Use boring languages with LLMs

For years, the tech world has chased the shiny new object. Python, JavaScript, and more recently, Go and Rust, have dominated the conversation. But a quiet revolution is brewing in the financial industry, and it's centered around…COBOL? And Fortran? Yes, you read that right. While these languages might not be topping "most popular" lists, they’re proving surprisingly vital in the age of Large Language Models (LLMs). This article dives deep into why these “boring” languages are experiencing a resurgence and how they’re unlocking new possibilities in finance.

§The Elephant in the Room: Legacy Systems and Financial Infrastructure

The financial world runs on decades of accumulated code. Much of this code – the backbone of core banking systems, insurance claims processing, and trading platforms – is written in languages like COBOL (Common Business-Oriented Language) and Fortran. These languages were the workhorses of the 20th century, chosen for their reliability, efficiency, and suitability for complex calculations.

But time marched on. Newer languages emerged, and fewer and fewer developers were trained in COBOL and Fortran. This created a looming crisis: a “skills gap” threatening the stability of critical financial infrastructure. Many in the industry viewed these systems as increasingly difficult and expensive to maintain. Replacing them wholesale? A multi-billion dollar undertaking, fraught with risk.

The problem isn't necessarily the languages themselves, but the knowledge surrounding them. The original developers are retiring, and finding experts to maintain these systems is increasingly difficult. That's where LLMs come in.

§How LLMs Breathe New Life into Legacy Code

Large Language Models, like those powering ChatGPT and other AI tools, are showing remarkable ability to understand, translate, and even improve code written in older languages. This isn’t about replacing these systems, but about augmenting them and making them more manageable. Here’s how:

Code Understanding & Documentation: LLMs can analyze complex COBOL or Fortran code and generate clear, concise documentation. This is a massive win for organizations struggling to understand the logic behind critical processes. Imagine feeding a program a 50,000-line COBOL routine and receiving a human-readable explanation of its function in minutes.
Code Translation: LLMs can translate code from older languages into more modern ones (like Python or Java). This isn’t a simple one-to-one replacement. LLMs can consider context and suggest optimizations during the translation process, making the new code more efficient.
Bug Detection & Remediation: LLMs can identify potential bugs and vulnerabilities in legacy code, suggesting fixes based on best practices. This dramatically reduces the risk of system failures and security breaches.
Automated Testing: LLMs can generate test cases for legacy systems, helping to ensure that changes don’t introduce regressions. This is crucial for maintaining the stability of these critical applications.
Modernization without Full Rewrite: Rather than a costly and risky full system replacement, LLMs facilitate a phased modernization approach. Specific modules can be translated or enhanced, allowing organizations to reap the benefits of modern technology without disrupting core operations.

§Specific Use Cases in Finance

Let’s look at some concrete examples of how LLMs are being applied to “boring” languages within the finance industry:

Risk Management: COBOL is prevalent in risk management systems. LLMs can analyze these systems to identify potential vulnerabilities and ensure compliance with evolving regulations. They can also simulate stress tests more efficiently.
Fraud Detection: Fortran is frequently used in high-performance calculations, including those related to fraud detection. LLMs can refine and optimize these algorithms, leading to more accurate and timely fraud alerts.
Algorithmic Trading: While modern languages dominate new algorithmic trading systems, legacy Fortran code still powers parts of some high-frequency trading platforms. LLMs can help optimize this code for speed and efficiency.
Loan Origination and Servicing: Many loan origination and servicing systems are built on COBOL. LLMs can automate tasks like data validation, document processing, and compliance checks, streamlining the lending process.
Insurance Claims Processing: COBOL-based systems are common in insurance claims processing. LLMs can accelerate claims adjudication, reduce errors, and improve customer satisfaction.

§The Power of Precision: Why COBOL and Fortran Still Matter

While Python’s flexibility is lauded, COBOL and Fortran offer unique strengths, especially in the finance domain:

Precision: Fortran, in particular, is renowned for its precision in numerical computations. This is absolutely critical in financial modeling, where even tiny errors can have significant consequences.
Determinism: COBOL is highly deterministic, meaning it produces the same results given the same inputs. This is essential for auditing and regulatory compliance. Randomness can be a liability in financial calculations.
Data Handling: COBOL excels at handling large volumes of structured data – the lifeblood of financial institutions.
Reliability: These languages have been battle-tested over decades and are known for their stability and reliability.

These attributes aren’t easily replicated in newer languages without significant effort. LLMs allow us to leverage these strengths while simultaneously addressing the challenges of maintaining aging codebases.

§The Rise of "Low-Code/No-Code" for Legacy Systems – and LLM's Role

The combination of LLMs and low-code/no-code platforms is particularly powerful. LLMs can translate legacy code into a representation suitable for low-code tools, allowing business users to make changes and enhancements without needing to write code themselves.

Imagine a financial analyst needing to modify a loan calculation rule. Instead of submitting a request to a developer, they could use a low-code platform powered by an LLM to adjust the logic directly. This dramatically accelerates innovation and reduces reliance on scarce developer resources.

§Challenges and Considerations

§While the potential is enormous, there are challenges to overcome:

Data Privacy & Security: Feeding sensitive financial data into LLMs requires careful consideration of data privacy and security regulations. https://example.com/ (consider linking to a book about data security in finance here).
Hallucinations & Accuracy: LLMs can sometimes "hallucinate" – generate incorrect or nonsensical outputs. Rigorous testing and validation are crucial when applying LLMs to financial code.
Model Training & Fine-Tuning: Off-the-shelf LLMs may not perform optimally on legacy code. Fine-tuning models with domain-specific data is often necessary.
Vendor Lock-In: Reliance on specific LLM providers can create vendor lock-in.
Explainability: Understanding why an LLM made a particular change to the code can be challenging. Explainability is crucial for trust and accountability.

§The Future is Hybrid

The future of finance technology isn’t about abandoning legacy systems. It’s about creating a hybrid environment where older, reliable code coexists with modern technologies. LLMs are the key to bridging the gap, unlocking the value hidden within these “boring” languages and empowering financial institutions to innovate and adapt to a rapidly changing world. It's not about replacing the past, but leveraging it for the future.

§Disclaimer

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