Top healthcare IT outsourcing companies in the USA

These days, Fintech is becoming one of the world's fastest-growing industries. Every year, the number of new Fintech startups increases. In 2024, more than 13,000 such enterprises were recorded in the Americas.

Financial technologies include online banking applications, lending platforms, trading platforms, and robo-advisor apps for investing and budget planning. All these innovations demand top-notch Fintech solutions to serve customers, so aspiring Fintech startups need to find a talented team that can build an advanced product.

Key considerations when hiring

Before hiring a FinTech development company, it’s crucial to ensure the specialists have all the essential hard and soft skills. This way, the Fintech application project will have more chances to succeed. So, to hire reliable developers for your project, you need to understand the essential compliance requirements, which you will need to check. 

What are the must-have competencies of web development companies for Fintech?

• Technical knowledge. The company's developers must be well-versed in all the necessary coding languages, frameworks, technologies, and security norms. 
• Background in your niche. Check if a company has a portfolio of projects related to the financial field. 
• Familiarity with applicable finance legislation. The company must know industry norms such as PCI DSS and GDPR.
• Social skills. Seek a team that is able to ensure transparent communication, flexibility, well-organized teamwork, and attention to detail.

Experience in FinTech

Familiarity with the industry is obligatory for financial services development because the specialists must comprehend the main finance principles. A deep understanding of monetary standards and risk management allows developers to craft solutions that meet the niche requirements. Look for a company that has worked on apps or websites for mobile banking, budget management, cryptocurrency wallets, or other products related to Fintech app development. 

Technical Expertise

First of all, make sure the tech specialists know the necessary programming languages. For instance, frontend development for Fintech may require knowledge of JavaScript, libraries and frameworks such as React.js, adaptive and cross-browser layouts, as well as principles of responsive web design and great user experience. At the same time, Python paired with Node.js may be useful for backend development for Fintech.

Secondly, developers must ensure the product's security. To achieve this, they must understand finance APIs and data encryption protocols. Similarly, implementing secure coding methods and following best practices are critical to providing data security. Additionally, expertise in cloud and blockchain technologies can be vital for the safe and reliable launch and scaling of Fintech products.

Security and Compliance Knowledge 

Fintech solutions must meet the latest regulations and standards for user data security. Since Fintech startups and online banks carry sensitive customer data, they are often victims of cyber attacks. That’s why there are legal regulations aimed at the safety of clients and ensuring secure accounting transactions. So, to get secure Fintech development services, check if a company has relevant experience and understanding of the best practices in data protection along with industry norms such as PCI DSS and GDPR.

Communication and Collaboration

Effective communication is crucial for successful Fintech website development. A simple and streamlined interaction provides quick response, the ability to adjust tasks quickly, and full transparency of their implementation. Also, the tech team members must evolve their problem-solving skills, be adaptable, and demonstrate excellent collaborative skills. The last thing can be reached only by an experienced provider, so you should assess the management style in a company.

Research Potential Partners 

Before hiring a Fintech development company, conduct research on your own. A company must have a website providing all the information about its expertise, portfolio, and contact details. Besides, don’t forget to check customers’ testimonials to determine the quality of the services provided. Likewise, case studies of Fintech projects will best demonstrate a company’s expertise and outcomes achieved. 

Develop a Request for Proposal

When you already have a candidate for possible cooperation, the next step is to write a project proposal. Initially, you need to define the project's goals, target audience, desired functionality, and budget. This process will help your possible contractor fully understand your business demands and provide you with a personalized offer.

Conduct Interviews

While conducting a technical interview, ask detailed questions about the development process to check your possible contractor’s skills. Secondly, the organization's leadership style and structure should be analyzed. Inquire about any cross-functional initiatives the company has worked on.

It’s also important to find out who is on the team. In addition to the usual members of a tech team, such as a technical lead, developers, and QA testers, there should also be an expert who works on UI/UX design for Fintech. And, of course, don’t forget to ask about their previous experience in the niche and evaluate their grasp of your business needs and vision of the product.

