Artificial Intelligence (AI), Machine Learning (ML), and other similar technology terms are easily tossed around and often used interchangeably. Despite their interconnected nature and the fact that they often complement each other — exhibiting some shared characteristics — they possess distinct features and specific domains of application that demand attention and awareness.

Technological advancements are dynamically reshaping the financial sector. We must identify these emerging trends and critically assess their potential and ethical considerations. Let's dig deeper into the critical technologies poised to reshape financial services. 

1. Artificial Intelligence (AI)
   AI involves replicating human-like intelligence in machines. These programmed systems emulate human thinking and actions, demonstrating the capability to perform tasks that typically require human intelligence.
   1. Application: AI is used for fraud detection, customer service through chatbots, algorithmic trading and managing customer data
   2. Potential value: Increases efficiency, reduces operational costs and enhances customer experience
   3. Moral and ethical considerations: Concerns include the potential for job displacement, data privacy issues and the imperative to ensure AI decisions remain free from discriminatory biases against specific groups
2. Machine Learning (ML)
   ML is a component of AI that enables systems to autonomously learn and enhance their performance through experience without explicit programming.
   1. Application: ML is used for predictive analytics, risk management, customer segmentation and personalized marketing
   2. Potential value: Improves prediction accuracy, enhances risk assessment and enables targeted customer service
   3. Moral and ethical considerations: The risks involve a lack of algorithmic transparency, potential data misuse and the challenge of addressing the 'black box' problem, where decisions made by ML models are not easily interpretable
3. Deep Learning (DL) 
   DL is a subset of ML based on artificial neural networks with representation learning. It empowers machines to tackle intricate problems, leveraging notably diverse, unstructured and interconnected datasets.
   1. Application: Used in complex tasks like speech recognition, image recognition and natural language processing (NLP), which are crucial in customer service automation and sentiment analysis
   2. Potential value: Enhances the ability to uncover hidden patterns in data and significantly improves the accuracy of predictive models
   3. Moral and ethical considerations: Risks include high resource consumption, potential for overfitting and the propagation of bias in the training data
4. Natural Language Processing (NLP) 
   NLP is an AI subfield that enables machines to read, comprehend and extract meaning from human languages.
   1. Application: Automated chatbots for streamlining customer service, sentiment analysis to gauge market sentiment, detecting fraud through textual data analysis and extracting information from financial documents
   2. Potential value: Improves customer interaction, aids decision-making by analyzing market sentiment and automates routine tasks
   3. Moral and ethical considerations: Concerns include the potential for misinterpretation of nuances in language, privacy issues, and the challenge of ensuring non-biased interaction
5. Robotic Process Automation (RPA) 
   RPA involves using software robots or 'bots' to automate highly repetitive and routine tasks previously performed by humans.
   1. Application: Used for automating back-office tasks, like data entry, account reconciliation and processing transactions
   2. Potential value: Increases efficiency, reduces errors and frees employees to focus on more strategic tasks
   3. Moral and ethical considerations: Includes job displacement concerns and ensuring the ethical use of bots, especially regarding data privacy and security

AI, ML, and other related technologies are buzzwords and potent forces that are reshaping the financial industry. Therefore, understanding the distinct characteristics of these cutting-edge technologies is not just about intellectual curiosity; it's about harnessing their unique strengths to solve real-world problems. As we progress in this age of intelligent machines, we must strive for a harmonious blend of innovation, customer well-being and social responsibility.