Trending December 2023 # Open Banking Apis In 2023: Definition, Benefits & Applications # Suggested January 2024 # Top 17 Popular

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Open APIs have enabled the creation of open banking. In May 2023, Open Banking Implementation Entity (OBIE) reported more than 1 billion successful API calls were made by account servicing payment service providers (ASPSP); this number was 410 million in May 2023.

Open banking has provided considerable benefits to its users.

40% indicate that open banking has improved their financial decision-making

31% indicate an increase in payment option

23% indicate better borrowing opportunities.

Different companies, like fintech, can use open banking APIs for different purposes, such as developing budgeting tools and providing cloud accounting.

In this article, we will explore open banking while explaining the benefits, opportunities, and applications of open banking APIs to inform finance sector professionals regarding the latest trends.

What is open banking?

Open banking APIs are open-source software and network protocol for integrating financial services and personal data across devices. The mission of open banking is to make it easier for people to access their money and other information (e.g. …) via a simple, secure, stable, and regulated digital marketplace (see Figure 1).

Figure 1: Open banking vs. traditional banking

Open banking allows financial institutions such as banks to open their APIs to third parties, like software companies and other businesses. As seen in Figure 2, thus third parties can use that data to create new services for their customers, such as:

Budget management tools

Bank accounts aggregation tools

Digital lending

In the UK, 64% of open banking services are used for data sharing, 30% for payment, and 6% for both.

Figure 2. Open Banking flow

Open banking regulatory landscape

Banking is a highly regulated industry, and open banking is no exception. Major open banking regulations and standards have been passed in different countries. The most notable ones are:

EU revised Payment Services Directive (PSD2)

UK’s Open Banking Standard

Security is a crucial part of banking as they store sensitive information. In a 2023 survey, 95% of responders indicated they had suffered an API security incident in the last 12 months. Moreover, 40% of API users have indicated that they have experienced API malfunctioning. Given the importance of security and the high level of competition in open banking, API providers should test their APIs rigorously to ensure a high level of security and functionality.

Chatbot Sentiment Analysis Benefits In 2023

Chatbots are used in several industries to provide customers with better service. The chatbot market is estimated to be more than $1 billion in 2025. Although chatbots are becoming increasingly available, customer satisfaction with customer service chatbots is around 30%. This is partially due to the use of rule-based chatbots, which are not intelligent enough to deal with problems in human interactions.

Developing reliable chatbots that can quickly solve users’ problems is crucial to ensuring customer satisfaction. Bots capable of sentiment analysis can help businesses better understand their customers’ needs and take necessary actions.

How sentiment analysis can be applied to chatbots?

Through AI-powered methods, chatbots can identify users’ sentiments from their unstructured input and categorize them as positive, negative, or neutral using a labeled dataset. Depending on the emotional tone depicted in the text of customers, chatbots can give responses or take necessary actions.

Figure 1. A simplified visualization of how AI-powered chatbots work What are the benefits of chatbot sentiment analysis? 1. Improve customer experience Figure 2. Example utterance sequences and their sentiment scores

Customers use more anger-related and less positive words when they are unhappy with your products, services, or chatbot. So, their conversations with chatbots carry important insights regarding their sentiment.

By implementing sentiment analysis methods, you can understand customers’ needs, complaints, and wishes and take quick action against them. For instance, if a customer is frustrated, sentiment analysis can help your chatbot detect poor experience quickly and transfer the customer to a live agent.

Using sentiment analysis, chatbots can provide a more personalized experience for your customers. You can understand how they feel and what they need and create a friendly atmosphere where they are willing to have more conversations.1

2. Gather input to improve recommendation systems

Recommendation systems help customers save time searching for what they want and need. However, their satisfaction decreases when the recommendations do not match their needs.

Thanks to chatbot sentiment analysis tools, you can improve recommendation systems in a way that they offer products or services based on consumers’ sentiments and interests. Customers can save time and energy, which increases their satisfaction.