Portfolio Review

When selecting a software development company, experience is a key consideration. And the easiest way to assess it is by checking the portfolio. Companies that have been on the market for a while usually have numerous projects. However, you need to pay the strongest attention to those connected to the Fintech industry. Check for mobile banking apps, home loan calculators, or trading tools technologies to make sure their design, usability, and the achieved results are close to your goals and needs. 

Conclusion

Developing a Fintech product is not an easy task. Besides the fact you need to retain customers by providing an outstanding service, you also have to worry about things like regulations and data security. That’s why it’s essential to learn how to recruit decent Fintech development specialists. By following the clues given above, you will be able to find suitable candidates.

If you regularly think about how to optimize your business operations by speeding them up, increasing their accuracy, reducing related expenses, and eliminating risks associated with the human factor, perhaps the introduction of artificial intelligence will be the best option. 

Specifically, according to the CompTIA IT Industry Outlook 2024 report, today, about 22% of companies are actively seeking to integrate AI into most of their technological solutions and processes, and 45% of companies are considering its implementation. If you are still in the second category, it's time to figure out which programming language will be able to form the basis of your new highly intelligent software tools.

The term “Artificial Intelligence” describes software algorithms and systems that are aimed at solving problems typical to the human mind—such as learning, as well as pattern generation and recognition. Such algorithms are based on colossal amounts of disparate data collected from various sources. Thus, these algorithms form self-learning software solutions capable of analyzing this data and extracting valuable insights from it.

Top 10 Programming Languages for Artificial Intelligence in 2025 

It is worth noting that AI-powered solutions may not be based on any software language—this can be explained by the high resource consumption of the AI algorithms that form them, as well as the lack of high-level functions. At the same time, there are ten languages that are most often used in AI programming. Which one of them? best programming language for AI?

Python 

At the top of our list of programming languages for artificial intelligence is Python. This programming language appeared long before the popularization of AI development. However, thanks to its low entry threshold, open-source code and extensive compatibility, its community quickly grew, and today, Python is considered one of the three most relevant languages worldwide. Beginners can easily find support and useful resources on platforms such as GitHub and Stack Overflow.

As for the tools for artificial intelligence programming, there are a lot of Python-compatible frameworks, libraries, and APIs, aimed at simplifying and speeding up this process. Among them are TensorFlow, PyTorch, Keras, SciPy, NumPy, Scikit-learn and Matplotlib and many others. Using them, developers can create high-performance, reliable, and scalable solutions that can solve even the most complex research problems. This significantly saves time since, instead of creating everything from scratch, you can simply import ready-made tools.

One of the factors that makes Python one of the most popular AI programming languages is its simple and intuitive syntax, which ensures ease of learning.

Python is also known for its versatility, making it a top programming language for artificial intelligence among data analysis scientists and software developers. For example, it is actively used to create neural networks and machine learning algorithms with frameworks like TensorFlow and PyTorch. Additionally, Python is widely applied in data analysis, helping to identify patterns, trends, and correlations.

Best suited for: AI development, data analysis, and machine learning.

Tip: If your project involves resource-intensive computations and requires a detailed analysis of requirements, it is worth considering other AI programming languages.

R

R, being a statistical programming language, is great for data analysis and visualization. Even though developing artificial intelligence and machine learning solutions is not the primary use case for R, it is still adept at handling very large numbers. So, where popular Python may fall short, particularly in Data Science and Data Analysis solutions, R may be a better alternative.

Additional benefits of this developer tool include support for functional programming, vector computing, and OOP, as well as a number of packages specifically created for AI and ML development (for example, gmodels, RODBC, and OneR).

It includes numerous visualization libraries such as Ggplot2, Lattice, and Shiny, which allow for the quick creation of graphs, charts, and other visual representations of data. Thanks to built-in functions like GLM and LM, specialists can perform statistical modeling.

The Caret library enables building, training, and testing machine learning models, as well as optimizing their performance through hyperparameter tuning.

R also provides tools for performance evaluation, including resampling methods and cross-validation, which help developers improve their AI models. Additionally, R supports integration with other programming languages, such as Java and Python, creating a powerful ecosystem for developing various applications.

Best suited for: Statistical analysis, data processing, and visualization in AI projects.

Tip: R is the best programming language for AI in the early stages of research and experimentation. However, its use for deploying machine learning models in real-world products may be limited, as it lacks built-in compilers and efficient memory management. Moreover, it may be challenging to learn for those who do not specialize in data science.

