Generative AI in Financial Services

Generative artificial intelligence (genAI)—a cutting-edge technology that enables tools (i.e., the chatbot ChatGPT) to generate content—is rapidly gaining popularity within the financial services, wealth management, and banking industries. As the demand for instant insights and time savings continues to grow, leading firms are recognizing the immense potential of generative AI for transforming their operations and decision-making processes.

By harnessing the power of this new technology, financial professionals can leverage advanced algorithms and deep learning capabilities to analyze vast amounts of financial data, organize unstructured data, identify patterns, and generate valuable insights in a fraction of the time it would ordinarily take. This not only enhances efficiency but also enables professionals to make more informed decisions based on accurate and up-to-date information.

From automating data analysis and forecasting to generating personalized investment recommendations, this iteration of AI is revolutionizing the way financial professionals work. With genAI, firms can not only save time but also improve the accuracy and reliability of their insights, ultimately leading to better outcomes for their clients.

Ultimately, the adoption of AI tools is not just a trend, but a strategic move that can drive innovation, operational efficiency, and success in the ever-evolving world of finance. Below, we answer the questions every financial professional has about this revolutionary technology—its pros, cons, and use cases.

Use Cases of Generative AI in Financial Services

Generative AI has emerged as a powerful addition to the existing set of AI-powered tools in the field of financial services. By leveraging natural language processing (NLP) algorithms, these machine learning models have numerous use cases, from providing instant insights to streamlining workflows, as well as boosting time savings for professionals.

Some of the key generative AI use cases in financial services include:

Financial Reporting: Generative AI can be used to automate the process of financial reporting. Through historical financial data analysis, genAI algorithms can generate accurate and comprehensive financial reports, saving time and dramatically reducing the chance of human error

Earnings Analysis: Training models on historical earnings reports allows generative AI algorithms to produce insights and predictions about future earnings. This can help financial professionals make informed investment decisions and identify potential opportunities in the market.

Market Research: GenAI can also be a valuable tool for conducting market research, as it can analyze large volumes of market data, predict market trends, analyze customer preferences, and conduct competitor analysis. When used proactively, financial professionals gain a competitive edge and make data-driven decisions.

Finance Planning: One of the most promising use cases for genAI is that it can aid in finance planning by analyzing financial data and generating accurate forecasts. Using historical financial data and market trends to train on, these algorithms can provide insights into future financial scenarios. This can assist financial professionals in developing effective financial strategies and optimizing resource allocation.

Risk Assessment and Management: Unbeknownst to most professionals, genAI can play a crucial role in risk management. A model’s training data can teach algorithms to generate risk models and identify potential risks, helping financial professionals in assessing and mitigating risks, improving decision-making, and ensuring the stability of operations.

Performance Management: By analyzing performance data of financial products or portfolios, generative AI algorithms can generate insights and recommendations for optimizing performance. This can assist financial professionals in monitoring and improving the performance of their investments.

Benefits of Generative AI in Financial Services

Generative AI, with its ability to generate new data that closely resembles existing data, offers several benefits to the financial services industry. Here are some key benefits and how they work:

Centralize Internal and External Research 

Today, professionals face siloed resources containing information that, while essential for compliance purposes, creates inefficiencies. The solution: technology that centralizes research across teams so as to improve synergy, efficiency, and decision-making. 

With genAI, you can spend less time searching for company and market insights across internal and external sources with the help of integrations, which connects research from multiple investment teams and locations onto a centralized platform. Platforms like AlphaSense leverage purpose-built genAI technology that generate relevant summarizations by securely integrating internal research perspectives.

Spend Less Time Searching for Key Topics or Deal Terms 

There’s no denying that establishing benchmarking terms and building out comps today take longer due to the fragmentation of historical deal data housed across CRMs and other content sources. That’s why growing numbers of investment teams are embracing genAI to take advantage of a single search that pulls from every internal and external resource. 

The advantages of technology range from instant content summarization, to intelligent search surfacing key topics and terms from historical deal content and side-by-side comparisons with current external market and company insights.

Quickly Search for Company and Market Insights 

The scenario of time lost due to difficulty chasing content hidden within historical meeting notes, internal research thesis, memos, etc. is all too common. With a platform that leverages genAI, you can spend less time searching for company and market insights across internal and external sources. Additionally, integrated content sets can prove to be beneficial as a single “source of truth,” along with summarizations produced by genAI that can quickly surface insights and jumpstart research on new companies or markets.