As sentiment analysis methods can also identify customers’ behavioral patterns or purchasing habits, companies can provide wider choices, better recommendations, and personalized offers.2

3. Monitor your brand reputation

You can learn what your customers think about your products or services through chatbot services and get insights into what they think or feel about your brand. You can manage your brand reputation and develop new strategies using chatbot sentiment analysis methods.

For example, you can ask for feedback through chatbots and understand how customers evaluate your brand.

4. Develop new growth strategies Industries that can benefit from chatbot sentiment analysis

All customer-facing industries can benefit from integrating sentiment analysis methods into their chatbots. Feel free to check our articles on the use of chatbots in:

To learn more about companies offering sentiment analysis, check our data-driven list of sentiment analysis services.

Reinforcement Learning: Benefits & Applications In 2023

Machine learning algorithms are used in a wide range of applications, from image recognition to natural language processing (NLP) and predictive analytics. One major challenge in the field of machine learning is designing algorithms that can learn to make complex, long-term decisions in dynamic environments. This is particularly relevant in fields such as robotics and autonomous systems, where the ability to adapt to changing circumstances is crucial.

Reinforcement learning is a type of machine learning algorithm that focuses on training models to make decisions in an environment in order to maximize a reward. This is typically done through trial and error, as the algorithm receives feedback in the form of rewards or punishments for its actions.

In this article, we’ll explore what reinforcement learning is, how it works, its applications, and its challenges.

What is reinforcement learning (RL)?

Reward rules are determined in the reinforcement learning algorithms. The agent of the model tries to reach maximum rewards through its actions. The algorithm starts with trials and learns to make decisions by itself to gain maximum rewards.

Reinforcement learning models can gain experience and feedback (rewards) from their actions which help them to improve their results. This machine learning approach can be best explained with computer games.

What is the level of interest in reinforcement learning?

Reinforcement learning may be a key player in further development and the future of AI. So, the interest in reinforcement learning has been continuing for the last five years. The machine learning domain has been improving reinforcement learning models with new areas such as deep reinforcement learning, associative reinforcement learning, and inverse reinforcement learning. The interest in reinforcement learning is seen below from the chart.

Source: Google Trends

How does it work?

There are five key elements of reinforcement learning models:

Agent: The algorithm/function in the model that performs the requested task.

Environments: The world in which the agent carries out its actions. It uses the current states and actions of the agent as input, rewards, and the next states of the agents as output.

States: It refers to the situation of the agent in an environment. There are current and future/next states.

Actions: The moves are chosen and performed by the agent to gain rewards.

Rewards: Reward means desired behaviors that are expected from the agent. Rewards are also called feedback for the agent’s actions in a given state and are described as results, outputs, or prizes in the model.

Different algorithms and approaches are used in the reinforcement learning models. Some of them are listed below.

Markov Decision Processes (MDPs): It is a framework that is used to model decision-making processes. The decision maker, the states, actions, and rewards are the key elements of MDPs. MDPs are effective for formulating reinforcement learning problems.

SARSA (State-Action-Reward-State-Action): It is an algorithm to learn a Markov decision process policy. The agent in its current state selects and performs an action and gains a reward for its action. Then, the agent gets into a new state and selects a new action.

Q-learning: It is a reinforcement learning algorithm. It does not need a model to learn the value of the actions and there is no policy. It means that it is a self-directed model.

Deep Reinforcement Learning: Reinforcement learning models are used with artificial neural networks to solve high-dimensional and complex problems. Deep reinforcement learning algorithms can work with large datasets. DeepMind’s game, AlphaGo Zero is a popular example for deep reinforcement learning.

There is a simple flow for the agent–environment interaction in a Markov decision process below.

What are the applications of reinforcement learning?

A large amount of data is required for reinforcement learning models. That means it is not applied in the areas which have limited data, but it may be ideal for robotics and industrial automation and building computer games. Reinforcement learning algorithms have the ability to make sequential decisions and learn from their experience. That is their distinguishing feature from traditional machine learning models. Common areas where reinforcement learning is used are listed below:

Computer Games: Pac-Man is a well-known and simple example. Pac-Man’s (the agent of the model) goal is to eat the food in the grid (the environment of the model), but not get killed by the ghost. Pac-Man is rewarded when it eats food and loses the game when it is killed.

