Data Science Salon
A comprehensive guide to financial AI
What is artificial intelligence
Modern Artificial Intelligence is an umbrella term that includes the Machine Learning techniques and tools that enable computers to produce desired outcomes based on large quantities of data. The “outcome” may take many forms – from image recognition to data exploration to producing entirely new content using Generative AI. In the process of producing the outcome, AI-based systems can (and usually need to) perform actions unseen before in computer systems, be it reasoning, making decisions, or solving problems unpredicted by the creator.
Currently, there are two main branches of AI – traditional Artificial Intelligence and Generative AI. The former uses the more traditional approach seen in image recognition, natural language processing, or automated data analysis. In the latter, the key aspect is the way the user interacts with the AI system – via prompt. The prompt is the short, usually written (yet may be enriched with multimodal data) instruction that tells the AI what to do – for example, it may instruct a Large Language Model to write a poem, an email, or a blog post.
What makes the difference between traditional AI and Generative AI is the flexibility of the system. In the traditional approach, the AI solution is performing only a task that the creator has designed it to. Generative AI can be easily applied to multiple types of tasks. Yet the price of this flexibility is less predictability.
Benefits of AI in finance industry
Data management
First and foremost, Artificial Intelligence in finance can be used to manage data and harvest insights from it. When it comes to gnawing through vast amounts of data, AI is the perfect pick – from clustering algorithms that discover new target groups or opportunities, to automated systems that dig through unstructured data like company emails and communication history with clients.


Opportunity discovery
With the data ready to be explored and insights ready to be harvested, companies in the financial sector may gain an even bigger lever from the data they own. Also, using AI in automating processes is a gateway to making financial services more accessible to a larger group of clients and customers. For example, automated AI investing recommendations may make investing advisory more accessible. The same goes for chatbot interfaces for customer support or handling complaints.
Opportunity discovery
With the data ready to be explored and insights ready to be harvested, companies in the financial sector may gain an even bigger lever from the data they own. Also, using AI in automating processes is a gateway to making financial services more accessible to a larger group of clients and customers. For example, automated AI investing recommendations may make investing advisory more accessible. The same goes for chatbot interfaces for customer support or handling complaints.

Fostering innovation
Artificial intelligence is a game-changer for financial services, as it is for basically every other sector. The potential of AI to transform day-to-day business is fertile ground for new business models and the disruption of existing ones.

Customer relationship management boost
Last but not least, the conversational nature of LLMs brings a plethora of possibilities in customer relationship management and building a better user experience. With years of semi-automated systems people are used to interacting with, implementing ever-vigilant, and happy-to-help AI chatbots that will provide the user with responses for one’s problems delivers a real one-on-one experience on a mass scale.
Customer relationship management boost
Last but not least, the conversational nature of LLMs brings a plethora of possibilities in customer relationship management and building a better user experience. With years of semi-automated systems people are used to interacting with, implementing ever-vigilant, and happy-to-help AI chatbots that will provide the user with responses for one’s problems delivers a real one-on-one experience on a mass scale.

Challenges of AI in financial services
AI applications in the finance sector are not only a great opportunity for the whole sector. It is also a great challenge in many fields, with compliance and cybersecurity risks being only the tip of the iceberg.
How to use AI in finance companies
AI for Financial Analysis
With its powerful data-gnawing capabilities, the most obvious use case is financial analysis. The process of analysis is basically about gaining insight into the extremely complicated system of connections and interactions between a company’s actions, financial effects, and overall performance.
With AI support, a company can boost the speed of the analysis process as well as its accuracy, due to the greater capability of machines to burrow through massive amounts of data.


Artificial Intelligence – Lending Perspective
AI in loan companies can play a pivotal role when used to analyze the risks and opportunities regarding a particular loan. The AI-based system can suggest the optimal conditions on which a loan may be given, the best interest rate regarding the applicant, or whether the loan should be given to a particular institution.
The challenge regarding AI in lending companies is the fairness and compliance of the solutions. The AI finance tool that makes the decision whether one can or cannot get a loan they need cannot be a black box that delivers judgment out of nothing.
Artificial Intelligence – Lending Perspective
AI in loan companies can play a pivotal role when used to analyze the risks and opportunities regarding a particular loan. The AI-based system can suggest the optimal conditions on which a loan may be given, the best interest rate regarding the applicant, or whether the loan should be given to a particular institution.
The challenge regarding AI in lending companies is the fairness and compliance of the solutions. The AI finance tool that makes the decision whether one can or cannot get a loan they need cannot be a black box that delivers judgment out of nothing.

AI in finance and accounting
Accounting and bookkeeping combine the contradictory needs to run repetitive and schematic errands with the possibility to encounter extremely complicated challenges. For the former, AI can be a perfect solution. For the latter, the time saved on the first group if handled by AI can be a game-changer.
AI in insurance companies
Insurance is about massive predictions, with companies struggling to find a balance between the attractiveness of the offer, and maximizing the income. With multiple use cases and ways to follow, the companies need to ensure the compliance and security of their solution.
AI in insurance companies
Insurance is about massive predictions, with companies struggling to find a balance between the attractiveness of the offer, and maximizing the income. With multiple use cases and ways to follow, the companies need to ensure the compliance and security of their solution.
Fraud detection
The Artificial Intelligence has a far superior ability to recognize patterns in data than a human has. Also, the monitoring can be done in real-time, so the time between the intrusion and detection can be significantly reduced.
Using LLMs in Financial Companies
Large Language Models (LLMs) have disrupted multiple areas of modern business, from marketing to customer service to data analysis. There are multiple use cases for LLMs in companies, ranging from employing chatbots as the first line of customer service to utilizing internal experts who analyze data and provide necessary information to users.
Using LLMs in Financial Companies
Large Language Models (LLMs) have disrupted multiple areas of modern business, from marketing to customer service to data analysis. There are multiple use cases for LLMs in companies, ranging from employing chatbots as the first line of customer service to utilizing internal experts who analyze data and provide necessary information to users.
Trading Support
Another interesting use case for Artificial Intelligence in financial services comes from trading practices, where AI can be applied to multiple scenarios. This ranges from providing traders with desired sets of information to monitoring the internet for news that may impact their work. For example, if a trader is focused on resources, spotting early signs of political troubles in countries that provide oil for the market can be a game-changer.
Moreover, there is high potential for fully automated trading, with artificial intelligence making decisions. Given AI’s speed and accuracy in data analysis, this use case may be of extreme importance, particularly in high-frequency trading scenarios.

AI in Fintech
Artificial intelligence in fintech is a whole different story. Fintech companies are exploring ways leverage AI to disrupt and improve financial processes and build new finance AI tools. AI in fintech companies finds a multitude of use cases, depending on the process, market, or product the company is willing to reshape with its offering.
AI in Fintech
Artificial intelligence in fintech is a whole different story. Fintech companies are exploring ways leverage AI to disrupt and improve financial processes and build new finance AI tools. AI in fintech companies finds a multitude of use cases, depending on the process, market, or product the company is willing to reshape with its offering.

Summary
Artificial Intelligence is on a path to disrupt basically any industry and niche. When it comes to AI tools for the finance sector, the key advantage lies in the data richness of the companies. With data being the nourishment of AI models, this sector is fertile ground for implementing Machine Learning-based automations.
More about the matter can be explored during the upcoming New York conference aimed at financial sector professionals and experts. All the talks enriching the text above were initially delivered during previous editions.
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