Consolidate Internal and External Deal Intelligence 

Often, inefficiencies in the due diligence process stem from challenges with leveraging past deal details siloed in CRMs, network drives, deal rooms, etc. Regardless of where this information is sourced or exists within your company’s intelligence base, this information silo impacts deal velocity.

With the help of genAI technology and integration capabilities, your team can connect multiple internal research sources within one, centralized resource. The result leads to improved discovery—with the help of genAI-sourced summaries on internal and external content—which consequently supports more efficient, consistent deal analysis and structuring.

Competitive, Peer Monitoring, and Benchmarking

Financial professionals understand the challenge of keeping up-to-date on competitors during earnings season. The task is tedious and time-consuming, yet crucial to maintaining a lead in your industry. In a perfect world, your team could reduce the amount of hours spent on taking notes distilling key insights from large sets of qualitative data, and ultimately save time in tracking, analyzing, and reporting on public company competitors.

AlphaSense’s genAI-powered Smart Summaries feature does just that and more. Leverage the ability to cross-check key takeaways from earnings calls, establish a base camp for your analysis, quickly access parts of a transcript, and spend less time on secondary or tertiary competitors.

Managing Your Portfolio

It’s true that the more information you have at your disposal, the better decisions you’ll make. There’s no limit to the amount of potential influences that sway a monumental deal or strategy,  from a company’s performance  to stocks that are secondary important. AlphaSene’s proprietary genAI technology builds a mosaic of crucial information central to your portfolio by quickly consuming earnings and recent calls on non-core companies, allowing you to get caught up on “new names” and review company results. 

Preparing for Earnings Season

To reiterate, there’s no such thing as too much competitive intelligence— meaning the more competitors or peers’ earnings calls you can review, the better. Without such access to these limited resources, you risk being potentially under-prepared for questions analysts might ask on their own earnings call.

With AlphaSense’s genAI technology, you can easily stay on top of more competitor earnings calls by quickly identifying the topics or content most relevant to your search.  Our Q&A summaries make it simple to quickly spot trends in what questions are being asked and how competitors are responding—eliminating the useless fluff simultaneously. 

Challenges and Risks of Generative AI in Financial Services

Large Energy Requirement

Generative AI in financial services often requires significant computational power and energy consumption. The complex algorithms and foundational models used in genAI can put a strain on the resources needed to train and deploy these systems, leading to increased costs and taxing of other internal resources. 

Poor Input, Poor Output

The quality of the data sets used in generative AI models directly impacts the quality of the responses and insights generated. In financial services institutions, where accurate and reliable data is crucial, poorly reported data can lead to inaccurate or unreliable outputs, resulting in significant miscommunications or falsified results. It is essential to ensure that the input data used in generative AI models is of high quality and is properly validated and vetted to mitigate this risk.

Cybersecurity Threat

Generative AI systems in financial services can be vulnerable to cybersecurity threats, as they rely on large amounts of data that could be susceptible to hackers and malicious actors. Breaches in the security of these systems can lead to unauthorized access to sensitive financial information, financial fraud, and other cybersecurity risks. Robust cybersecurity measures and constant monitoring are necessary to protect their integrity.

Governance and Regulatory Compliance

The use of generative AI solutions in financial services raises governance and regulatory compliance challenges. Institutions need to ensure that their actions comply with industry regulations and guidelines. This includes considerations such as transparency, explainability, and fairness in the decision-making processes of generative AI systems. Adhering to governance and regulatory requirements is crucial to maintain trust and mitigate potential legal and reputational risks.

Data Privacy & Security

Any genAI tool relies on vast amounts of data, including sensitive and personal information, which means ensuring data privacy and security is of utmost importance to protect the confidentiality and integrity of this information. Financial institutions must implement robust data protection measures, including encryption, access controls, and data anonymization techniques to safeguard the privacy of individuals and comply with protection regulations.

How Financial Services Teams Can Prepare for Generative AI

As the financial industry continues to evolve, the adoption of genAI is becoming increasingly important for staying competitive. Financial services teams can take several steps to prepare for the integration of this technology into their operations. 

