Every lending process, whether it is a personal loan or a commercial one, begins with a keen analysis of financial statements. It plays a key role in determining whether a firm or an individual is eligible for the loan – disclosing crucial financial information about the person or the business. It also helps banks and NBFCs dodge bad loans and spot red flags before they bruise their balance sheets. Therefore, it wouldn’t be an exaggeration to say that credit risk management is way more than a regulatory headache and pretty much the lifeline of any financial institution.
However, the traditional methods of analyzing financial reports aren’t really catching up in today’s digital world. In this blog, we will try to understand the importance of financial statement analysis in credit risk management and how Generative AI (Gen AI) is transforming the credit risk assessment process.
Importance of Financial Statements in Credit Risk Management
Financial statements are the backbone of credit risk analysis, acting as the first and sometimes even the last line of defence against defaulters and risky lending. Analyzing a potential borrower’s bank statements helps banks and NBFCs get a clear picture of their financial status, income tax returns, etc, answering crucial questions like – Is the borrower solvent? Or can they service their debt in a downturn?
All of these questions in turn determine a borrower’s creditworthiness. However, it is imperative to understand that financial statements by nature are historical in nature, i.e. they give lenders an idea about what has happened and not what will happen in the future. Relying heavily on 3 core financial statements, traditional methods of credit risk assessment fall short of giving a predictive analysis of future trends, profitability and operational efficiencies, and cash flow patterns.
This is where Generative AI (Gen AI) comes into the picture, as a powerhouse that is completely transforming the credit risk assessment process for lenders.
But before we dive into it, let’s look at the shortcomings of the traditional credit risk models.
Where do Traditional Credit Risk Models Fall Short
Credit risk management is the lifeline of any financial institution – whether it is banks, NBFCs or auditing firms. A single bad loan causes so much trouble for banks, imagine a cascade of them! That could bring down an entire institution. While traditional credit risk assessment models work fine, they have some major shortcomings which need to be addressed:
1. Data Delay
Data delay or latency refers to the time gap between receiving and processing data (in this case financial statements) by the lenders. By the time the banks are done analyzing business financial statements, the current financial position of the business may have shifted significantly. A company that looked stable 6 months ago, may be on the verge of bankruptcy due to any reasons.
2. Unaccounted Qualitative Factors
While analyzing the financial statement the traditional way is a great way of showing numbers, they do not account for external factors that affect a borrower’s ability to repay the loans. Factors such as trade wars, regulatory changes, and tech disruptions, all significantly impact businesses and therefore the ability to repay the loans. All of these factors and their impacts are not actually reflected in the traditional financial statements.
3. Manual Data Overload
Credit analysts analyzing financial reports are often faced with a firehose of information, from financial history and industry trends to borrower history and economic data. Analyzing all of this manually could be challenging, time-consuming, and prone to human error.
All of the above are easily mitigated through AI tools, specifically GenAI or Generative AI models, which analyze financial data and help lenders make informed decisions quickly and efficiently. So let’s look at how Gen AI is changing the financial statement analysis game for banks and NBFCs.
How Gen AI is Revolutionizing Financial Statement Analysis for Credit Risk Management
In recent years Gen AI has seen significant development and adoption in the banking industry, especially in the lending sector. With the recent advancements in deep learning such as LLMs, NLP and ML, artificial intelligence has started taking centre stage in financial statement analysis.
But before we jump at the ‘how’ of things, we need to understand that Gen AI isn’t just another AI-driven tool that interprets financial data. It is a generative model that is trained on vast datasets and therefore is capable of providing predictive analytics from any given financial statement, generating accurate and comprehensive financial reports that not only save time but also reduce human error.
1. Predictive Credit Risk Modelling
One of the biggest advantages of using Gen AI in analyzing business financial statements is that it doesn’t just analyze data, but rather identifies patterns to predict future trends. This leads to better and more accurate prediction of the probability of default and loss-given-default calculations. It also acts as an early warning system that flags declining financial health before it’s too late.
2. Automated Data Extraction
Gen AI significantly eliminates the hassle of manually sorting through loads of financial data. Instead, it automates everything from extracting key information from financial statements in real time to standardizing everything across different formats. Generative AI models also cross-validate information against data sources other than the statements, thereby reducing the risk of fraud.
3. Efficient Utilisation of Unstructured Data
While traditional models rely solely on the numerical analysis of available data, Gen AI can efficiently make use of industry reports and news to detect upcoming economic declines before they hit, effectively skim through social media and other available data to assess reputational risks and analyze earnings call transcripts and investor reports for sentiment analysis. All of this helps in gathering a comprehensive overview of the borrowers’ market standing.
4. Real-time Monitoring & Risk Mitigation
Another fascinating aspect or feature of Gen AI is that it allows real-time monitoring of borrower finances rather than performing quarterly or annual credit reviews. Similarly, with Gen AI, banks and NBFCs get to adjust their strategies simultaneously based on changing market conditions, along with automated recommendations for risk mitigation strategies.
5. Transparent Decisions Through XAI (Explainable AI)
Most financial institutions using AI tools often face the ‘black box’ problem, i.e. they have to rely on AI-generated decisions without a probable explanation. Gen AI solves these issues by clearly explaining how the risk scores have been calculated, or by highlighting the financial and non-financial factors that influence the credit risks. This way credit analysts get a chance to review and adjust AI-generated insights as per their own understanding.
In the current digital financial landscape, running your lending business without integrating Gen AI is like driving a car blindfolded. The success of a financial institution depends on how effectively it uses financial statements to make lending decisions and manage credit risks.
Final Thoughts: The Way Forward
We live in a world where credit risks and frauds are getting more and more complex and unpredictable, and utilizing Gen AI to stay ahead of the curve is no longer an innovation, but a competitive advantage. While analyzing financial statements remains fundamental to the process, relying solely on traditional methods doesn’t work. Gen AI brings the power of prediction and real-time adaptability to credit risk management. The only way forward is a hybrid one that utilizes the power of Gen AI while keeping human expertise for holistic and strategic decision-making. The most successful institutions will be the ones that integrate Gen AI into their frameworks while keeping human oversight a key component.
