Spreading – The Analysis Complex

Spread Numbers
Spreadsheet of Numeric Data

Financial statements can be complex, containing multiple line items, adjustments, and accounting principles. Lenders must possess specialized expertise and analytical skills to interpret financial statements accurately and identify key trends, ratios, and risk factors. Since financial statements are instrumental in financial spreading, serving as the foundation for assessing borrower creditworthiness, conducting financial analysis, and making informed credit decisions, it is important that lenders are able to analyze and interpret the data quickly and effectively.

A Numbers Game

Financial statements can vary significantly in format, presentation, and terminology, depending on factors such as industry practices, accounting standards, and company-specific reporting preferences. This variability makes it challenging for spreading analysts to standardize and compare financial data across different borrowers consistently. This variance is compounded by companies preparing statements using different reporting standards, such as Generally Accepted Accounting Principles (GAAP) or International Financial Reporting Standards (IFRS), where each reporting standard has its own rules, conventions, and disclosure requirements, leading to differences in financial statement presentation and interpretation.

Companies may also employ complex accounting policies and practices to record transactions, estimate reserves, and recognize revenue, particularly in industries with specialized accounting requirements such as financial services, technology, or healthcare. Analyzing financial statements with complex accounting policies requires spreading analysts to have a deep understanding of accounting principles and industry-specific nuances.

Understanding Complexities of Financial Analysis

Financial statements can contain a large volume of numerical data, including balance sheet items, income statement components, and cash flow metrics. This data may even comprise interconnected financial metrics, ratios, and trends that reflect the company’s financial performance, liquidity, solvency, and profitability. Spreading analysts must sift through vast amounts of data, identify relevant information, and extract key financial ratios and indicators before they can even begin to analyze the relationships between various financial metrics, identify trends and patterns, and assess the overall financial condition of the borrower comprehensively.

Non-financial factors such as industry trends, marketing dynamics, and management expertise may also play a role in a borrowers financial health, and these factors may not be captured by a financial statement. This requires analysts to supplement the data in the financial statement with qualitative insights and external sources of information to provide a holistic assessment of borrower creditworthiness. Because analyzing complex financial statements involves a degree of subjectivity and judgment, requiring analysts to interpret financial data, make assumptions, and draw conclusions about borrower credit risk, this can lead to differences in spreading outcomes and credit decisions, particularly when assessing qualitative factors or non-standard situations.

Financial Spreading, Simplified

Automating financial spreading can help alleviate the complexity of financial statements by streamlining processes, standardizing analysis, and improving accuracy. An automated financial spreading solution utilizing machine learning (ML) can significantly contribute to reducing financial statement complexity issues by automating tasks, extracting insights, and improving decision-making.

ML-based spreading systems can do a lot of heavy lifting in financial analysis by automating repetitive tasks such as data entry, ratio calculation, and report generation as part of a larger spreading solution. Automated spreading tools themselves work to optimize spreading workflows themselves, automating routine tasks like data validation, ratio calculation, and report generation. Together, spreading solutions with ML streamline the spreading process, reducing manual effort and accelerating decision-making.

While automated spreading solutions may be able to extract financial data from electronic sources like accounting software, financial databases, or electronic statements, machine learning algorithms can be trained to extract the relevant data from other locations, like files. These algorithms are able to parse unstructured data, identify key financial metrics, and convert them into structured formats. ML’s data extraction helps normalize data by adjusting for variations in accounting practices, currency conversions, and reporting standards, while an automated spreading tool can enforce a standardized spreading methodology institution-wide, overcoming variations in financial statement presentation and terminology to ensure consistency in analysis across borrowers and industries. These tools can also help normalize financial data by adjusting for non-recurring items, accounting anomalies, and seasonality effects, ensuring financial ratios and metrics reflect the underlying business performance accurately.

Why This Matters to Lenders

Using machine learning, automated spreading solutions can use techniques like anomaly detection algorithms to identify irregularities, outliers, and discrepancies in financial statements and flag potential errors, inconsistencies, or fraudulent activities, enabling spreading analysts to investigate further and ensure data integrity. These models can also recognize patterns and trends in financial data, such as seasonality effects, cyclical trends, or correlation between variables. By analyzing historical financial statements and macroeconomic indicators, ML algorithms can identify predictive patterns and forecast future performance more accurately. With built-in Natural Language Processing (NLP), machine learning can also process the textual information in financial statements, such as footnotes, disclosures, or management commentary, complementing the quantitative analysis with qualitative information and providing a more comprehensive view of borrower financial health.

Automating spreading allows lenders to leverage more advanced analytics techniques, such as data visualization, trend analysis, and predictive modeling, to interpret complex financial data effectively. Identifying patterns, outliers, and correlations in financial statements allows analysts to uncover insights and make more informed credit decisions. ML models can assist with these analytics, helping to assess borrower credit risk by analyzing financial statement data and other relevant factors to predict the likelihood of default, estimate credit scores, and categorize borrowers into risk segments based on historical patterns and statistical relationships.

Automating spreading can also help by applying rules-based decisioning algorithms to standardize assessment process and enforce guidelines. These algorithms can evaluate financial ratios, benchmarks, and thresholds to assess borrower creditworthiness objectively, reducing subjectivity and bias in spreading analysis. In addition to the built-in tools to help analyze data and make decisions, many automated spreading tools can integrate with external data sources, such as credit bureaus, market databases, or industry reports, supplementing the financial statement data with additional insights. This integration enriches the analysis, providing spreading analysts with context, benchmarks, and industry-specific information to enhance decision-making.

By automating spreading and employing machine learning models to extract data, financial analysis becomes much more scalable and adaptable to handling large volumes of data and diverse loan portfolios. ML models constantly learn from new data and adapt to changing market conditions, regulatory requirements, and borrower behaviors overtime, updating and refining their algorithms. With the ability to capture feedback, monitor performance issues, and identify areas for optimization, automated spreading solutions support continuous improvement for lenders to refine spreading methodologies update decision rules, and incorporate best practices over time, enhancing the effectiveness and reliability of automated spreading processes.

Conclusion

Overall, automating financial spreading helps lenders overcome complexity issues associated with financial statements by standardizing analysis, improving data accuracy, leveraging advanced analytics, and optimizing workflows. By utilizing machine learning, solutions like Cync Spreading can offer powerful capabilities for addressing financial statement complexity issues by automating tasks, extracting insights, and enhancing decision-making. By embracing automation, lenders can streamline the spreading process, improve data accuracy, and make more informed credit decisions, ultimately driving better outcomes in loan origination and risk management.