Python's popularity in FinTech stems from its ease of use, rapid development capabilities, and a rich collection of specialized libraries such as NumPy, Pandas, SciPy, and scikit-learn. These libraries are instrumental for quantitative analysis, risk modeling, and implementing machine learning algorithms for fraud detection or credit scoring. Its extensive community support also ensures a wealth of resources and readily available solutions.

Java's 'write once, run anywhere' capability is a significant advantage for global financial institutions. Its mature ecosystem, including frameworks like Spring, facilitates the development of complex, secure, and scalable enterprise applications such as banking back-end systems, trading platforms, and payment processing infrastructure. Its strong memory management and concurrency features are also vital for high-throughput financial operations.

C++ provides low-level memory manipulation and direct hardware access, enabling developers to optimize performance to the highest degree. This makes it indispensable for algorithmic trading systems where milliseconds can mean millions of dollars. Its efficiency is also critical for complex financial modeling and simulations that require significant computational power.

With the rise of web and mobile-based financial services, JavaScript has become indispensable. It powers the interactive elements of online banking portals, trading dashboards, and mobile payment apps. Node.js, a JavaScript runtime environment, also allows for efficient back-end development, enabling full-stack JavaScript solutions.