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Using Python for Accounting And Finance Applications
The global FinTech market was valued at $127.66 billion and is expected to reach $309.98 billion at a CAGR of 24.8%, a report by PRNewsWire suggests. The staggering growth is the result of digital payments and transactions. From investors and traders to personal loan customers, everyone is hooked to FinTech.
If you are building FinTech products to cater to this multiplying need, you are on the right track.
However, what you need is technology that can help you realize that dream. FinTech involves working with data, transaction management, scalability, APIs, and much more. Python development services are the answer to all these requirements.
Some of the already popular Python-based solutions in FinTech are Stripe, Robinhood, Zapa, Square, Paypal, amongst many others.
Python’s development is highly robust for Finance and FinTech applications. Python is now the third most popular programming language behind C and Java. It is used in Fintech because it can work efficiently with data.
In this article, we will discuss the following things:
- Why Python software development is gaining popularity.
- How is the world using Python in Finance?
- The best Python libraries for FinTech development.
- Conclusion: Why Python is best for FinTech.
After reading the article, you’ll be able to better understand why Python knowledge is so superior to other programming languages in today’s world. You’ll also learn the different Python features that make it a unique programming language for FinTech solutions.
#python #fintech
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Buddha Community
1619518440
top 30 Python Tips and Tricks for Beginners
Welcome to my Blog , In this article, you are going to learn the top 10 python tips and tricks.
1) swap two numbers.
2) Reversing a string in Python.
3) Create a single string from all the elements in list.
4) Chaining Of Comparison Operators.
5) Print The File Path Of Imported Modules.
6) Return Multiple Values From Functions.
7) Find The Most Frequent Value In A List.
8) Check The Memory Usage Of An Object.
…
#python #python hacks tricks #python learning tips #python programming tricks #python tips #python tips and tricks #python tips and tricks advanced #python tips and tricks for beginners #python tips tricks and techniques #python tutorial #tips and tricks in python #tips to learn python #top 30 python tips and tricks for beginners
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Lambda, Map, Filter functions in python
Welcome to my Blog, In this article, we will learn python lambda function, Map function, and filter function.
Lambda function in python: Lambda is a one line anonymous function and lambda takes any number of arguments but can only have one expression and python lambda syntax is
Syntax: x = lambda arguments : expression
Now i will show you some python lambda function examples:
#python #anonymous function python #filter function in python #lambda #lambda python 3 #map python #python filter #python filter lambda #python lambda #python lambda examples #python map
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Why use Python for Software Development
No programming language is pretty much as diverse as Python. It enables building cutting edge applications effortlessly. Developers are as yet investigating the full capability of end-to-end Python development services in various areas.
By areas, we mean FinTech, HealthTech, InsureTech, Cybersecurity, and that's just the beginning. These are New Economy areas, and Python has the ability to serve every one of them. The vast majority of them require massive computational abilities. Python's code is dynamic and powerful - equipped for taking care of the heavy traffic and substantial algorithmic capacities.
Programming advancement is multidimensional today. Endeavor programming requires an intelligent application with AI and ML capacities. Shopper based applications require information examination to convey a superior client experience. Netflix, Trello, and Amazon are genuine instances of such applications. Python assists with building them effortlessly.
5 Reasons to Utilize Python for Programming Web Apps
Python can do such numerous things that developers can't discover enough reasons to admire it. Python application development isn't restricted to web and enterprise applications. It is exceptionally adaptable and superb for a wide range of uses.
Robust frameworks
Python is known for its tools and frameworks. There's a structure for everything. Django is helpful for building web applications, venture applications, logical applications, and mathematical processing. Flask is another web improvement framework with no conditions.
Web2Py, CherryPy, and Falcon offer incredible capabilities to customize Python development services. A large portion of them are open-source frameworks that allow quick turn of events.
Simple to read and compose
Python has an improved sentence structure - one that is like the English language. New engineers for Python can undoubtedly understand where they stand in the development process. The simplicity of composing allows quick application building.
The motivation behind building Python, as said by its maker Guido Van Rossum, was to empower even beginner engineers to comprehend the programming language. The simple coding likewise permits developers to roll out speedy improvements without getting confused by pointless subtleties.
Utilized by the best
Alright - Python isn't simply one more programming language. It should have something, which is the reason the business giants use it. Furthermore, that too for different purposes. Developers at Google use Python to assemble framework organization systems, parallel information pusher, code audit, testing and QA, and substantially more. Netflix utilizes Python web development services for its recommendation algorithm and media player.