Industrial Automation and Robotics: Reinforcement learning helps industrial applications and robotics to gain the skills themselves for performing their tasks.

Traffic Control Systems: Reinforcement learning is used for real-time decision-making and optimization for traffic control activities. There are existing projects such as the project to support air traffic control systems.

Resources Management Systems: Reinforcement learning is used to distribute limited resources to the activities and to reach the goal of resource usage.

Advertising: Reinforcement learning supports businesses and marketers to create personalized content and recommendations.

Other: Reinforcement learning models are also used for other machine learning fields like text summarization, chatbots, self-driving cars, online stock trading, auctions, and bidding.

What are the challenges of reinforcement learning?

Reinforcement learning is not a new area in machine learning and progress is still continuing despite the challenges. Those challenges are summarized below:

Reinforcement learning needs large datasets to make better benchmarks and decisions.

When the model’s complexity increases, reinforcement learning algorithms need more data to improve their decisions. That means the environments of the model may become more difficult to create a reinforcement learning model.

The results of reinforcement learning models depend on the agent’s exploration of the environment and it brings limitations to the model. The agent takes action according to the environment and its current state. If the environment changes constantly, making a good decision could be difficult.

The design of the reward structure of the model is another challenge for reinforcement learning. The agent uses rewards and penalties to make a decision and perform its task. The way the agent is trained in the model is the key to the success.

For more on different types of machine learning approaches, feel free to read our other articles:

If you have questions about reinforcement learning, we would like to help:

This article was drafted by former AIMultiple industry analyst Ayşegül Takımoğlu.

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.

Parsing Xml With Sax Apis In Python

SAX is a standard interface for event-driven XML parsing. Parsing XML with SAX generally requires you to create your own ContentHandler by subclassing xml.sax.ContentHandler.

Your ContentHandler handles the particular tags and attributes of your flavor(s) of XML. A ContentHandler object provides methods to handle various parsing events. Its owning parser calls ContentHandler methods as it parses the XML file.

The methods startDocument and endDocument are called at the start and the end of the XML file. The method characters(text) is passed character data of the XML file via the parameter text.

The ContentHandler is called at the start and end of each element. If the parser is not in namespace mode, the methods startElement(tag, attributes) and endElement(tag) are called; otherwise, the corresponding methods startElementNS and endElementNS are called. Here, tag is the element tag, and attributes is an Attributes object.

Here are other important methods to understand before proceeding −

The make_parser Method

Following method creates a new parser object and returns it. The parser object created will be of the first parser type the system finds.

xml.sax.make_parser( [parser_list] )

Here is the detail of the parameters −

parser_list − The optional argument consisting of a list of parsers to use which must all implement the make_parser method.

The parse Method

Following method creates a SAX parser and uses it to parse a document.

xml.sax.parse( xmlfile, contenthandler[, errorhandler])

Here is the detail of the parameters −

xmlfile − This is the name of the XML file to read from.

contenthandler − This must be a ContentHandler object.

errorhandler − If specified, errorhandler must be a SAX ErrorHandler object.

The parseString Method

There is one more method to create a SAX parser and to parse the specified XML string.

xml.sax.parseString(xmlstring, contenthandler[, errorhandler])

Here is the detail of the parameters −

xmlstring − This is the name of the XML string to read from.

contenthandler − This must be a ContentHandler object.

errorhandler − If specified, errorhandler must be a SAX ErrorHandler object.

