By: Pradeep K. Mishra, Chief Technology Officer, Banking & Financial Services, LTIMindtree

Introduction

The global financial services sector has recognized the importance of leveraging technological innovations. The leading institutions are already undertaking various digital transformation initiatives to compete with fintechs, neobanks, and tech players. Artificial Intelligence (AI) and Large Language Models (LLMs), such as GPT-4o, are central to many of these transformations.

Leading institutions using large learning models in financial services are seeing significant enhancements in customer engagement and operational efficiency, with AI-driven initiatives contributing to revenue growth. In fact, the market for LLMs in financial services is projected to reach USD 40.8 billion by 2029[i], growing at a Compound Annual Growth Rate (CAGR) of 21.4% from 2023.

However, to fully exploit LLMs’ potential, institutes must consider fine-tuning them to meet their specific needs and regulatory requirements. This article will explain why.

The advantage of finetuning LLMs

LLMs are designed to consume and process complex data and provide creative outputs in human language. While powerful, general-purpose LLMs are trained on expansive datasets, many of which are not relevant to financial services. This makes them simple to use but provides limited adaptability and context to handle domain-specific tasks.

Every financial institution has a treasure trove of data that’s steeped in jargon, technical terms, and regulatory language that general LLMs might not fully grasp. Fine-tuning LLMs with domain-specific texts—such as regulatory documents and financial reports—helps the models understand this terminology, which is essential for tasks like compliance and risk assessment. The ability of fine-tuned LLMs to perform complex financial calculations and analyze unstructured data significantly enhances decision-making processes, enabling faster and more accurate financial predictions. It also helps the model in effectively handling specialized tasks such as fraud detection, customer inquiries, risk assessment, automated trading, and more.

How do you finetune an LLM for your business?

The need for regulatory compliance and data protection requires that LLMs should be able to provide accurate information, free of bias and hallucination, without compromising data security. Here are the steps that financial institutions must follow to fine-tune an LLM:

Step 1: Data collection and preprocessing

Collecting data, including historical transactions, market reports, regulatory documents, and internal data, is the first step. Preprocessing this data to remove irrelevant information, normalize formats, ensure consistency, and anonymize and encrypt data is also crucial. The data must be representative of the financial tasks the model will perform.

Step 2: Model selection and training

The fine-tuning process starts with selecting the right base model. Factors such as the model’s size, pre-training data, and performance on similar tasks guide this choice. Once selected, fine-tuning involves adjusting hyperparameters such as temperature, maximum length, top P, and frequency penalty and employing training methods tailored to financial data.

Step 3: Evaluation and validation

After training, the model’s performance must be tested rigorously using financial-specific metrics. This includes evaluating its accuracy in tasks such as fraud detection, market trend prediction, and compliance reporting. Financial institutions often use precision, recall, and domain-specific benchmarks to measure performance.

However, a point to note is that accuracy has a trade-off relationship with latency, and the balance between latency and accuracy depends on the application. While real-time chatbots may prioritize speed, research or financial models may favor accuracy even if the result takes longer. The future scalability of the model is an important point to consider at this point.

Step 4: Deployment and monitoring

Even after deployment, LLMs need to be continuously monitored to ensure that the model adapts to new data and maintains accuracy over time. Regular updates and retraining are required to keep the model aligned with changes in financial regulations and market dynamics​.

Challenges and considerations

Here are some of the major challenges that are faced by financial institutions looking to fine-tune LLMs:

Data privacy and security

Challenge: LLMs handle sensitive financial data, including Personally Identifiable Information (PII) and confidential financial details. LLM security management is necessary as any breach can lead to severe legal and financial penalties.

Solution: Implementing robust data anonymization, encryption, and access control policies at the preprocessing stage ensures compliance with global privacy standards such as General Data Protection Regulation (GDPR), Bank Secrecy Act, Gramm-Leach-Bliley Act (GLBA), etc. Building a centralized AI governance structure can help institutes manage these risks.