Java

This portable and scalable programming language is actively used in the development of highly intelligent corporate applications, especially for mobile platforms. Thanks to its platform independence, Java programs can run on any operating system where the Java Virtual Machine (JVM) is installed. In addition to compatibility with popular tools for ML programming and AI programming, such as the Deep Java Library for deep learning, OpenNLP for natural language processing, and Neuroph for building neural networks, Java is easy to write and debug program code.

It is worth noting that this language is object-oriented and scalable. It also simplifies data visualization and supports Swing and SWT (Standard Widget Toolkit), which allow developers to create attractive and complex graphics and interfaces.

Java offers a wide range of built-in libraries and modules, such as JUnit and Apache Commons, which accelerate development and help create high-performance applications. For example, Java is actively used in simulations, robotics, and embedded artificial intelligence systems. Additionally, the RTSJ extension enables the development of real-time systems, particularly for robotic solutions.

Support for multithreading makes Java suitable for resource-intensive tasks that require parallel execution. The language scales well, allowing for the creation of both simple and complex applications capable of adapting to growing demands. A large user community provides access to numerous learning materials and valuable resources.

Optimal choice for: Developing scalable, platform-independent AI applications and embedded systems.

Tip: As an artificial intelligence programming language, Java is an excellent choice for large-scale business AI systems where algorithms need to be transformed into stable and reliable software. It ensures high performance, reliability, and compatibility with various devices, making it an efficient choice for building high-performance solutions.

Weakness: Verbose syntax, not specifically designed for AI.

C++

C++ is considered an extremely powerful language for AI programming and can greatly benefit developers when creating games and embedded systems. Like Python, C++ is a mature language, which does not detract from its advantages, such as flexibility and high performance. C++ has several libraries for machine learning and neural networks that help complex algorithms run faster (including MapReduce, mlpack, and MongoDB). In general, many software engineers prefer this language for building projects that require high speed, as it interacts with training modules in a production environment.

Actually, that’s why this low-level language used for AI is primarily a tool for creating complex banking software, cloud systems, and intelligent speech recognition solutions.

C++ plays a key role in developing complex software systems with a high level of abstraction. Its popularity in the field of artificial intelligence is due to its flexibility, performance, and ability to efficiently interact with hardware components.

Although machine learning libraries such as TensorFlow and PyTorch are primarily designed for Python, C++ allows working with them through APIs or bindings. This makes it possible to integrate powerful tools into developed applications without the need to build all algorithms from scratch. Thanks to support for multithreading and parallel computing, C++ is suitable for creating high-performance AI solutions and stands out among other machine learning programming languages.

Developers can manually manage memory allocation, for example, by allocating more resources for computationally intensive tasks, which reduces latency and improves performance. Additionally, using OpenCL and CUDA enables hardware acceleration, enhancing algorithm efficiency by leveraging the computing power of graphics processors.

Best suited for: Developing high-performance AI applications with hardware resource control and memory management.

Weakness: A complex language designed for system programming.

JavaScript

JavaScript is a scripting language used to add interactivity to web pages. Even though it is not as popular as the AI programming languages ​​described above, it can be extremely helpful in implementing solutions for Data Science, one of the most promising areas for using JS.

In addition, JavaScript is the most popular programming language in the world, so you won’t have any problems finding the right AI development tools and frameworks that are compatible with it. The popularity of this coding language for AI can be explained by several factors: reliability, stability, abundance of high-level functions, libraries, and tools, as well as fairly low requirements for other technical skills of developers (it is likely that you will not have any difficulty finding dozens of AI tools that support JS, which will cover the whole scope of work related to your AI project).

Among AI programming languages, JavaScript is particularly useful for developing real-time AI applications, including virtual assistants, chatbots, and recommendation systems. By using APIs, developers can easily integrate artificial intelligence into web applications, expanding their functionality.

Since code execution occurs on the client side, this reduces server load and increases speed, which is especially important for applications requiring instant interaction.

With the emergence of JavaScript frameworks such as TensorFlow.js, Brain.js, and ConvNetJS, it has become possible to directly implement machine learning in web development. These tools allow training and deploying machine learning models directly in the browser, expanding AI capabilities in the web environment.