Example #!/usr/bin/python import xml.sax class MovieHandler( xml.sax.ContentHandler ): def __init__(self): self.CurrentData = "" chúng tôi = "" self.format = "" chúng tôi = "" self.rating = "" self.stars = "" self.description = "" # Call when an element starts def startElement(self, tag, attributes): self.CurrentData = tag if tag == "movie": print "*****Movie*****" title = attributes["title"] print "Title:", title # Call when an elements ends def endElement(self, tag): if self.CurrentData == "type": print "Type:", self.type elif self.CurrentData == "format": print "Format:", self.format elif self.CurrentData == "year": print "Year:", self.year elif self.CurrentData == "rating": print "Rating:", self.rating elif self.CurrentData == "stars": print "Stars:", self.stars elif self.CurrentData == "description": print "Description:", self.description self.CurrentData = "" # Call when a character is read def characters(self, content): if self.CurrentData == "type": self.type = content elif self.CurrentData == "format": self.format = content elif self.CurrentData == "year": self.year = content elif self.CurrentData == "rating": self.rating = content elif self.CurrentData == "stars": self.stars = content elif self.CurrentData == "description": self.description = content if ( __name__ == "__main__"): # create an XMLReader parser = xml.sax.make_parser() # turn off namepsaces parser.setFeature(xml.sax.handler.feature_namespaces, 0) # override the default ContextHandler Handler = MovieHandler() parser.setContentHandler( Handler ) parser.parse("movies.xml")

This would produce following result −

*****Movie***** Title: Enemy Behind Type: War, Thriller Format: DVD Year: 2003 Rating: PG Stars: 10 Description: Talk about a US-Japan war *****Movie***** Title: Transformers Type: Anime, Science Fiction Format: DVD Year: 1989 Rating: R Stars: 8 Description: A schientific fiction *****Movie***** Title: Trigun Type: Anime, Action Format: DVD Rating: PG Stars: 10 Description: Vash the Stampede! *****Movie***** Title: Ishtar Type: Comedy Format: VHS Rating: PG Stars: 2 Description: Viewable boredom

For a complete detail on SAX API documentation, please refer to standard Python SAX APIs.

General Ledger Software In 2023: Benefits & Key Features

Despite the significance of automation for businesses, the deployment of automated AP systems has been slow so far:

9% of AP departments consider themselves completely automated.

54% of AP departments throughout the world still rely on manual processes.

However, AP departments are now starting to invest heavily in automation:

In 2023, 41% of businesses expect to implement Accounts Payable automation.

5% of 2023 respondents expect full department automation within 6-12 months.

To help those investing in AP automation, this article demonstrates the benefits of using a general ledger software, as well as the tools and technologies available to help businesses manage their general ledger effectively.

What is a general ledger software?

A general ledger (GL) is a record of a company’s financial transactions. A general ledger software is a computerized accounting system that automates recording and tracking these transactions, including:

Assets

Liabilities

Equity

Expenses

Revenue

Transactions are recorded in general ledger in a chronological order, with each transaction being recorded in at least two accounts. For example, when a company buys goods on credit, the transaction will be recorded in the accounts receivable account and the inventory account.

General ledger is usually maintained by the company’s accounting department and is an important tool for financial reporting and analysis because it serves as the central repository for all of a company’s financial transactions. It includes all of the accounts that a company uses to record its financial activity.

A general ledger software can also:

Generate journal entries automatically and reduce the risk of errors and ensures that transactions are recorded accurately

Provide real-time financial reporting, allowing a company to track its financial performance on an ongoing basis.

Generate financial statements such as the balance sheet and income statement.

4 benefits of general ledger software 1. Increased efficiency

A general ledger can be complex to set up and maintain, especially for companies with a large number of financial accounts. This can be time-consuming and requires a high level of technical expertise.

A general ledger software is typically less complex than a manual general ledger because it automates

The generation of journal entries

The preparation of financial statements

The tracking of financial transactions.

In addition, if a general ledger is not properly integrated with other systems, it can lead to data inconsistencies and inefficiencies. A general ledger software can be integrated with other accounting systems, such as:

2. Increased accuracy

Accuracy of financial data is also improved with accounting software. General ledger’s primary function is to record the company’s financial activities and to generate financial and management reports like balance sheets and profit & loss accounts to assist stakeholders in making decisions.

