Signals Fintech

Why Choose Java for AI and Data Science in Fintech

While Python and R have traditionally been the most popular languages for building AI, ML, and DS solutions, Java has emerged as a preferred choice for companies of all stripes.

In this post, I’ll show why fintechs ramp up their use of Java for creating next-level AI.

Fintech companies are using the powerful trio of Machine Learning (ML), Artificial Intelligence (AI), and Data Science (DS) to create data-driven products that are transforming the industry. These technologies are now vital in getting insights into client experience and acting on them to perfect the product and drive it to success.  Scaling is one of the prerequisites for utilizing such tools, and that's where Java steps in the game. But that's not the only ace it has up its sleeve.

What makes Java fit for AI and data science

Being one of the longest-present coding languages, Java is widely used in enterprise systems. Given, it may harbor many benefits for your AI project, including the following:

  • Automated memory management due to Java Virtual Machine’s (JVM) runtime environment, which saves tons of effort for your developers;
  • DS methods include NLP, data processing, and statistical analysis. By utilizing Java in data science, your business can smarten up products and apps with machine learning algorithms;
  • Simple integration;
  • Quick talent hiring from the pool of specialists with in-depth knowledge to run data science projects;
  • Key big data frameworks like Kafka, Hadoop, Apache Spark, Hive, Cassandra, and Flink are JVM-based;
  • Lambda feature, allowing for easier DS development;
  • A vast ecosystem of development tools matches a variety of Java’s cloud providers, making the platform a premier-class choice;
  • Multiple libraries and frameworks, including the free ones, for DS and ML;
  • Recent additions like Project Panama as part of OpenJDK facilitate application scaling.
Java-plus-AI

Java against Python in AI development

While R and Python enjoy immense popularity in AI and DS app development, Java has all the potential and real capability to get a sweet spot in the area. Compared to the other two languages, Java leverages easier and more effective integration into widely used stream processing apps. Its capabilities have been perfected for running data science activities straight from the Java environment. 

Another benefit is that the JVM comprises a managed runtime environment that can alleviate the memory management responsibilities of your dev team. Utilizing just-in-time compilation to produce native code can lead to a substantial performance boost compared to Python and R or statically compiled languages such as C or C++.

However, Java lacks inherent backing for numerous data frame operations, obliging developers to adopt third-party libraries like Spark. Additionally, Java's deficiency of native support for REPL can make using Jupyter notebooks more challenging. While Scala and Kotlin offer REPL support, Python integrates more efficiently with notebooks.

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Why Fintech underestimates Java

Java’s success in the domain of server-side applications has been remarkable. By scaling global workloads, it has earned the trust of tech giants like Apple and eBay, which put a solid on Java’s reputation in working with big data. However, this success may have hindered Java's adoption in data science applications. 

It became a common assumption that Java was unsuitable for data science since it’s predominant in enterprise apps. Thus, Python and R made it to the mainstream, and Java has become a frequently overlooked alternative. What many do not realize is that Java is the underlying technology for numerous data science platforms, including SAS and Rapid Miner. Other platforms, such as Alteryx, seamlessly integrate with Java.

The challenges you might encounter with Java in AI

While Java fits well in the AI development context, it has its distance from being an ideal choice. But let’s be honest; there are always a few steps left between good enough and perfect. Below are those for Java:

  • Java has a scarce choice of data visualization libraries. 
  • Data scientists new to the job may require more time to become proficient in Java, and using data science APIs with Java can complicate things further.
  • Java has better readability than Python, but its syntax is more complex. This results in more time for the team to become proficient in Java if they lack expertise in this language.
  • Some of the most powerful libraries for data science only support Java through a set of API wrappers. Due to this, the initial setup of a Java environment for developing data science applications can be challenging.

Data Science Platforms built on Java

Looking at the future of Java and AI models

Java has a strong position in powering the data pipelines for AI. As for its use in programming AI models, opinions differ.

JVM’s Kotlin, Scala, and other languages are a better fit for data science workloads than Python. However, Python beats them at the learning curve, so it’s hardly possible for Python to get retired at all.

On the other hand, the competition can very well mean that JVM languages have much room to take in deep learning soon. One reason is the JVM community, which seems to keep itself busier than others in this area. Some of the achievements that are easy to spot are the growing use of deeplearning4J and PyTorch’s new Java API. Also, there is now DJL, a robust open-source deep learning framework. If these frameworks cement their presence in deep learning, we’ll see more ML libraries spring up.

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That's how you AI with Java

In times of data-driven, Java is an excellent option for businesses that want to mix time-tested reliability and performance with the latest tech advancements. Considering the rate at which Java releases updates, the contrast between it and AI leaves out any hint of legacy technology.

Easy scaling and integration, automated memory management, and other advantages make Java a viable alternative to Python and other interpreted languages in AI, ML, and DS development.

Some of the main drawbacks of using Java include complex syntax and challenging initial setup. However, with a team proficient in Java and having years of running and completing Fintech projects, these disadvantages have no say in your business. If you want to know how our Java expertise can help your business grow and take more space in the market, schedule a quick call with our experts.

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