4 Major Challenges for Finance Software Development

Amir Kirsh
Amir Kirsh reading time: 6 minutes
December 14, 2023

Like most industries, the ever-evolving nature of finance presents unique challenges to software development and solutions. While these difficulties are not unique or novel, their influence becomes more pronounced over time as financial systems become more intricate and complex.

We have previously discussed embedded finance and algo trading in a previous post. But in this post, we want to focus on challenges in the broader finance domain.

When we speak about financial software development, the list of underlying sub-domains is long and very diverse (although there is, naturally, a lot of overlap):

  1. Banking (with its own list of subdomains such as retail, commercial, online banking, mobile banking, etc.)
  2. Investments (which include analysis, portfolio management, asset management, investment advisory, etc.)
  3. Exchange Markets (stock markets, commodities, foreign exchange markets, cryptocurrency markets)
  4. Algo Trading (a domain of its own with a significant market size that is expected to even grow further with AI and machine learning)
  5. Loan management (personal loans, mortgages, business loans, debt securitization, etc.)
  6. Risk management
  7. Digital payments
  8. Corporate Finance
  9. Accounting and compliance (including tax management)
  10. Cryptocurrency (ICOs, token management, mining, wallets, exchanges, conversion, etc.)
  11. Fraud prevention
  12. Insurance (a broad domain of its own)

Most new financial technology (FinTech) companies usually fit under one or more of these categories – though sometimes they’ll find a new niche. Regardless, these companies are all driven by the idea that R&D is critical to success. In an industry saturated with companies looking for a technological edge, those that fall behind risk losing market position. 

If your risk management does not use the right AI technology, you may take risks that were rejected by other companies and reject those that should have been taken. If your trading latency is too high, you may lose arbitrage opportunities that were identified but exploited by other traders faster.

Despite the vast diversity in domains, FinTech contains a myriad of hybrid fields and sectors, each carving its unique path within the broader industry. Despite their distinctiveness, they share a common thread: the fundamental challenges that highlight the complex nature of finance. Whether it’s navigating regulations, protecting sensitive data, improving performance, or integrating older systems, these industries all face similar hurdles.

The pursuit of security, innovation, compliance, and seamless functionality forms the bedrock of these subdomains’ development journeys.

Understanding and addressing the challenges in financial software development is fundamental to shaping the future of this domain.

High Performance and Low Latency

Financial transactions often require high-performance systems with low latency to execute trades and process transactions in real-time, often on a high-volume scale. Developing software that can handle a large volume of transactions without slowdowns or delays is essential. Ensuring consistent performance under heavy load is a technical challenge that requires optimization and careful system design.

This leads to:

  • Close monitoring of system latency after each change made in the system.
  • Selection of compiled languages with manual memory management and deterministic runtime latency (such as C++, Rust or Java with Realtime GC support)
  • Preferring binary protocols over text protocols (especially between in-house components)
  • Shifting computations to compile time and/or application load-time and warm-up, to reduce runtime latency

In some cases, companies recompile the underlying OS kernel with some specific configurations and tweaks to achieve better performance. Companies looking for even lower latency will rent servers as close as possible to the market they trade on, to reduce communication latency to the minimum (many exchanges offer co-location services, where the provided servers are being physically placed within the same data center as the exchange’s infrastructure). This physical proximity allows for a significant reduction in the time it takes for data to travel between the traders’ systems and the exchange’s trading platform.

Security and Data Privacy

Ensuring the security and privacy of sensitive data is crucial in financial systems. Developing software that can prevent data breaches, cyberattacks, and unauthorized access is a significant challenge for almost any system, yet it remains an uncompromising necessity for almost all financial systems. Moreover, the software needs to adhere to industry standards and best practices for security that change and evolve over time, with new threats and new regulatory requirements.

Rapid Changes

The financial domain is characterized by both stability and reluctance for quick changes but on the other hand with rapid changes triggered by market changes, regulatory compliance and new technologies.

The finance industry is heavily regulated to ensure transparency, security, and fair practices. Regulations evolve to close gaps and breaches. For example, anti-money laundering (AML) regulations have evolved in the past two decades with new requirements and distinctiveness. Keeping up with evolving regulations and integrating them into software systems requires careful attention and expertise.

Other forces that push for change are integrations with new systems. While many financial institutions still rely on legacy systems that have been in place for years, integrating modern software into their workflows can be complex due to differences in technology, architecture, and data formats. Ensuring a seamless transition from older systems to new software without disruptions is significant.

Resiliency and Durability

Most financial systems are required to maintain constant availability during a given window of time. The resiliency and durability challenge requires the ability to withstand and recover from unexpected disruptions while maintaining consistent functionality and data integrity: resiliency refers to the system’s capacity to adapt to unforeseen events, ensuring that it continues to operate with minimal interruption or downtime; durability, emphasizes the system’s long-term stability and capability to retain its functionality over time.

In the context of financial systems that handle critical transactions, sensitive data, and market operations, resiliency and durability become paramount. These systems must be designed and fortified to withstand various challenges, such as cyberattacks, hardware failures, natural disasters, and economic fast downturns (that may create unexpected traffic or data flows). Ensuring the resiliency and durability of financial systems involves implementing redundancy, failover mechanisms, backup systems, and comprehensive disaster recovery plans.

This involves real-time monitoring, rapid response mechanisms, and the capacity to seamlessly switch between various components to maintain operations.

Ultimately the resiliency and durability challenge highlights the critical importance of maintaining stable and functional financial systems, even in the face of unforeseen challenges. Achieving this requires a blend of advanced technological solutions, proactive risk management strategies, and a deep understanding of the unique demands of the financial landscape.

Other Challenges

Other challenges derive from the challenges we’ve discussed above. Some dev managers see testing as a big challenge in the financial domain. Testing is a challenge in many domains and as the environment is noisier and with more moving parts it becomes even more of a challenge.

Integration with internal and external systems is another challenge, already discussed above. This challenge is not going to disappear, though AI tools might make it easier to mitigate (we already discussed trends in this path a while ago, here and here).

Huge amounts of data are also a challenge. From merely the storage capacity to the need to analyze and perform upon tons and tons of information records of all kinds. Data analysis (with big data and machine learning) is developing at record speed to allow new ways and usage of data.

Yet another challenge that is mentioned by development managers is the recruitment of the required skilled employees, those who have not only the technological skill set but also the ability to follow financial thinking.

What’s common to all these challenges?

Something that is common to almost all challenges and is mentioned by development managers is the need to have a rapid development process. To overcome all challenges described there is a need for a very organized architecture and design, together with agile development cycles. New features, integrations, regulation compliance, performance optimization, all these shall be deployed quickly, in a system testing environment or in an integration environment, to allow quick iterations before reaching deployment. And as smaller changes are tested much better than big chunks, this again requires fast and small iterations.

Amir Kirsh
Amir Kirsh reading time: 6 minutes minutes December 14, 2023
December 14, 2023

Table of Contents

Related Posts

6 minutes The Ultimate Guide to C++ Code Review – Part 2

Read More  

6 minutes The Ultimate Guide to C++ Code Review – Part 1

Read More  

6 minutes How DevOps in Game Development is Changing How Studios Work

Read More