The importance of categorized data in financial analysis

January 2, 2024

minute read

  • Alice Gregson
    Senior Product Manager

In the intricate world of financial data analysis, the need for categorized data is more crucial than ever.

In its unadulterated form, raw transaction data is akin to an unsolved puzzle — perplexing and devoid of meaningful insights. At Inscribe, we recognize the importance of data categorization to unravel the narrative hidden within your customers' transaction data. 

In this blog post, we’ll delve into the significance of categorized data, its challenges, and our commitment to enhancing transaction categorization for a clearer financial picture.

Why categorized data?

Understanding the story embedded in an applicant’s financial transactions requires more than just raw data. It demands enriched data that goes beyond the surface, offering insights into the nuances of each transaction. 

  • Raw data: unprocessed, unorganized, and unstructured data directly collected from various sources
  • Categorized data: Raw data that has been organized, classified, or sorted based on certain criteria.

The challenges arise in making sense of the information from various transaction components, such as description, amount, positive or negative attributes, transaction method, and regularity. These components collectively form the mosaic of a customer's financial situation, and accurate categorization is the linchpin to decoding this complex narrative.

At Inscribe, we have centered our insights around an applicant's creditworthiness, weaving a narrative grounded in the enriched data derived from meticulous transaction categorization. Speed is imperative, but accuracy is non-negotiable, as the accuracy of our insights directly influences our customers' financial decisions.

Enriching raw transaction data in order to categorize it is no small feat. It involves carefully examining various transaction attributes, aiming to identify the most relevant category. Each part of a transaction contributes to the puzzle, requiring a synthesis that unveils the context surrounding a customer's financial activities. Our dedication to accuracy is unwavering, and we are investing significantly in expanding the breadth and depth of our transaction categorization to unearth new dimensions of individual and business creditworthiness.

Inscribe’s categorization approach 

Our focus is on what truly matters to lenders — correctly categorizing income sources and deciphering the landscape of loans.

Income is not a one-size-fits-all concept. In order to provide nuanced insights, our Credit Intelligence product breaks down income into distinct categories, including commission, retirement, revenue, salary, social security, and interest. This granularity allows us to offer a comprehensive view of our customers' financial streams, catering to the specific needs of lenders seeking precision in their assessments.

Not all debts are created equal, and we understand the importance of distinguishing between them. Whether it's a routine credit card repayment, a merchant cash advance, a mortgage, a payday loan, an auto loan, or a Buy Now Pay Later (BNPL) commitment — each tells a unique financial story. By accurately identifying these nuances, we empower lenders to make informed decisions based on the distinct nature of each debt.

BNPL has emerged as a critical player in the evolving financial landscape. What sets BNPL apart is that this information often eludes traditional credit bureaus. Recognizing its importance, we diligently sift through transaction data to identify BNPL usage. This ensures that our customers and lenders understand financial behaviors comprehensively, even when certain elements are not reported through conventional channels.

The necessity of recategorization

The landscape of financial categorization is as varied as the financial institutions that interpret it. 

Each lender brings its unique perspective to categorizing transactions, leading to disparities in defining good versus bad debt and determining what constitutes true revenue or income. In recognizing this diversity, recategorization becomes a necessity. It empowers users to modify a transaction's category, aligning it more closely with their understanding.

While we acknowledge that achieving 100% accuracy in categorization is an ambitious goal, we are relentlessly pursuing it. Our commitment to accuracy is a pledge to our customers, ensuring that the insights derived from their financial data are as precise and reliable as possible.

Last year, we added transaction categorization with 40+ transaction categories and subcategories, including loan payments, self-transfers, expenditures, and BNPL to our Credit Intelligence solution. Now, you can recategorize transactions as needed with just two clicks. Any customer-level insights relating to that transaction will automatically update in the Credit Insights dashboard once that category changes. 

The future of categorization: Empowering credit teams 

We envision a future where lenders fully control how categorization shapes their financial insights. 

Enabling recategorization is our first step towards putting the power in the hands of our customers. By fostering a collaborative approach with them, we can create a financial ecosystem that truly understands and adapts to their unique perspectives and needs.

At Inscribe, the journey toward perfecting transaction categorization is ongoing. We invite our customers to join us in shaping the future of financial insights, where accuracy, transparency, and control converge to create a holistic understanding of their financial world.

Want to learn more about how to supercharge your credit or underwriting team with transaction insights? Take a self-guided tour of Inscribe or reach out to speak with a member of our team

Start your AI journey today

See how Inscribe can help you unlock superhuman performance with AI Risk Agents and Risk Models.