Deploying Generative AI in the Financial Sector

Gen AI is rapidly transforming various industries, showcasing an astonishing pace of innovation. The emergence of tools like ChatGPT, Microsoft Copilot, Midjourney, Stable Diffusion, and others has unlocked unforeseen possibilities compared to just 18 months ago.

Although initiating pilot projects for gen AI applications is relatively straightforward, transitioning them to production-ready, customer-facing solutions presents a new challenge, particularly within the financial services sector. Concerns such as risk, compliance, data privacy, and escalating costs are pressing issues facing financial institutions today.

This blog post aims to address:

  • The potential implications of generative AI in financial services
  • The complexities involved in deploying Large Language Models (LLMs) in production

Key engineering and risk-related considerations essential for successful implementation of gen AI within financial institutions’ operational frameworks.

What is the Potential Impact of Generative AI and Analytics on Financial Services?

The potential yearly value of AI and analytics in global banking could soar to $1 trillion. The transition from analytical AI to generative AI has resulted in significant advancements in advanced analytics capabilities. Generative AI holds promise for substantial additional value, potentially leading to margin improvements of 3-5%, translating to productivity gains worth approximately $200 billion to $340 billion.

Potential Use Cases for Generative AI

Which areas are most suitable for leveraging generative AI? A multitude of potential applications exists across various functions and business units within banking and securities. These encompass numerous scenarios in Marketing, Operations, Legal and Compliance, and Talent and Organization.

For instance, applications range from crafting compelling customer content and profiling wealth prospects to drafting financial reports, monitoring fraud, and generating job profiles. Explore the full list of possibilities in the webinar.

Today, it is estimated that around 75% of the value derived from generative AI applications is concentrated in four primary use cases:

1. Virtual Expert – This involves summarizing and extracting insights from unstructured data sources, efficiently retrieving information to support problem-solving, and verifying the credibility of sources.

2. Content Generation – This includes automating the creation of contracts, non-disclosure agreements (NDAs), and other documents to reduce manual labor, as well as generating personalized messages and product recommendations.

3. Customer Engagement – Features include virtual co-pilots that provide customers with personalized navigational assistance, and sophisticated chatbots that offer round-the-clock customer support.

4. Coding Acceleration – Tasks such as interpreting, translating, and generating code (for instance, scaling up legacy system migrations), creating synthetic data, and developing application prototypes are part of this category.

These applications considerably boost user productivity. To capitalize on the financial benefits discussed, the financial services industry needs to broaden its focus beyond conventional areas like marketing, sales, and risk management. Generative AI could significantly improve operations in sectors such as capital markets, investment banking, asset management, corporate banking, wealth management, retail banking, and others.

What are some generative AI pitfalls?

However, generative AI isn’t suitable for every scenario. It’s advisable to avoid using generative AI in:

  • High-stakes situations where errors, factual inaccuracies, or value judgments could lead to harm, such as in disease diagnostics.
  • Environments with a high volume of requests and/or strict response time requirements, such as high-frequency stock trading.
  • Unrestrained, lengthy, open-ended generation that could disseminate harmful or biased content, like in legal document creation.
  • Settings that demand explainability and/or a comprehensive understanding of possible failure modes (for example, highly regulated industries), such as credit scoring.
  • Tasks that involve numerical reasoning, from basic calculations to optimization, like demand forecasting.

This is due to the significant and unique risks introduced by generative AI. These risk categories include compromised fairness, intellectual property infringement, privacy issues, malicious use, challenges related to performance and explainability, security threats, negative impacts on environmental, social, and governance (ESG) factors, and third-party risks. Even when deploying generative AI for recommended uses, organizations must establish and adhere to strict guardrails to mitigate these risks.

Implementing Generative AI in Production

Effectively scaling and promoting a generative AI application demands a holistic corporate strategy. This involves leadership vision and strategy, allocation of resources, alignment of data, technology, and operational models, comprehensive risk management, and proactive change management.

Key considerations include:

  • Enterprise positioning
  • Data architecture, especially the access to extensive unstructured data (models are essential but not the only requirement)
  • Selection of cloud infrastructure
  • Designing the appropriate UI/UX interface
  • Implications for processes and personnel (incorporating a “human in the loop” and analytics as a critical third component alongside technology and business)

Transitioning a generative AI application to production also entails extensive engineering, surpassing the simplicity of the prototyping stage. The necessary architecture includes data, ML, and application pipelines, along with a multi-stage pipeline. Learn more here.

Exploring the Data Management Pipeline.

Key Elements of Data Management in Generative AI

The data management process begins with a data pipeline that sources data from various origins, performing tasks like transformation, cleaning, versioning, tagging, labeling, and indexing. These steps are crucial for producing high-quality data, which in turn, leads to the creation of superior models.

After data ingestion, it undergoes several transformations, including:

  • Text cleansing and correction
  • Toxicity detection and removal
  • Bias identification and reduction
  • Protection of Personally Identifiable Information (PII)
  • Data deduplication
  • Formatting and tagging
  • Keyword and metadata extraction
  • Data splitting and chunking
  • Tokenization and embedding

Once the data is organized as indexed or feature-rich data, subsequent processes include:

  • Data transmission to Vector Database and Key/Value Store
  • Data security measures
  • Data governance practices
  • Data version control
  • Data cataloging and labeling
  • Assurance of data quality

A key component is metadata management, encompassing:

  • Orchestration of the data pipeline
  • Data lineage and traceability
  • Resource management and observability

For instance, consider a document filled with irrelevant symbols. The initial step involves symbol filtering, followed by deduplication to enhance model accuracy and prevent overfitting. The next steps include anonymizing names and social security numbers, tokenization, and finally, indexing the data in a vector database.

Even after these processes, it remains critical to validate both requests and responses to minimize risks.


Generative AI offers significant opportunities for margin enhancement and operational improvements within the financial services sector. Nonetheless, the path to fully harnessing these benefits is fraught with challenges. Financial institutions must allocate resources towards risk management, compliance, data privacy, and technology integration to capitalize fully on the advantages of generative AI.

In particular, deploying generative AI in production necessitates a well-designed data management pipeline. This pipeline should facilitate the ingestion, transformation, cleaning, versioning, tagging, labeling, indexing, and enhancement of data, thereby mitigating risks and enhancing data quality.

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