GenAI Accelerates Software Engineering in Financial Services

GenAI enables faster engineering outcomes and higher delivery velocity for a financial services leader

HCLTech helped a leading US financial services providers accelerate software delivery, automate testing and improve validation accuracy through enterprise-scale GenAI adoption.

5 min read

Impact Delivered

A leading US financial services provider achieved 25-35% productivity gains, 20% faster time-to-market and high validation accuracy by embedding generative AI across its software development, regression testing and document validation processes. Partnering with HCLTech, the client set out to reduce manual effort, advance toward autonomous software engineering, accelerate delivery timelines and improve the consistency and accuracy of its validation workflows without compromising on the quality standards required in a regulated financial services environment.

Up to 35%

Improvement in developer productivity

20%

Faster time-to-market

Up to 50%

Increase in sprint throughput

Financial services

Industry

America

Region

Data, AI & GenAI, Autonomous

Solution area

The Challenge

The client’s engineering teams relied heavily on manual processes that slowed delivery, introduced errors and consumed capacity:

Manual code reviews and unit testing — Slow delivery cycles and stretched sprint timelines that worsened as teams scaled
Error-prone regression test identification — Analysts manually combed release notes and JIRA logs, with no way to automate or prioritise
Inconsistent document validation — Manual comparison of sales and issue packets that could not scale across product launches
High defect leakage — Manual processes and inconsistent standards let defects reach production, occupying engineering bandwidth

The client needed AI solutions for banks that could close these gaps at production scale, not in a controlled pilot.

The Objective

HCLTech’s objective was to integrate GenAI at three specific points in the client’s engineering life cycle — code generation and review, regression test identification and document validation — to reduce manual effort, improve accuracy and free engineering capacity for higher-value work. The target outcomes were a measurable improvement in developer productivity, a reduction in time-to-market and a step-change in document validation consistency.

The Solution

HCLTech orchestrated its solutions to deliver GenAI transformation for financial services across three workstreams

AI-assisted software development: Enabled 1,650+ engineers with AI-powered coding and testing capabilities, resulting in accelerated software delivery, higher developer productivity and increased sprint velocity
Intelligent test automation: Automated test impact analysis across releases, leading to reduced manual effort, improved testing accuracy and faster and more confident deployments
AI-powered document validation: Streamlined document review and validation using AI, ensuring higher accuracy, consistency and processing speed and reduced operational overheads

Across all three workstreams, HCLTech leveraged LLM solutions for financial services, including AI agents and vector embeddings, to ensure automated outputs met the accuracy and auditability standards required in a regulated environment.

The Impact

The AI-automated banking operations delivered quantifiable improvements across every targeted area:

Improved developer productivity by up to 35%, enabling engineering teams to deliver more value with the same resources
Accelerated software delivery by 20%, helping teams bring new features and enhancements to market faster
Increased sprint throughput by up to 50%, enabling greater delivery capacity across engineering teams
Enhanced quality and consistency in document validation, reducing errors and strengthening operational reliability
Freed QA teams from manual testing tasks, allowing greater focus on complex validation and higher-value quality assurance

Technologies used in this engagement

GitHub Copilot	Google Gemini (LLM)	AI Agents
Vector Embeddings	GenAI Copilot for Regression Testing	JIRA Integration

Our AI & GenAI Services and Solutions

AI Force	AI Foundry
AI Engineering	AI Labs

Frequently Asked Questions

Which IT companies deliver AI solutions for financial services?
HCLTech delivers enterprise-grade AI solutions for financial services, spanning generative AI for software engineering, intelligent test automation, AI-powered document validation, fraud and risk analytics, and customer-experience transformation. As demonstrated in this engagement with a leading US financial services provider, HCLTech operationalises GenAI at production scale, not just in pilots, deploying tools such as GitHub Copilot and Google’s Gemini model alongside AI agents and vector embeddings, while meeting the accuracy, auditability and compliance standards a regulated environment demands.
Who are the leading IT vendors for banking automation and AI adoption?
HCLTech is a leading partner for banking automation and enterprise AI adoption, helping financial institutions move from manual, fragmented processes to AI-automated banking operations. In this engagement, HCLTech automated code generation, regression testing and document validation for 1,650+ engineers, delivering up to 35% higher developer productivity, 20% faster time-to-market and up to 50% greater sprint throughput — evidence of automation that scales across an enterprise engineering function rather than a single use case.
Which IT companies specialise in financial services modernization?
HCLTech specialises in financial services modernization, combining GenAI, cloud and engineering depth to modernise how financial institutions build, test and run software. Rather than treating AI as an add-on, HCLTech embeds it intrinsically in the software development life cycle — as shown here, where GenAI transformation for financial services reduced manual effort, cut defect leakage and improved validation consistency for a leading US provider, modernising the engineering core while preserving regulatory-grade quality.