Massive community support
Python has a steadily developing community that offers enormous help. From amateurs to specialists, there's everybody. There are a lot of instructional exercises, documentation, and guides accessible for Python web development solutions.
Today, numerous universities start with Python, adding to the quantity of individuals in the community. Frequently, Python designers team up on various tasks and help each other with algorithmic, utilitarian, and application critical thinking.
Progressive applications
Python is the greatest supporter of data science, Machine Learning, and Artificial Intelligence at any enterprise software development company. Its utilization cases in cutting edge applications are the most compelling motivation for its prosperity. Python is the second most well known tool after R for data analytics.
The simplicity of getting sorted out, overseeing, and visualizing information through unique libraries makes it ideal for data based applications. TensorFlow for neural networks and OpenCV for computer vision are two of Python's most well known use cases for Machine learning applications.
Summary
Thinking about the advances in programming and innovation, Python is a YES for an assorted scope of utilizations. Game development, web application development services, GUI advancement, ML and AI improvement, Enterprise and customer applications - every one of them uses Python to its full potential.
The disadvantages of Python web improvement arrangements are regularly disregarded by developers and organizations because of the advantages it gives. They focus on quality over speed and performance over blunders. That is the reason it's a good idea to utilize Python for building the applications of the future.
#python development services #python development company #python app development #python development #python in web development #python software development
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How To Compare Tesla and Ford Company By Using Magic Methods in Python
Magic Methods are the special methods which gives us the ability to access built in syntactical features such as ‘<’, ‘>’, ‘==’, ‘+’ etc…
You must have worked with such methods without knowing them to be as magic methods. Magic methods can be identified with their names which start with __ and ends with __ like init, call, str etc. These methods are also called Dunder Methods, because of their name starting and ending with Double Underscore (Dunder).
Now there are a number of such special methods, which you might have come across too, in Python. We will just be taking an example of a few of them to understand how they work and how we can use them.
1. init
class AnyClass:
def __init__():
print("Init called on its own")
obj = AnyClass()
The first example is _init, _and as the name suggests, it is used for initializing objects. Init method is called on its own, ie. whenever an object is created for the class, the init method is called on its own.
The output of the above code will be given below. Note how we did not call the init method and it got invoked as we created an object for class AnyClass.
Init called on its own
2. add
Let’s move to some other example, add gives us the ability to access the built in syntax feature of the character +. Let’s see how,
class AnyClass:
def __init__(self, var):
self.some_var = var
def __add__(self, other_obj):
print("Calling the add method")
return self.some_var + other_obj.some_var
obj1 = AnyClass(5)
obj2 = AnyClass(6)
obj1 + obj2
#python3 #python #python-programming #python-web-development #python-tutorials #python-top-story #python-tips #learn-python
1622207842
Top 7 RPA Finance and Accounting Use Cases
The financial services industry is complex. There are massive numbers and tons of data. Accuracy and error-free recording and management are essential. A single mistake in accounting can cost you millions of dollars. Therefore, the need for digital technologies to reduce human-errors, achieve efficiency, and deliver accurate results becomes imminent.
RPA in finance and accounting is now showing promising results. Implementing RPA reduces errors by almost 50% and increases the accuracy by 75%. RPA use cases in finance are going beyond traditional data entry tasks — firms use it to onboard customers, prepare financial statements, and much more.
This article will highlight the top 7 RPA use cases in finance and accounting. RPA services are the first technology that drives digital transformation in banks and financial institutions. Therefore, understanding the most significant use cases of RPA implementation in finance and accounting is necessary to achieve the best organizational results. But first,
Checkout 11 Use Cases and Benefits for Robotic Process Automation
What are the Benefits of RPA in Finance and Accounting?
End-to-end automation enables financial companies to automate processes without any human intervention. RPA in financial services allows accounting staff to focus on gathering insights rather than preparing documents.
RPA tools like Automation Anywhere, BluePrism, and UiPath offer separate accounting and financial bots for multiple activities. Before we look at their use cases, here are the few benefits of RPA in accounting:
- Cut down costs
Financial companies can cut down costs by investing once in RPA bots. The long-term investment is nothing compared to the cost of hiring accounting professionals for minor tasks. - Higher accuracy
Can’t find a mistake in your excel sheet? RPA solutions increase the accuracy by 50 to 70 percent and eliminate all the errors from your financial recordings. - Increase in efficiency
RPA software installs bots that can perform operations much faster than human personnel. They are known to reduce the process completion time by almost 50%. - Automate data
You can automate unstructured data and invoices from emails, pdf files, and more using Robotic Process Automation (RPA). Data extraction can help you with that. - Assist employees
When RPA technology takes care of mundane financial processes like entering customer details in a form, employees can focus on high-value tasks that increase organization profitability.
