Financial statements serve important functions in decisions made for business lending, investment planning, M&As and corporate fraud prevention strategies. Their significance stems from the need to minimize risks and streamline processes. The objective of most decision-making workflows entails verifying the target organization’s financial records, such as balance sheets, P&L summaries and bank statements. While they are handled manually, trained employees scour through hundreds of pages to extract figures, cross check calculations and interpret notes to arrive at a decision.
This ensures that the data is vetted and reviewed for risk-free decision making. However, this is a long process, inherently slow, error-prone and cannot meet the growing demands of modern businesses that are scaling rapidly.
In order to catch up pace with competition, AI powered tools have become essential to business processes. Progressive lenders must automate routine workflows, to free up bandwidth for business critical tasks, leverage AI to analyze financial statements, assess creditworthiness, fulfil customer verification and other regulatory mandates.
The Age of LLMs (Intro to LLM)
Generative AI based applications have moved from labs into the mainstream sphere into laptops and smartphones. The concept of AI in the early days of AI research was to have intelligent models that could generate original content. ChatGPT in late 2022 came with this idea into the mainstream, stimulating a range of creative applications around the world. Generative AI is an umbrella covering methods of content creation, including text, images, audio and video. It involves a suite of Machine learning techniques, includingLanguage Learning Models(LLMS). These LLMs are highly trained to give responses to queries in natural language. LLM models can be interactive in conversations, respond to questions, and summarize information with human-like characteristics. The growth of LLM-based applications is giving rise to a range of new-age business applications. In this article, we shall discuss how financial institutions and banking can benefit from AI to analyze financial statements.
AI, especially LLMs are relevant to businesses of any type, size or segment with their ability to lower operating costs, enhance customer experience and improve the pace of decision making. A report by the Alan Turing Institute predicts the global LLM market to grow to $40.8 billion in 2029. A McKinsey Global Institute study illustrates what this means to businesses – a value of $2.6 trillion to $4.4 trillion per annum. The banking industry is predicted to make the most of the opportunity with an annual increase in revenue of $200 billion to $340 billion, most of it in the corporate and retail banking segments.
Forward thinking leaders in the industry can discern the rapidly emerging developments in the Generative AI landscape and the exciting possibilities that it presents.
How LLMs Can Transform Financial Statement Analysis Workflows
KYC Checks: The customer documents are verified by banks and lenders in the documents processing phase. Banks follow KYC verification as a regulatory measure essential for fraud prevention and risk free operations. Purpose-built algorithms empower AI tools to analyze financial statements comprehensively, with precision and accuracy required to make any lending decision. Similarly, they also make ID and address validation simpler and faster, with reliability. They authenticate information on the documents against government or publicly available records and correlate the data across documents, enabled by a simple and customizable set of rules.
Creditworthiness assessments: Financial services companies undergo this routine but complex exercise of computing credit scores. It demands compilation of data from a wide range of sources, such as interpreting the nature and values of transactions from bank statements, cash flow statements, profit and loss statements, balance sheets and tax documents. The data is further analysed along with information about the customer’s business environment, industry trends and the financial stature of the promoters or directors. Financial statement analysis AI tools have accelerated assessments and have the capability to identify varying patterns. Organizations can ensure comprehensive, precise evaluations with these AI tools offered for different document types by fine-tuning their algorithms and adopting suitable evaluation rules aligning with their corporate policies. LLMs are often used in these tools, making them intelligent to monitor and detect frauds, thereby minimizing risks while improving regular loan origination processes.
Business Analytics: In a dynamic market infused with evolving technology, leaders need to keep their products and services to match user expectations. Business leaders obtain valuable insights from balance sheets and cash flow statements. However, applying AI to analyze financial statements gives them actionable intelligence and bandwidth, that can help identify unexplored markets, build new products or reposition existing ones and optimize their go-to-market strategies and sales campaigns.
What Banking Businesses Can Gain from LLMs
- Faster workflows: LLM-based automation ensures a quicker turnaround time meeting customer expectations and more, for the company to aim for a higher market share.
- Higher accuracy: Loan approvals or investments benefit from robust, objective evaluation that consistently delivers accurate results, regardless of the complexity, size or nature of the client.
- Scalability: AI solutions are built with a scalable design, allowing organizations to meet growing demands effortlessly without compromising the system performance or quality of results.
- User experience: LLMs enable user friendly systems with a simple interface making it easy for both employees and customers to access it for various functions,
- Competitive edge: Organizations have experienced first mover advantage by harnessing innovative AI strategies for their businesses.
Understanding the Apprehensions
Business leaders are often wary of disruptive developments, understandably, which force them to reorient their business processes. A revolutionary technology such as LLMs comes with few genuine concerns that slows down their adoption for real world applications. This discussion would be incomplete without addressing the reservations that businesses have before adopting AI tools to analyze financial statements.
- Data security and privacy: Data integrity is critical to businesses. The sensitive nature of data being processed, requires failsafe measures to prevent possible misuse and leakage of information.
- Capability Limitations: LLMs struggle often with abstract reasoning, as they are trained on empirical data. As financial statements can be complex contexts, Generative AI may produce unpredictable results, which require additional validation.
- Bias: The inputs used to train the models may introduce biases, leading to skewed, inaccurate, and possibly risky decisions.
- Interpretability: LLMs are often visualised as black boxes, which makes regulatory compliance challenging.
- Consistency: The output of LLMs can vary even with the same input prompt as they can work with natural language inputs, unless the prompt engineering is technically sound to deliver accurate results.
Generative AI technologies continue to break new ground at a feverish pace. With enhanced capabilities, performance, and efficiency, they facilitate innovative financial statement analysis AI tools that address such concerns.
Data security and privacy issues are guarded by a robust infrastructure design built with multiple layers of security while adhering to industry best practices. In some cases, on premise deployments are recommended. An LLM system has versatile architecture that can be adopted to the local compliance and data protection regulations. These solutions are customizable to be integrated with existing systems while avoiding unnecessary complexities. The ideal way to ensure minimal bias and constant improvement in accuracy is to train with real-world data and optimizing the system with inputs from stakeholders.