Best suited for: Developing interactive AI applications in the browser and integrating artificial intelligence into web projects.

Weakness: Not well-suited for mathematical computations and machine learning.

Scala

This machine learning language is concise and expressive, and is often used in big data solutions. Scala is a multi-paradigm language specifically designed to express common programming concepts in a simple, convenient, and type-safe manner.

Scala is commonly used in the development of machine learning algorithms and pattern detection in large data sets, and all this – thanks to the built-in Smile data processing library, extensive compatibility with AI frameworks and libraries (in particular, BigDL, Breeze, DeepLearning.scala, and so on), as well as its object-oriented and scalable nature.

It allows writing clear and efficient code, facilitating the creation of high-performance applications.

One of Scala’s key advantages is its default support for immutability. This means that once declared, objects do not change their state, making them easier to track and manage throughout the entire development lifecycle. Similar to C++, Scala supports multithreading and parallel computing, enabling the creation of AI applications capable of performing multiple tasks simultaneously.

Thanks to its compatibility with Java, developers can leverage tools and libraries built for Java, including Apache Hadoop and Spark, which are essential for processing large volumes of data. This helps analyze information, identify hidden patterns, and make informed decisions.

Scala also features a powerful pattern matching system, making it convenient for natural language processing (NLP). This is particularly important in areas where AI needs to recognize and analyze user input to generate appropriate responses. Scala’s object-oriented approach supports the development of specialized tools, such as for prototyping and research.

Best suited for: Developing scalable AI applications with big data support and real-time information processing.

However, Scala is not the best choice for projects requiring rapid prototyping, such as NLP or machine learning model development in tight timeframes. This is due to a limited selection of AI-focused libraries and frameworks, as well as a longer compilation process, which complicates quick testing and model adjustments.

Julia

Julia is the best programming language for AI powered scientific solutions and technical projects that require statistical data processing. This is a relatively new language with dynamic typing, parallel programming, and JIT compilation that has proven itself well due to its high speed (Julia-based projects are rarely inferior in performance to their C++-based counterparts), flexibility, and a mature ecosystem.

Many of the tools built into this language use polymorphic dispatch, which makes switching between them much faster than in other languages. Julia has gained popularity among AI programming languages for AI developers due to its high performance and rapid prototyping capabilities. It ensures fast data processing, which is critical in the field of artificial intelligence due to the vast amounts of data that need to be analyzed. Moreover, its syntax is clear and similar to Python, making the learning process easier.

Julia supports interaction with other programming languages, such as R, C, and Python. This allows developers to use powerful libraries, including TensorFlow and PyTorch, integrating them into their projects without the need to rewrite code.

With built-in support for parallel computing and distributed computational processes, Julia is capable of effectively handling large datasets and significant computational loads without a noticeable drop in performance.

As an open-source language, Julia provides access to libraries developed by the community, saving time and effort during development. Notable tools include Plots.jl and Gadfly.jl for data visualization, MLJ.jl used for machine and deep learning, as well as Scikitlearn.jl, which is an interface to the Python library scikit-learn.

Best suited for: High-performance AI applications that require fast data processing and easy integration with other languages.

Weakness: Newer language, smaller ecosystem.

Haskell

Haskell is based on the functional programming paradigm and uses key concepts such as pattern matching, higher-order functions, and immutability. This makes it suitable for handling complex data structures in the field of artificial intelligence.

The language has a strong mathematical foundation, which contributes to the development of complex algorithms and even deep learning models. Thanks to lazy evaluation, expressions are only evaluated when needed, allowing for more efficient resource usage.

Haskell's strong type system and type safety control help avoid errors related to incompatible values in variables, improving the reliability and stability of AI applications developed in this language.

Best suited for: Creating AI systems with complex algorithms and a focus on functional programming.

Weakness: Abstract, rarely used in the field of artificial intelligence.

Lisp

Lisp is a high-level functional programming language that is well-suited for artificial intelligence development due to its flexibility and ability to process symbolic expressions. These expressions can contain both simple data types (such as strings or numbers) and complex structures like lists and arrays. This makes Lisp effective in working with large data sets, performing complex computations and manipulations.

