AI Researcher

Security has been an issue for growing companies and professionals in finance for a long time now. And as the Internet continues to dominate the world, cyber security has become a more dominant issue for companies, especially financial firms, to deal with. In the context of financial security, generative AI has begun to be used for cyber security purposes, particularly through models such as generative adversarial networks. In the context of financial security, the application of this technology has transcended the conventional paradigms, introducing new methodologies to ensure integrity and safety. With the emergence of generative AI, there are various applications, techniques, and challenges associated with using AI for financial security.

Utilizing generative models, financial institutions are now able to simulate fraudulent activities by creating synthetic data that mirrors real-world financial transactions. This, in combination with machine learning techniques such as deep learning and random forest algorithms, provides a multidimensional approach to fraud detection. The combination between generative AI and traditional machine learning augments real-time identification of fraudulent patterns, ensuring proactive measures.

2. Risk Management and Vulnerability Assessment

Hagop Alajajian

How AI changes the game in ensuring financial and cyber security

A New Frontier in Financial Security Using AI

AI is a rapidly evolving field that's transforming a multitude of industries, including the investing realm. One key aspect of AI development is the concept of fine-tuning, a technique that allows models to learn from small, specialized datasets by building on the knowledge acquired from larger, general-purpose ones. In other words, fine-tuning allows a pre-trained AI model to learn and adapt to a more specialized task, even when the available data for the specific task is limited. In the realm of investing, fine-tuning has the potential to revolutionize traditional approaches, providing finance professionals with advanced tools to assess risk, identify opportunities, and enhance decision-making. Essentially, fine-tuning helps produce more specific and desired outputs when using generative AI.  


Some models used for fine tuning are davinci-003 and davinci. Text-davinci-003, a modern and proficient model, is specially created for tasks that require adherence to instructions. Its design empowers it to provide concise and precise responses, even when no example prompts are presented (known as zero-shot scenarios). In contrast, davinci is highly effective when fine-tuned for a specific task, given the availability of several hundred training instances. Among other models, these can be used to help determine the most suitable model for investment-related tasks. Fine tuning these models further enhances their performance and tailors them to the specific needs of investment firms.


Fine-tuning, at its core, is a transfer learning technique. Click 

 to use generative AI to get a recommendation if fine-tuning is right for your task. Fine-tuning involves taking a pre-trained AI model, usually developed and trained on a large and diverse dataset, and adapting or 'fine-tuning' it for a specific task. This is achieved by continuing the training process on a smaller, task-specific dataset. The concept significantly reduces the need for extensive computational resources and time – two vital assets that are traditionally expended in the initial training process of an AI model. The technique leverages the general knowledge the model has acquired to rapidly learn the specificities of the new task. Moreover, fine-tuning allows the application of AI in scenarios with limited data availability, which is a common occurrence in specialized fields like investment news.

How to apply fine-tuning and generative AI to investment practices

A Comprehensive Insight into Fine-Tuning for Finance

Advancements in Artificial Intelligence (AI) have transcended the boundaries of science fiction, opening an array of possibilities for industries worldwide and staking its claim in the real world. Its most potent transformational impact is being felt in the economic sphere, reshaping both micro and macroeconomic dimensions. In particular, it's reshaping the world of economics, causing ripples across both small and large-scale financial landscapes. One of the most potent of these disruptions? A revolutionary approach to spend analysis in corporate financial management.

At the heart of changes to how companies deal with financial management is financial planning tools, armed with the capability to predict future expenditure trends. These powerful tools provide an innovative way of crafting budgets that are not only achievable but align seamlessly with a company's financial goals.

If you peek behind the scenes at any firm, you'll find a treasure trove of financial data accumulated over years. This vast accumulation of data, ranging from single-entry and double-entry bookkeeping to income statements and asset tracking, make it an ideal candidate for analysis via AI technologies. With generative AI—a vibrant subset of AI—this wealth of data can be mined for nuggets of insight to enhance how firms manage expenses and assets.

Jamie Anderson, the visionary Chief Revenue Officer at US-based global spend optimization firm Emburse, encapsulated this potential when he told PYMNTS, AI and machine learning [ML] are the next frontier of [spend management] — whether it’s about helping companies build policies, or just getting the insight to make predictions on future spend or savings.” Anderson’s words are a sentiment shared by many companies, who are excited to embrace these emerging technologies to boost their efficiency and strategic reach.

And the benefits of generative AI extends beyond mere data processing. It can intelligently analyze, identify, and track expenses and spending patterns of individuals or entities. Consider this: AI can dive into every line item, match travel expenses with corresponding sales opportunities in a company, and process all this information to provide insightful metrics like the average cost of customer acquisition. It's a level of detail and correlation that's leaps and bounds far ahead of traditional financial management methods.

Reimagining Spend Management with AI

The integration of artificial intelligence (AI) in various business sectors has been a game-changer, with AI-based applications significantly transforming operations in today's workplaces. Generative AI is one such AI domain that holds immense potential in financial report creation and business communication. Generative AI can generate coherent, natural language text, providing innovative ways to improve workplace efficiency and communication effectiveness. Generative AI can be used in the workplace for creating financial reports and communicating with brokers, bankers, accountants, and people unfamiliar with a certain topic. Financial reports are important for investors, shareholders, creditors, execs, regulatory institutions (like the IRS), industry consumers, unions and employees; therefore, finding an efficient way to communicate financial information with all these parties is increasingly getting more crucial.