A general ledger software automatically creates detailed account balances for financial reports and keeps financial data

Up to date

On-demand

Correct

so that stakeholders can reach efficient decisions.

Companies must ensure that sensitive financial data is secure and protected against unauthorized access. General ledger software has robust security measures in place to protect against unauthorized access to financial data. This can help to prevent financial fraud and data breaches or leaks.

4. Improved visibility 8 key features of general ledger software

Accounting systems are built around a general ledger. General ledger softwares are therefore important tools for financial reporting and analysis to ease the processes for business’ accounting departments.

Accounting firms look for a financial software that is dynamic, adaptive, and scalable, while also providing them with the flexibility, insights, and control to keep up with rapidly changing business requirements. Here we provide 8 key features of general ledger software:

Automatic generation of journal entries

GL software can automatically generate journal entries based on data entered into the system. General ledger software can be customized to automatically add transactions in more than one accounting ledger, which streamlines the process.

Custom segments

Custom general ledger segments including:

Profit centers

Funds

Programs

Product lines

can be defined with GL software. This can be particularly useful for companies with complex financial structures or operations in multiple locations.

Financial transparency

Using general ledger software, income and expenses can be tracked in real time against budgets. These budgets can be created within the system or imported as files, allowing organizations to closely monitor their financial performance and make informed business chúng tôi feature can be particularly useful for organizations looking to optimize their financial planning and management.

Financial reporting

An automated general ledger system can provide real-time financial reporting, allowing a company to track its financial performance regularly. It can also generate financial statements such as the balance sheet and income statement.

Support for multiple currencies and banking integration:

General ledger softwares can provide complex financial needs with multi-currency capabilities. These features can:

Convert

Revalue

Remeasure

Translate

several currencies according to local and worldwide accounting standards.

Multiple books

General ledger software can help companies navigate complex and regulated environments by streamlining compliance with local regulations and multiple reporting requirements.

This streamlined approach can help to reduce the time and effort required to manage multiple books and ensure compliance with various accounting standards.

Recommendations

Automated general ledger is a feature in AP software. Check out this list to learn more about accounting software and evaluate 483 services based on comprehensive, transparent and objective AIMultiple scores.

If you have further questions regarding AP automation you can reach us:

This article was drafted by former AIMultiple industry analyst Kübra İpek.

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.

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Chatgpt Education Use Cases, Benefits & Challenges In 2023

As it suddenly became popular on the internet and social media, teachers and lecturers across many educational levels are afraid of ChatGPT‘s possible negative impacts on academic integrity. They fear that the chatbot is going to increase plagiarism and spread academic dishonesty.

For academics in the Rochester University, ChatGPT and other similar AI tools are in the “yellow” category of higher education online educational tools, meaning students should be careful while using them.

OpenAI states that they are working on reducing such risks by developing a watermarking method to detect the outputs of the bot.

Whether this will be achieved successfully or not, it is a fact that ChatGPT is changing the traditional learning process and forcing everyone to rethink and remodel education besides other generative AI technologies. In this article, we gathered ChatGPT education use cases for teachers and students, and also explained its benefits and challenges to education.

Top 7 ChatGPT Education Use Cases For Teachers 1- Content creation

ChatGPT is a valuable tool to teach students effectively and engagingly. It can help teachers generate ideas for lesson plans, activities, and projects that align with specific learning objectives and curricular standards.

Also, teachers can use ChatGPT to create and curate educational materials, such as presentations, worksheets, quizzes, and other resources tailored to their students’ needs (Figure 1).

Figure 1. ChatGPT creates a quiz for a class

2- Grammar and writing check

Another use case of ChatGPT for teachers involves leveraging the AI’s natural language understanding capabilities to assist in evaluating and improving the quality of written work. This can be applied in various ways:

Proofreading and editing: Teachers can use ChatGPT to quickly review and correct spelling, grammar, punctuation, and syntax errors in their own written materials, such as lesson plans, handouts, or email communications.