The recursive programming mechanism allows for the creation of powerful algorithms for analyzing and processing significant volumes of information. Additionally, automatic garbage collection enables programmers to delegate memory management to the system, focusing on developing core functionality.

Lisp's dynamic typing allows the interpreter to determine variable types during program execution. This provides high flexibility and makes it easy to work with complex data structures, which is important for artificial intelligence tasks.

Lisp is one of the first AI programming languages to support functional programming. Its simple yet powerful syntax structure encourages experimentation and research in AI, especially in areas that require symbolic expression processing.

Best suited for: Scientific research and experimentation in the field of artificial intelligence, particularly for working with symbolic expressions.

Although Lisp was once an innovative language in artificial intelligence, over time it has given way to statistical machine learning and neural networks, which currently dominate the field. However, it remains indispensable for developing expert systems and logical decision-making models based on symbolic reasoning rather than data analysis.

Prolog

Prolog is known for its capabilities in logical programming and symbolic reasoning, making it an effective tool for creating AI solutions, particularly for knowledge representation and solving complex problems.

Thanks to its declarative approach, developers can describe the logic of computations without focusing on the details of execution flow management. This allows for easy specification of rules, relationships, and facts, which is especially useful when creating knowledge bases in artificial intelligence.

The powerful pattern-matching mechanism enables quick analysis of large amounts of information and finding matches based on logical relationships. This makes Prolog a valuable tool in areas such as natural language processing (NLP), helping systems interpret and respond to queries. Additionally, this feature supports the development of various algorithmic solutions.

Best suited for: Symbolic reasoning, knowledge representation, and solving logical problems in AI.

Weakness: Limited usage and a small number of libraries.

What Is the Most Popular Programming Language Used in AI in 2025?

Considering the top programming languages for artificial intelligence, it is worth noting that in 2025, Python remains the most popular. Its popularity is driven by its easy-to-learn syntax and the wide range of libraries and frameworks, such as TensorFlow, PyTorch, and Keras, which significantly simplify the AI development process.

In addition to Python, other languages are also actively used in AI. These include R, known for its powerful statistical analysis and data visualization tools; Java, which offers high performance and scalability; C++, which provides low-level control over hardware; and Julia, which is quickly gaining popularity for its efficiency in numerical computations.

The choice of programming language for AI depends on the specific requirements of the project, available tools, and the personal preferences of the developer.

The Programming Languages That Are Not Recommended for AI

Along with the extensive list of languages for AI development, there are also those that are better used for solving other problems. These are usually niche and low-level languages, such as PHP and Ruby (scripting languages that do not have the needed flexibility and speed to simplify the development process), C (a low-level language that lacks almost all the functions needed in AI development), and Fortran (despite its use for mostly scientific purposes, it has poor functionality).

Ultimately, a seasoned software engineer will be able to cope with any AI development task using any language; another thing is that, given the expected scope of work, the languages that we described in the previous paragraph are much better suited for these tasks.

If you are looking for an experienced team that will launch the digital transformation of your business processes through custom-made AI and ML solutions, feel free to contact us.

Choosing the Best Programming Language for AI Development

From Python to Scala, different programming languages can be used for AI projects. While they share some similarities, each one has unique characteristics that make it optimal for specific tasks.

Here are a few steps that will help you choose the best programming language for your next AI project:

For Your Project

Start by assessing the requirements and needs of your AI project. Pay attention to the structure of the application and the main features it needs to function properly.

Determine whether you are building a web application, a deep learning model, or a robotics project. Different types of projects may require different programming languages.

For example, JavaScript may be suitable for building web applications, such as those related to trading, as it supports many browser features by default. In contrast, C++ would be the optimal choice for robotics development, as it provides low-level hardware integration.

To Learn

Consider your skill level, as programming languages have different learning curves. For instance, Python has a gentle learning curve thanks to its simple and understandable syntax. On the other hand, C++ has a steeper learning curve, meaning it requires more effort to master.

By taking the time to carefully assess these factors, rather than rushing, you will lay a solid foundation for your AI project. Proper planning at the start will help avoid costly problems in the future. Additionally, it will increase your chances of choosing a programming language that best suits both your current needs and long-term goals.

