As global markets recover, the economic outlook for the long term warns of possible turbulence for the financial sector. To thrive in this environment, financial services organizations must enhance their agility and prioritize cost efficiency.

In addition, businesses must also contend with the advent of Generative AI (Gen AI). Gen AI is one of the most exciting and innovative technology fields today. It can create new and valuable content, from images and text to music and code. But what are the implications of Generative AI for the financial services industry? How will it affect how we manage data, security, and customer experience?

In this blog, we will explore six Data and AI trends that experts at Snowflake are watching closely for 2024. We will also discuss how these trends, along with other developments such as cybersecurity and open-source technologies, will shape the future of finance.

Generative AI will Play a Critical Role in Future Industry Developments

Gen AI can give financial institutions a competitive edge by simplifying customer experience, streamlining operations, and reducing waste. However, implementing it can be complex, costly, and insufficient for creating a competitive differentiation.

Training and finetuning large language models (LLMs) can be expensive. Hence, organizations must decide whether to create their own LLM, finetune a commercial/OSS LLM, or implement a commercial LLM for the entire organization, considering factors such as access control and budget availability.

Essential data requirements for Gen AI and LLMs, as identified by technology experts, include: 

  • Strong Security and Governance: Vital for model training and usage, these measures protect personal data, ensure regulatory compliance, promote ethical data usage, and mitigate risks. 
  • Data Scale: Training or finetuning LLMs requires access to extensive data sets. 
  • Data Quality: The effectiveness of AI systems is directly linked to the quality of the underlying data. Poor data quality can introduce biases. 
  • Computing Power: AI models demand substantial computational resources for processing large datasets and making complex real-time computations.

A flexible, cost-effective data platform is foundational for accessing Gen AI. Organizations may opt for ready-made commercial and open-source LLMs for more straightforward tasks or finetune their LLMs for more complex applications. 

According to Snowflake’s insights, Gen AI has transformed customer interactions in banking and insurance by providing faster response times and simplified processes. They are using Gen AI to rebuild trust with clients after the 2008 crisis by improving client interactions through chatbots that can address investment queries and advanced data analysis. 

The strategic Gen AI adoption improves customer satisfaction and empowers professionals with deeper insights, setting a new standard in financial services. However, it is essential to note that while Gen AI has unparalleled potential for productivity and innovation, it also requires careful management to avoid risks.

Integrating Specialized AI with Data Sharing is a Business Differentiator

With the constant economic fluctuations globally, financial institutions are actively exploring new avenues for revenue generation, with data sharing emerging as a lucrative option. Leveraging their extensive client and transaction data, these companies are powering Gen AI-driven insights internally and externally.

Custodian banks, for instance, are developing Gen AI-enhanced applications that offer advanced asset and security servicing data, models, and analytics to institutional clients. Payment firms are utilizing transaction data combined with analytics to aid customers in fraud prevention and risk management. Similarly, exchanges and data providers merge their data with AI tools to create distinctive offerings for their clientele. This approach promises to streamline operations, reduce waste, and accelerate time-to-analytic insight within a data-sharing ecosystem.

The challenge, however, lies in sharing this data securely and efficiently, which traditional data platforms struggle with. Modern financial services require a contemporary data platform that supports anonymization and secure sharing of data without the need for physical movement or copying, thereby safeguarding personal information. Financial institutions that adeptly monetize their data through apps and data marketplaces are poised to capture a significant competitive edge.

According to a report, a Snowflake expert highlights that data sharing is revolutionizing the data experience for Snowflake’s clients. This process eliminates the need to physically transfer large amounts of data, opening new opportunities for generating revenue and making informed business decisions.

Unstructured Data Mining Yields Distinctive Analytical Insights

Much of the global data is unstructured and unlikely to change. Financial firms capable of harnessing this data for AI insights will most likely reap the benefits. Every subsector, including banking, asset management, payments, and insurance, will have new analytics use cases available.

Banks and insurance companies often need to sift through vast documents, such as contracts, reports, credit memos, and other unstructured PDFs, which can be time-consuming and resource-intensive. Gen AI can assist employees in more efficiently locating and comprehending the information they require for various tasks, including customer onboarding, claims management, due diligence, and company valuations. In addition, payment and insurance companies can utilize AI to analyze big data and deliver faster customer service through chatbots.

