Democratizing GenAI for a financial institution

Digital transformation case study

Industry: Financial services
Today's issue: Transformation
Country: Canada

Introduction

A major Canadian bank embarked on a transformative journey to integrate generative artificial intelligence (GenAI) across the enterprise. With ambitions to scale AI capabilities to meet evolving market demands and enhance customer experiences, the bank faced challenges in democratizing access to AI and moving beyond siloed AI experiments.

The bank was looking for a comprehensive platform that would allow it to leverage GenAI across departments while maintaining control, governance, and alignment with business objectives.

Challenges with classical thinking and a traditional approach

Fragmented GenAI efforts: The bank had experimented with various GenAI models across departments (e.g. customer service, risk analysis, and marketing). However, these efforts were siloed, leading to duplication of resources and inconsistent results.

Individual use cases were handled separately, with no cohesive strategy or shared infrastructure. This created inefficiencies, as different teams had to independently source, configure, and deploy generative models.
Narrow focus on specific models: The bank’s AI strategy focused primarily on specific generative models for each use case, limiting the flexibility and scalability of its AI initiatives. Each department was experimenting with different models, and this led to disconnected implementations.

There was limited understanding of the potential of generative applications, which involve integrating multiple models for complex, real-world tasks, and supporting multi-stage AI processes.
Lack of democratization: GenAI capabilities were concentrated within a small team of data scientists. Business users had limited access to AI tools and lacked the ability to experiment with AI, which slowed innovation and limited the potential impact of AI across the organization.
Governance and security concerns: The bank also faced challenges ensuring GenAI models were compliant with industry regulations and secured against risks such as data leakage and biased outputs. The lack of an enterprise-wide platform made it very difficult to govern these models consistently.

Approach

This project aimed to deliver a democratized platform for enterprise GenAI, focusing not just on specific models, but also on creating a comprehensive platform to support generative applications.

Key elements of our approach included:

We advised the bank to move away from focusing on individual generative models and instead think about generative applications—integrated solutions that use multiple generative models at various stages to meet complex business needs.

This shift allowed the bank to consider AI as a strategic business enabler, aligning AI-driven applications with broader business goals rather than focusing on isolated use cases.

Instead of treating each GenAI use case in isolation, we worked with the bank to create a portfolio of use cases that spanned various business units, from customer service automation to fraud detection.

This approach helped the bank realize economies of scale in AI investment, enabling shared infrastructure, models, and governance practices to support multiple use cases simultaneously.

We designed an intuitive, user-friendly platform that enables business users, beyond the core data science teams, to access, experiment with and deploy AI capabilities. This democratization was crucial for fostering innovation across departments and accelerating the time to market for AI-driven solutions.

We implemented a centralized governance framework that enables all GenAI models to comply with the bank’s security, privacy, and regulatory requirements. This framework includes automated checks for model bias, data leakage prevention and explainability.

The platform was built with scalability and security at its core, allowing the bank to confidently deploy AI at scale without compromising compliance or risk management.

We delivered a robust GenAI platform that allows the bank to build and manage generative applications. This platform is capable of integrating multiple models for tasks such as content generation, predictive analytics, and decision making.

The platform supports a wide variety of GenAI models, including customizable and pre-trained models, providing flexibility for different use cases.

By adopting a portfolio-based approach, the bank was able to scale GenAI across the enterprise. This approach allows teams to share best practices, tools, and models, leading to faster innovation and cost efficiencies.

We created a roadmap for new AI use cases, enabling future applications to be built on the existing platform infrastructure. This further reduces the time and cost needed to bring new AI projects to life.

The platform includes interactive decision trees that help users—both technical and non-technical—navigate the complexity of selecting and configuring the right models for their needs. These decision trees align technical choices with business requirements, helping the bank make strategic decisions about AI deployment.

The project introduced a comprehensive AI governance model that enables the bank’s GenAI applications to be compliant with regulatory standards and internal security policies. This includes monitoring for ethical AI practices, such as preventing biased or harmful outputs.

The platform also provides tools for model monitoring, enabling generative applications to be continuously improved and retrained with new data.

Impact

Accelerated innovation

The democratization of GenAI empowered business units across the bank to experiment and innovate with AI, leading to a significant increase in AI-driven projects and faster implementation of AI solutions. The platform enables non-technical users to develop AI-driven applications, breaking the bottleneck of reliance on data scientists.

Scalability and flexibility

The new platform allows the bank to scale AI across multiple departments and business functions. It provides the flexibility to adapt to new use cases and integrate additional models as needed, enabling it to support the bank’s future AI initiatives.

Strategic alignment with business goals

By shifting focus to generative applications that align with the bank’s strategic goals, AI became a core part of the bank’s broader business strategy. AI applications are developed with a clear understanding of their business impact, leading to a higher return on investment and more targeted AI investments.

Operational efficiency

The unified platform and portfolio approach reduces duplication of effort, enabling the bank to operate more efficiently. Shared infrastructure, tools, and governance practices have created a streamlined environment for developing and deploying AI at scale.

Improved governance and risk management

The centralized governance framework enables all GenAI applications to adhere to regulatory standards and mitigates risks related to bias, data security, and explainability. The bank is able to confidently scale AI while maintaining compliance with industry regulations.

Future-proof AI platform

The platform was built to be adaptable, enabling the bank to incorporate new AI models and techniques as they emerge. This future-proof approach ensures the bank’s investment in AI will remain relevant and continue to deliver value over time.

Transforming the bank’s approach to AI to drive innovation, improve customer experiences and enhance efficiency

The democratized GenAI platform transformed the bank’s approach to AI, allowing the bank to scale AI-driven innovation across the enterprise while aligning with its strategic business goals.

By focusing on generative applications rather than individual models, the bank gained a powerful tool to drive innovation, improve customer experiences, and enhance operational efficiency—all while maintaining the highest standards of governance and compliance.

This platform is now a core pillar of the bank’s ongoing digital transformation strategy, positioning the bank as a leader in the use of AI in the financial services industry.