address with the new solution were:

  • How do we achieve scale with minimal latency in operations and service?
  • How do we integrate new businesses seamlessly and without disruption?
  • How do we roll out new features faster to improve customer experience and get a competitive edge?
  • How can we use data to help customers make better trading decisions?
  • How can we monetize the data?

As a solution partner, we had to not only create a new IT architecture for the client’s trading platform but also constantly re-engineer and improve the architecture to quickly meet emerging business needs.

SOLUTION

We deployed a scalable, highly available auctions solution on the AWS cloud using Java, Springboot, React JS, React, Redis, and Kafka.

Optimized algorithms now achieve best matching with minimal latency while offering full price transparency. Artificial intelligence (AI) and machine learning (ML) provide greater insight and real-time price discovery for specific asset classes.

The new cloud-based architecture enabled the client to create products and monetize market data. Those products helped customers get accurate data in real-time to take better and faster trading decisions.

Test automation across the trade lifecycle using a combination of C#, Ranorex, Test Rails helped the client update user interfaces (UI) without reducing performance. It also eased integration linkages between the acquired solution’s frontend and the client’s existing backend.

OUTCOMES

The introduction of Agile methodology and the cloud-native application has helped the client significantly speed up time-to-market for new releases – it is now able to make releases several times a year.

The new IT architecture now allows the client to offer trading in Muni bonds (an acquired product) and U.S. treasuries (a new service). The solution also enables the client to support Chinese markets.

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Contact

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