Regulatory compliance

Challenge: Financial institutions are heavily regulated, and large language models in financial institutions must be fine-tuned to comply with regulations like the GDPR and the Financial Industry Regulatory Authority (FINRA). Without the right safeguards, LLMs might produce outputs that violate these regulations.

Solution: Training LLMs using domain-specific regulatory data and employing automated compliance checks, including continuous auditing and validation processes, should detect and address compliance issues in real-time.

Model interpretability and transparency

Challenge: LLMs are often perceived as “black boxes,” making it difficult to understand how they arrive at decisions. This lack of transparency can raise concerns among regulators and stakeholders, especially in high-stakes decisions like credit scoring or fraud detection.

Solution: Using Explainable AI (XAI) frameworks to enhance model interpretability will allow institutions to explain how a model reached a particular outcome, which is crucial for regulatory approval and customer trust. A Gartner[ii] survey found that improving interpretability is essential for financial institutions looking to scale their AI efforts.

Scalability and infrastructure limitations

Challenge: Deploying LLMs requires substantial computational power, storage, and infrastructure. Many financial institutions rely on legacy systems that cannot support the demands of modern AI models, leading to scalability challenges.

Solution: Transition to cloud-based infrastructure, which offers scalable computing resources and lowers costs. Adopting robust, high-performance cloud solutions allows financial institutions to process data faster and more efficiently while supporting large-scale LLM deployments.

Data bias

Challenge: LLMs can perpetuate and amplify biases if they are trained on datasets that reflect historical inequalities or discriminatory practices.

Solution: Carefully curate training datasets and implement bias detection algorithms to identify and mitigate bias before and after training. Regular audits of model outputs for bias are essential to ensure fairness in decision-making processes. Continuously refining LLMs using diverse datasets will help reduce bias and improve ethical performance.

Real-world use cases

LLMs are already being used by financial institutions for various use cases including customer interactions (AI-powered chatbots and virtual assistants), document analysis and content summarization, and offering personalized offerings. However, here are a few use cases that can be enhanced using customized LLMs and Retrieval Augmented Generation (RAG) techniques:

Detecting suspicious behavior and fraud

Leveraging LLM security management systems can analyze large volumes of customer data and transaction history to enhance credit risk assessment, identify and report fraudulent activities, and detect patterns of suspicious behavior. Some banks are already utilizing AI for trade surveillance, employing sophisticated models to detect and report anomalies with remarkable precision.

Automating processes

LLMs can generate predefined templates for various financial documents, such as loan applications or invoices, by extracting relevant information to complete responses. This automation can streamline lengthy processes, like customer onboarding, reducing human error and interaction, and enhancing customer experience and satisfaction.

Conducting financial analysis and research

LLMs can sift through vast amounts of publicly available information—such as news reports, social media content, company documents, and historical trends—to provide analysts and investors with comprehensive insights. They can generate research reports, forecast potential trends, and offer detailed summaries of investment opportunities, effectively personalizing financial advice and recommendations.

Ensuring regulatory compliance

LLMs can be fine-tuned to read and understand vast amounts of regulatory documents, automating compliance report generation. They can also help in monitoring transactions for Anti-Money Laundering (AML) compliance. This will help reduce the time and resources needed for compliance checks, helping financial institutions avoid costly penalties.

In conclusion

At LTIMindtree, we are infusing AI in everything we do and strongly believe that AI is for every persona across the financial industry. As financial services become increasingly AI-driven, staying ahead of advancements in LLMs is no longer optional—it’s crucial for maintaining a competitive edge. The ability of LLMs to handle complex financial data will continue to improve over the next 3-4 years with new techniques and training methods. For financial institutions, ongoing fine-tuning of these models ensures they remain aligned with evolving markets and regulations. This is not just about optimizing performance; it’s a strategic move to future-proof operations, drive innovation, and ensure compliance. Investing in LLM fine-tuning today will help you secure long-term success and operational excellence for tomorrow.

[i] The Impact of Large Language Models in Finance: Towards Trustworthy Adoption, The Alan Turing Institute

[ii] Building a Value-Driving AI Strategy for Your Business, Gartner