Providing feedback to students: ChatGPT can assist teachers in reviewing student essays, reports, or other written assignments, identifying areas that need improvement and providing specific feedback on grammar, sentence structure, and word choice.

Teaching writing skills: Teachers can use ChatGPT to generate examples and explanations of grammar rules, punctuation usage, and other writing conventions, helping students improve their writing skills.

3- Grading

ChatGPT can assist teachers in reviewing and grading student essays by analyzing the content, structure, and coherence of the writing. The AI can provide feedback on grammar, spelling, punctuation, and syntax, as well as assess the quality of the argument or analysis presented. However, it is important to not rely merely on ChatGPT while grading. Rather, teachers can use ChatGPT for determining the rubric for grading.

4- Designing syllabus outline

Teachers can incorporate ChatGPT by leveraging the AI’s capabilities to help create, organize, and structure course content in a coherent and effective manner (Figure 2). ChatGPT can help in:

Preparing course objectives and goals

Topic generation

Lesson planning

Identifying and curating relevant resources and materials for the course

Figure 2. ChatGPT creates a course design on cellular biology

For Students 5- Help with homework

Answering questions: Students can ask ChatGPT questions about specific topics or concepts they are struggling with, and the AI can provide explanations, examples, or resources to help clarify their understanding (Figure 3).

Problem-solving: ChatGPT can increase the problem solving skills of students by guiding them through the steps of solving problems in subjects like mathematics, physics, or chemistry, helping them understand the underlying concepts and methods.

Concept reinforcement: ChatGPT can provide additional examples, analogies, and explanations to reinforce students’ understanding of complex concepts or topics they are studying.

Improving writing skills: Students can use ChatGPT to get suggestions for improving their essays, reports, or other written assignments, including feedback on grammar, sentence structure, word choice, etc. They can use the tool to write essays, craft the first draft of their written assignments, which can save time to improve the fine details and overall quality of the work.

Figure 3. ChatGPT explains some key concepts for a high school student

6- Research

ChatGPT can support and streamline the research process for various academic projects, assignments, or personal interests (Figure 4). It can facilitate many steps in a research process such as:

Topic selection

Background information about the topic

Identifying relevant resources

Organizing research

Citation assistance

Figure 4. ChatGPT provides creative ideas for a term project

7- Language learning

ChatGPT can be a valuable tool for language learning, offering translations, grammar explanations, vocabulary practice, and conversation simulations to help students practice and improve their language skills. Also, it can help students to schedule a program for improving their language skills (Figure 5).

Benefits of ChatGPT for Education Speed

Artificial intelligence tools like ChatGPT can process and generate information quickly, which can save time and increase efficiency for both teachers and students. It creates a valuable time for students to focus on other tasks by allowing the automation of instant feedback and quick access to information.

By assisting with tasks like

grading

lesson planning

content creation

ChatGPT can save time for teachers, allowing them to focus more on direct instruction and student support.

Availability

ChatGPT provides a readily available source of information and guidance, making it easier for students and teachers to access support when needed, regardless of time or location.

Personalized learning

ChatGPT can help create tailored learning experiences for students based on their individual needs, interests, and skill levels, allowing for more targeted instruction and improved learning outcomes.

Challenges of ChatGPT for Education

Although the chatbot has many capabilities, it also has some limitations and challenges. The video below shows how ChatGPT can fail in some educational tasks.

Here are some other potential challenges of ChatGPT for education:

Accuracy and reliability problems

ChatGPT may sometimes provide inaccurate or incomplete information, which could lead to misunderstandings or confusion for students and teachers.

Possible biases

AI technologies like ChatGPT can inherit biases from the data they are trained on, which may result in biased or unrepresentative content generation that could impact teaching and learning negatively.

Risk of decrease in original and critical thinking skills

The convenience and speed of ChatGPT might lead to an over-reliance on AI-generated content and reduce critical thinking, problem-solving, and creativity in the educational process.

If you have questions or need help in finding vendors, we can help:

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.

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