Unveiling the Power of Generative AI for Enterprise Search and Productivity in Financial Services

June 04, 2024

4 min read

Dasha Fomin


Banking and financial services, known for their technical acumen, often lead the charge in adopting new technologies. Financial institutions’ rapid adoption of generative AI is a testament to this trend, with the banking sector’s investment in the new technology projected to skyrocket from $3.86 billion in 2023 to $85 billion by 2030. Currently, most banking organisations have implemented generative AI solutions or have projects in active production. This article will delve into the two most popular use cases of GenAI in the finance industry.

Generative AI and financial services

According to a recent report, North American banks are at the cutting edge of AI innovation, with JPMorgan Chase, Capital One, and the Royal Bank of Canada leading the way. These banks contributed to over 80% of the AI research published by the financial sector last year. Furthermore, Capital One and Bank of America were prominent in the AI patent field, accounting for two-thirds of all patents registered in the financial sector in the year leading up to June 2022.

A recent IBM study says that almost 8 in 10 institutions (78%) implement generative AI for at least one use case. Their strategies differ, often emphasising risk, compliance, and client engagement. Some 8% of institutions deploy generative AI across broader business areas. Many take the careful, guarded approach required to integrate such advanced technologies. For example, Citizens Financial Group explored over 90 different GenAI use cases. Only two – those focusing on data management and productivity – reached the production stage. Indeed, managing vast volumes of data and navigating through complexities are some of the most significant challenges financial institutions face.


Of course, effective information retrieval and management are paramount for operational efficiency and strategic decision-making. The ability to swiftly access accurate, relevant data can impact everything from client services to compliance reporting. However, traditional search technologies often need help to keep pace, leading to inefficiencies and missed opportunities. Herein lies the potential of generative AI to revolutionise these processes.

Indeed, GenAI marks a significant leap beyond traditional AI, generating original content by learning from extensive datasets. It uses advanced machine learning, like Generative Adversarial Networks (GANs), to discern patterns and context from data, producing unforeseen outputs ranging from text to media, with the potential to transform the landscape of enterprise search and productivity in financial services.GenAI understands intents, synthesises information from diverse sources, and enhances search experiences. It offers a leap forward in how financial institutions interact with their vast data reservoirs, paving the way for unprecedented efficiency and insight.

Using GenAI for enterprise search and productivity

Studies indicate that the adoption of generative AI has surged, nearly doubling over the past six months, with three-quarters of knowledge workers worldwide now utilising it. Employees independently introduce AI tools into their professional environments to manage the increasing demands and workload volume. According to Microsoft’s recent survey, users report significant benefits from AI usage, including time-saving (90%), enhanced focus on priority tasks (85%), increased creativity (84%), and greater job satisfaction (83%). The industry has seen several initial successes in implementing generative AI tools to enhance customer service. But now, we see financial institutions gravitate towards tools designed to aid customer representatives but not accessible to clients directly. Here are the most sought-after use cases:



Generative AI is transforming enterprise search in financial services with three fundamental advantages:


1. Comprehensive data retrieval

It conducts thorough searches across diverse datasets, leading to more precise and comprehensive information retrieval and ensuring consideration of critical data.


2. Contextual understanding and relevance

Generative AI excels in grasping the context behind user queries and tailors the search results to specific user needs, enhancing the accuracy of the data provided.


3. Speed and efficiency

Generative AI significantly boosts search speed by streamlining the search process. This effectively enhances productivity by allowing faster access to necessary information and facilitating quicker decision-making.

Boosting Productivity


Generative AI significantly boosts productivity in financial services through:


1. Streamlining decision-making processes


Case studies demonstrate generative AI’s effectiveness in accelerating and enriching decision-making within financial operations, enabling quicker, data-driven choices that propel the business forward.


2. Automating compliance management


It offers robust support to financial institutions in effortlessly managing compliance, automating the understanding and implementation of complex regulations, thus significantly reducing the workload and risk of non-compliance.


3. Enhancing customer experience


Generative AI is crucial in refining client service by providing faster access to accurate information, improving response times and overall customer satisfaction.

These points underscore how generative AI improves operational efficiency and introduces a new paradigm in customer relations and regulatory compliance in the financial sector.

Implementation challenges and considerations

While adopting generative AI for enterprise search and productivity in financial services offers transformative benefits, businesses must first navigate several potential hurdles. Key among these challenges is ensuring data privacy and security measures, which are critical given the sensitive nature of financial data.


Studies show that a surprising 78% of AI users introduce their own AI tools to the workplace (BYOAI), a trend that’s even more prevalent among small and medium-sized enterprises (80%). Contrary to what one might expect, this isn’t limited to Gen Z but spans all age groups. Interestingly, despite its widespread use, 52% of individuals utilising AI at work hesitate to disclose their reliance on it for critical tasks.


So, executives must take the lead and formulate an adoption strategy to control the in-house use of GenAI tools. Citizens Financial Group, as mentioned above, created a steering committee tasked with directing AI initiatives and halting unauthorised development efforts. Businesses must prioritise the protection of customer and corporate information, adhering to stringent regulatory standards and employing advanced encryption, access controls, and continuous monitoring to mitigate risks.


Also, businesses must work on seamless integration with existing systems to maximise the utility and efficiency of generative AI technologies without disrupting established workflows. Financial institutions must carefully plan and execute the integration process, selecting AI solutions compatible with their current infrastructure and capable of synthesising diverse data sources to enhance enterprise search and productivity outcomes.


Choosing the right AI tools and technologies that align with your unique business needs, possibly through collaboration with AI experts, is essential. With the Generative AI (GenAI) sector quickly expanding and filled with emerging startups—Dealroom’s global data platform in April 2024 showcased a Generative AI ecosystem featuring over 1,200 companies—it’s crucial to select a vendor with a proven track record in Conversational and Generative AI—Prioritise secure, dependable, and customisable tools, capable of accommodating various data sources and formats. A prime example is Tovie AI’s Data Agent, offering an effortless way to develop a no-code AI chatbot tailored to any specific requirement.

The trajectory of generative AI in financial services will dramatically reshape the industry’s landscape in the coming years. Predictions signal a future where its evolving role will streamline enterprise search and operational productivity and pioneer novel ways to interact with financial data and client services. Emerging innovations, such as more nuanced natural language understanding and predictive analytics, are poised to unlock deeper insights from vast datasets, making financial advice and decision-making faster and more accurate. Additionally, generative AI is expected to revolutionise compliance management by automating real-time tracking and application of regulatory changes, significantly reducing risk and operational overhead. As these technologies become more integrated, their potential to transform not just individual tasks but entire operational paradigms suggests that the financial institutions investing in generative AI today will be tomorrow’s innovators and market leaders.


So, GenAI has proven its potential to redefine enterprise search and productivity in the financial sector. From enabling comprehensive data retrieval to dramatically increasing the speed and efficiency of information access, generative AI fosters innovation in financial services. Its ability to understand context, predict needs, and produce actionable insights enhances decision-making, automates compliance, and amplifies customer satisfaction. In today’s digital era, financial institutions must establish a secure and regulated framework for embracing AI technology.


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