Utilizing Generative AI in Financial Report Writing

Financial reporting refers to the communication of financial information about a business entity to its stakeholders such as investors, creditors, and regulators. This process typically involves producing various documents and reports that present financial data, allowing stakeholders to understand the company's financial performance and make informed decisions. The primary documents in financial reporting include the balance sheet, income statement, cash flow statement, and statement of equity, which provide a detailed view of the company’s assets, liabilities, revenues, expenses, and cash flow. It is vital to create financial reports for one’s company because it can help others gain a meaningful understanding of the company’s financial performance.

In the complex world of business finance, generating financial reports is an integral but time-consuming process. Generative AI can be employed to automate the creation of key financial documents:

Apply AI in various ways to optimize the communication of finance and other business operations

Revolutionizing Finance Reporting and Communication with Generative AI

Market research plays a critical role in guiding business decisions and understanding consumer behavior. Market research—which is the action of gathering information about an industry and its consumers’ needs to answer questions about demand, market size, economic indicators, location, market saturation, and pricing helps businesses improve their financial strategies and decisions. With the rapid advancement of generative AI, there is increasing interest in exploring its potential applications in market research. I aim to provide a comprehensive analysis of generative AI's capabilities and challenges within the context of market research. By examining the economic potential, specific use cases, and associated challenges, we can clearly see the benefits of incorporating generative AI into market research practices. By leveraging generative AI, market researchers can benefit from its ability to process vast amounts of data, uncover hidden patterns, and provide valuable insights for decision-making.

Generative AI Applications in Market Research

Firstly, in terms of predictive modeling, generative AI can utilize historical data to forecast future outcomes, enabling market researchers to gain deeper insights into customer demand, competition, and product performance. Predictive modeling empowers researchers to make more informed decisions regarding buying and selling strategies. Also, by identifying patterns in consumer behavior, generative AI helps uncover hidden connections and reveals valuable insights. This application allows researchers to explore consumer attitudes, preferences, and behaviors in greater depth, leading to unique discoveries that may otherwise remain hidden. Furthermore, real-time analysis by Generative AI can process live data sources, such as social media or website traffic, to provide real-time insights. These insights are particularly relevant for topics that require up-to-date information, allowing researchers to understand consumer behavior, product performance, and competition in a timely manner.

Moreover, AI models may be able to conduct much of the operational side of market research like survey development and data analysis, which allows researchers to allocate their time to comprehending what the data collection and analysis means for their company. By using AI for market research, people conducting it can spend more time in the process of reevaluating the strategies for their businesses and improving whatever was created by the AI model to produce better and more robust outputs.  Generative AI can also be used to be able to better improve in the process of proposal creation, learning plan developments, and executive summary developments.

How to help your business with market research by using AI

AI for Market Research

Sentiment analysis has numerous applications across different domains. One very important transformation being made for how sentiment analysis is being conducted is through generative AI. It has become apparent that generative AI has become an extremely useful tool in many industries, and today it can be applied to the field of finance by interweaving sentiment analysis, a very powerful tool to understand emotions, trends, underlying opinions, etc. Specifically, business leaders are using to good advantage of generative AI to provide sentiments on various aspects of business, such as insight into public opinion and customer satisfaction, identifying trends in the market, among many other trends. In finance, sentiment analysis provides significant information to companies, and generative AI is making the process of conducting it much easier.

Sentiment analysis, also known as opinion mining, is a technique used to determine the sentiment or emotion expressed in a given piece of text, such as a sentence, paragraph, or entire document. It involves the use of natural language processing (NLP) and machine learning algorithms to analyze and classify the subjective information contained in the text.

Apply new techniques and tools to improve a team's investments and finances 

Using Generative AI to Produce Sentiment Analysis for Leaders in Finance

The era in which we live is experiencing rapid globalization and expanding corporations, the challenges faced by senior management staff in large organizations are becoming increasingly complex. As teams grow, maintaining standardization, implementing effective education initiatives, and navigating transitions to new technologies become daunting tasks. However, the field of generative AI provides a powerful solution to address these challenges. Finance is the field of managing money and how individuals, companies, and government groups intake and spend money. Just by definition, it is apparent how much interactions and information goes into the field of finance. As the world’s economy grows, the amount of data storing and interactions between companies and people will only rise exponentially as well. Therefore, it is important to install some sort of technology to mitigate the troubles of an expanding economy and managing teams, and this revolution has come to fruition with generative AI techniques.

Creating a cheaper, more efficient option to answering every employee’s questions is a way to significantly assist finance professionals, since AI tools can provide quick ways to do a task for the organization. By leveraging generative AI, very large corporations can efficiently manage their teams by providing a cost-effective and streamlined means of summarizing complex concepts for their employees. This paper explores how generative AI can be harnessed to distill large amounts of information for finance professionals, focusing on maximizing understanding, accuracy, and usefulness.

As corporations expand and teams multiply, the need to summarize information effectively becomes crucial. This is particularly true for very large corporations with hundreds of locations and thousands of employees, such as international financial firms. The process of educating employees on specific topics becomes increasingly challenging as the same questions are repeated, requiring senior experts to invest significant time and effort in providing detailed answers. The same is true for employers who have decided to reshape their company by adopting new models in the workplace. For instance, if you have a team of hundreds of developers, it is quite difficult to transition from object-oriented coding (java) to reference coding (python). This situation not only hampers the progress of critical projects but also limits the experts' ability to provide comprehensive responses. Consequently, employees are often directed to reference extensive tutorial guides, resulting in a lack of personalized and efficient information delivery.

How company leaders can use generative AI to spread important information amongst team members

Posts by Hagop Alajajian