Here are some key strategies to consider:

Identify and Train Talent for AI Adoption: Building a team of professionals who are well-versed in AI technologies is crucial. Invest in training programs and workshops to upskill your existing workforce and attract new talent. Consider fostering a culture of human-AI collaboration by establishing frameworks that promote effective teamwork between humans and AI systems.

 Building or Buying a GenAI Tool: 

Many organizations continue to face a similar challenge: their data is a mess. Even when a professional knows where to look for it, it takes 20 clicks in folders to access it, and then relying on CTRL+F to search a tidbit of information within a document. That’s where genAI comes in. 

Not only is it one of the most important pieces of technology to overcome this obstacle, but it’s led to executives across industries to decide whether they should build or buy their own genAI tool. The bottom line: building large language models (LLMs) takes significant time, resources, and capital in order to effectively acquire, clean, and curate large data sets. Just for model training, you need computational resources (i.e., hardware, software, cloud services), ramping personnel, maintenance and updates, the list goes on and on. 

That’s why professionals are trusting platforms like AlphaSense to deliver the research results they need while ensuring the privacy and security of their data.  

Research Case Studies to Prepare and Strategize: Study successful case studies of genAI implementation in the financial industry. Analyze how other organizations have leveraged this technology to gain insights and improve efficiency. Use these insights to develop a strategic plan for integrating a genAI tool into your own operations.

Work Closely with IT teams: Collaboration between financial services teams and IT departments is integral for successful AI adoption. IT teams can provide the necessary infrastructure, data management systems, and security protocols to support implementation. Regular communication and coordination between these teams will also ensure a smooth integration process.

Embrace Innovative Technology: Embracing genAI technology requires a mindset shift within financial services teams. Encourage a culture of innovation and experimentation, where employees are open to exploring new possibilities offered by AI. Foster an environment that encourages learning and adaptation to maximize the benefits of generative AI.

Pilot Programs: Before fully implementing these advancements across all operations, consider running pilot programs to test its effectiveness and identify any potential challenges. Start with small-scale projects and gradually expand as you gain confidence in the technology. Monitor the results closely and make necessary adjustments to optimize its performance.

A Reliable, Accurate GenAI Tool for Every Professional 

Financial firms and institutions stand in a unique position to take an early lead in the adoption of generative AI technology. This presents fresh and exhilarating prospects to actively influence the future of finance, fostering innovation and transformation.

However, it is crucial to recognize that we are currently deep in the hype cycle surrounding generative AI. Without understanding the limitations and potential consequences of using this technology, a company can quickly run their operations amuck if no training or vetting is put in place. Given this context, industry leaders must redirect their attention towards pinpointing the specific areas where this state-of-the-art technology can genuinely provide substantial commercial value to their businesses in the present. 

Those who adeptly navigate this pivotal decision-making process and align it with their strategic objectives will undoubtedly emerge as frontrunners. By doing so, they position themselves ahead of the curve, ready to capitalize on the true commercial potential of generative AI as the hype inevitably subsides and its real impact on the industry unfolds.

AlphaSense Enterprise Intelligence enables secure searches, summaries, and follow-up questions across your proprietary internal data and a vast repository of 300M+ premium external documents—all powered by our industry-leading AI for market intelligence.

Already, 1,300-plus AlphaSense customers have integrated their proprietary internal content alongside our premium external market intelligence and leverage our industry-leading search, summarization, and monitoring tools. They’re leveraging our best-in-class search technology that saves time by delivering and summarizing the most relevant results across their proprietary internal content and hundreds of millions of premium external documents.

And potentially most important, Enterprise Intelligence meets the highest industry standards—including SOC2, ISO270001 compliant, regular, accredited third-party penetration testing, FIPS 140-2 standard encryption on all content, and SAML 2.0 integration. Ultimately, not only does AlphaSense speed up your internal knowledge search but ensures privacy and security from external, malicious forces. 

Start your free 2-week trial today to explore the AlphaSense platform firsthand and see how it can raise your organizational IQ.

Tim Hafke
Tim Hafke
Content Marketing Specialist

Formerly a writer for publications and startups, Tim Hafke is a Content Marketing Specialist at AlphaSense. His prior experience includes developing content for healthcare companies serving marginalized communities.

Read all posts written by Tim Hafke