No matter how ambitious your goals are when you first think of creating a custom software solution, strict budget and deadline constraints can easily dash them. So, what should you do in this case?

Specifically, before your tech team starts implementing a healthtech solution, you can choose two different paths for them: native and cross-platform mobile app development. In the vast majority of cases, in this area, the second option can cover all the needs and requirements for the project without harming its critical aspects, such as usability, performance, reliability, etc. At the same time, you will be able to launch your product on the market faster and save your financial resources. 

However, such a development process is somewhat different from the one that precedes the launch of several native applications. Below, we'll cover how these differences can impact critical aspects of your project and share insights that help you fix them.

Best Practices for Cross-Platform Development in Healthcare

Since cross-platform app development involves adapting a healthcare solution to multiple platforms at once, it is important for the team to adhere to the following best practices to ensure the proper quality of the resulting solution.

Compliance and Security

The first thing you should probably start the development process for the healthcare industry is to ensure compliance with generally accepted standards and policies. The most common among them are HIPAA (Health Insurance Portability and Accountability Act) and GDPR (EU regulation aimed at giving citizens control over their personal data). Along with that, software engineers must use standard security mechanisms such as encryption, integration of secure connection certificates, etc. Ultimately, it is better to check the requirements for medical software development according to the legislation in force in a particular region – this way, you will not face any fines and bans.

User Experience (UX) Design

Now, let's talk about user experience in health apps – it should be smooth regardless of the platform they are running on. Since many healthcare mobile applications are positioned as an alternative to offline services from legally operating medical institutions, they must comply with accessibility rules that are described in detail in WCAG 2.0. These guidelines allow developers to implement seamless interaction with medtech solutions for people with all kinds of disabilities. Depending on the type of medical tasks assigned, you will need to ensure that your healthcare app UI/UX design meets WCAG 2.0 requirements to the appropriate level.

Performance Optimization

Nowadays, high performance is considered one of the key aspects ensuring the app’s survival in the market; however, in the healthcare sector, this parameter is much more critical since someone's life may depend on it. Indeed, if your digital solution involves an urgent call to a doctor, lags in its functioning may end tragically for some of the users. At the same time, it is believed that native applications work faster than those created in the process of cross-platform or hybrid app development. This is not true – such modern development tools as React Native, Flutter, Ionic, and many others allow teams to build software with complex business logic without reducing its speed. Of course, these frameworks will not handle resource-intensive gaming applications, but, most likely, this is not the original task. Whatever approach and technology stack you choose, your team involved in software development for healthcare will need to spend some time testing the application before launching and ensuring that it works equally effectively on all platforms.

Testing and Quality Assurance

Yes, we have already outlined the importance of this process above, but we will repeat it once again: no application can be launched for public access before it has passed a series of tests, both with the participation of the team itself and involving focus groups assembled from representatives of the target audience of your software. The most common types of testing are modular, integration, functional, acceptance, system, smoke, and so on. Each of these types takes place at a certain stage of development (and has to be performed on each platform separately), but the amount of code covered by test cases and the number of iterations will directly depend on the established deadlines. 

Integration

Finally, it is worth saying a few words about integrating your app with existing third-party products and services that your organization already uses. This is usually necessary to expand the functionality of the former and, in particular, to ensure seamless data transfer between applications. However, while the vast majority of modern custom healthcare solutions integrate via API, it is quite predictable that your team will face some issues if you use legacy software. For this, an additional middleware will have to be developed – it will take responsibility for converting data to the required format and back.

Conclusion

In general, as you may know, cross-platform development is cheaper than native development, because most of the code is reused between platforms, and you do not need to hire two separate teams to create native software for each of them (in particular iOS and Android). Moreover, with the right approach, frameworks such as Ionic allow you to build both mobile cross-platform and web applications, reusing most of the code, which, in turn, speeds up and reduces the cost of development for mobile and web platforms, respectively.

So, now, knowing the specifics of developing solutions using cross-platform app development tools, you will be better informed about how to ensure their proper quality and control each stage of their creation. However, if you would like to outsource this complex task without unnecessary doubts so as not to spend too much time on supervising, feel free to contact us.