Many conventional data management systems face difficulty storing and analyzing large volumes of unstructured data. This unstructured data may include emails, social media posts, scanned documents, and video and audio recordings, among others. However, a contemporary data platform equipped with cloud-based storage and processing capabilities can efficiently scale to handle such data per organizational requirements.

Organizations often have much information about their customers, but it is usually scattered across sources like call center transcripts, tax documents, application forms, and social media posts. This information is typically unstructured and can be challenging to analyze. However, organizations can harness the power of this unstructured data. In that case, they can gain valuable insights to help them improve their customer 360 initiatives, speed up the customer onboarding process, and enhance their ‘Know Your Customer’ procedures.

Robust Data Security Will Drive Sustainable Growth

Data privacy and security concerns are top of mind for financial services leaders. It is rightly so, as the financial services industry was the second-most targeted for cyberattacks, resulting in data compromises in 2022, just behind healthcare. 

In November 2023, the financial services division of Chinese bank ICBC in the US was hit by a cyberattack that caused disruptions in the US Treasury markets. Such an attack highlights the ambivalent nature of Gen AI in financial services cybersecurity. While it can enhance security by identifying risks and offering prompt automated responses, it can also create new vulnerabilities. 

Robust security and governance controls are crucial for any enterprise data strategy, especially now with the implementation of Generative AI. However, if there is poor data control in these areas, it can have significant impacts, such as non-compliance and spiraling costs, as well as adverse effects on the customer experience and reputation damage. This is why organizations will increasingly prioritize security and governance capabilities to maximize the value of Generative AI while minimizing the associated risks. 

Financial institutions will rely on solid data foundations that share, secure, and govern data throughout the business ecosystem as they develop Generative AI solutions. They will prioritize data solutions that work across multiple clouds and platforms with built-in security capabilities that are highly observable and easy to use. These capabilities will help strengthen the business while enabling more effective threat response.

Agile Leadership is Essential for Regulatory Adaptation

Global regulators will scrutinize Gen AI as concerns arise over data privacy, intellectual property rights, and misuse of AI-generated content.  

For instance, in the United States, the Securities and Exchange Commission (SEC) proposed new regulations to prevent companies from prioritizing their interests over their investors. These proposed rules mandate broker-dealers and investment advisers to address any conflicts of interest arising from their use of predictive data analytics and similar technologies while interacting with investors. 

China, too, has released a draft of regulations aimed at promoting healthy development and standardized application of Generative Artificial Intelligence (Gen AI). The European Union is also working on its AI Act, which seeks to enhance data transparency, human oversight, and accountability rules. 

The way companies operate in the cloud is now facing increased regulatory scrutiny. This is particularly challenging for global organizations that must comply with different regulatory requirements in other regions. Many of these requirements may also involve regulatory reporting. Therefore, organizations with a broad geographic reach must account for these diverse reporting requirements. They must adjust their data strategies or architectures to meet these jurisdictional requirements and deploy workloads accordingly. In other words, there is now a new standard for conducting business in the cloud.

Data Strategy Sets Industry Pacesetters

For maximum growth, companies must have a robust data strategy that involves several key elements to leverage Gen AI. Here are four of the most essential elements: 

  • Access: Access data from diverse sources and avoid data silos that compromise quality and efficiency. 
  • Quality: Standardize and validate source data and address potential bias in the framework. 
  • Governance: Implement robust governance policies to maintain data quality across the enterprise. 
  • Security: Secure data and privacy and keep up with changing regulations. 

A holistic approach to the management of data can result in enhanced metadata, consistency, and improved quality of both the input and output of an LLM.


In conclusion, agility and cost-efficiency become paramount as financial sectors navigate through global market recovery and potential turbulence. The emergence of Gen AI is revolutionizing the industry, from streamlining customer interactions to enhancing data processing.   

Although Gen AI offers a competitive edge, it requires thoughtful implementation and management to mitigate risks and drive customer satisfaction and professional empowerment through unparalleled data mastery and customer-centric innovation.

If you’d like to know more about Snowflake Data Cloud and our service offerings, connect with experts at AppsTek Corp today!