Now that you understand how Robotic Process Automation (RPA) services can benefit the financial industry let’s look at the 7 best use cases for RPA in finance and accounting examples.
Read more: 7 Step Guide of RPA implementation for Enterprise Digital Transformation
7 Best RPA Use Cases in Finance and Accounting
Finance and accounting have complex, high-volume processes. For example — it takes a lot of time for bank employees to enter customer details for the loan form. Robotic Process Automation (RPA) can easily save this time. It can extract data and transfer it directly to the form by taking it from the centralized system.
You can find many such use cases of RPA in accounting and finance operations. We will look at the 7 most prominent use cases of RPA in operational finance and accounting. Here they are -
1) Customer onboarding
- As KYC becomes an important part of onboarding customers, employees spend more time gathering information than managing the account. RPA bots easily solve this problem by automating the data collection process.
- Using Optical Character Recognition, the RPA software can feed client information from their documents and arrange it into a report. The manager can see the final report from the RPA solutions and make an informed decision. After receiving the manager’s approval(a pre-determined trigger), the RPA bots will directly feed the data into the customer profile for final creation.
2) Data recording
- One of the best use cases of RPA in accounts is data recording. Most of the time, gathering and entering data becomes time-consuming. It also leads to human errors that can result in companies losing huge sums of money.
- Robotic Process Automation (RPA) services enable companies to record data error-free and with complete accuracy. Financial transactions require a lot of care. With an RPA platform, you can add triggers for entering data and recording them in the right sheets. This way, you can save a lot of money by eliminating the need to hire personnel to check the transactions manually.
3) Accounts payable & receivables
- Managing payments is one of the riskiest tasks in the financial services industry. By implementing RPA, you can streamline the receivables and payables process. There are strict timelines you need to adhere to and have proper amounts of payment.
- Through robotic accounting, you can automate the process of payments to vendors and receiving money from stakeholders. You can automate the timeline for sending and receiving payables. RPA bots can build approval workflows and process payments, receipts and ensure that all the transactions take place on time. It also ensures that you adhere to financial audit policies and regulations.
4) Invoice management
- Whether it’s a bank or a financial institution, invoices are a part of their everyday operations. Processing one invoice takes somewhere around 2 weeks as most of the process requires manual input.
- RPA in finance processes simplifies invoice management. Bots gather and extract receipts, collect data, send an invoice to vendors, provide notifications, and offer reconciliation — all in a matter of minutes. RPA bots are available 24×7, so there are fewer chances of missing an invoice. The RPA software directly extracts the data from the receipt as soon as it receives it.
5) Investment management
- The use case of RPA in investment management is still in its infancy. While they cannot directly provide investment advice, they act as a support system for wealth management.
- When it comes to price comparison, RPA solutions deliver promising results. They provide real-time updates on the prices of stocks and mutual funds. By comparing them, you can choose the best options. Apart from that, it can monitor your portfolio and evaluate the fluctuations in your wealth. By understanding that in detail, you can make informed decisions.
6) Financial closing
- One of the best RPA use cases in finance and accounting is the support in financial closing. All the expenses, receipts, and transactions from the year require to record maintenance from excels to sub-ledger. It is a time-consuming process.
- RPA technology enables companies to automate the process of data extraction and recording in the right sub-ledgers. There are hundreds of invoices, receipts, and documents that you need to account for. RPA in banking helps the employees with closing the yearly and monthly accounts by providing the right information at the right time to the right form.
7) Financial planning
- Planning and forecasting financial outcomes possess several challenges for accountants. RPA in the finance department simplifies budgeting and planning the expenses for a period of time.
- RPA in finance and accounting provides data sets to systems that do financial planning and forecasting. Predicting results becomes easier as RPA bots extract data from internal and external sources, feed it into the predictive system, which then provides insights for making informed decisions. It provides up-to-date information so that complex processing is done easily.
Conclusion: RPA in Finance is Necessary
To cater to the growing complexity in financial processes and operations, RPA technology is necessary. Bots and software solutions automate financial transactions and management to the extent where the employees only need to focus on direct revenue-generating activities.
BoTree Technologies, a leading software development company, provides complete RPA automation solutions in the finance and accounting industry. Get started with RPA tools today and increase your efficiency by 50%.
Contact us NOW!
#rpa in finance and accounting #rpa use cases in finance #benefits of rpa in finance and accounting #rpa in financial services #rpa in accounting