--- title: Structured and Unstructured Data in Data Driven Underwriting description: Know the differences between structured and unstructured data and how unstructured data is incorporated into machine learning credit underwriting models. image: https://trustscience.com/hubfs/2025%20Brand%20Assets/Blog%20banner%20placeholder.png --- [![Trust Science Logo (Menubar)](https://trustscience.com/hubfs/Trust%20Science%20Logo%20(Menubar).jpg)](https://trustscience.com) - Solutions - [Credit Bureau+™](https://trustscience.com/credit-bureau) - [T°Score™](https://trustscience.com/t-score) - [CashFlow+™](https://trustscience.com/cashflow) - [Services](https://trustscience.com/services) - Industries - [Auto Lenders](https://trustscience.com/auto-lenders) - [Installment & Single Repayment Lenders](https://trustscience.com/installment-single-repayment-models) - Company - [About Us](https://trustscience.com/about) - [Our Team](https://trustscience.com/team) - [Press Releases](https://trustscience.com/blog/tag/press-releases) - [Careers & Culture](https://trustscience.com/careers) - [Patents](https://trustscience.com/technology-patents) - [Resources](https://trustscience.com/resources) - [Marketing Channel Partners](https://trustscience.com/partners) - [News & Blog](https://trustscience.com/blog) - [Events](https://trustscience.com/events) - [Gartner Research](https://trustscience.com/resources/gartner-research-mentions) - Support - [Consumer Disclosure](https://trustscience.com/legal/consumer) - [Consumer Resources](https://trustscience.com/legal/help-page) [CONTACT US](https://trustscience.com/contact) [REQUEST A DEMO](https://trustscience.com/demo) Blog Archive # Structured and Unstructured Data in Data Driven Underwriting Know the differences between structured and unstructured data and how unstructured data is incorporated into machine learning credit underwriting models. [Trust Science](https://trustscience.com/blog/author/trust-science) May 19, 2025   ***Disclaimer**: Thank you for reading. The content you are currently viewing is archived material and may be outdated. We strive to provide accurate and up-to-date information, but please be aware that the content in our archives may no longer reflect the latest developments, events, or changes in our field.* Gartner estimates that [80% of enterprise data](https://www.gartner.com/document/3077117?ref=solrAll&refval=184973918&qid=7ac0f5706d57ff6d65c141149d8da3e4) is comprised of unstructured data. Yet until tech advances afforded by machine learning, structured data has been the go-to for data analytics and models. The advances in computation power have afforded organizations the ability to analyze and incorporate unstructured data into their business decisions. One could even say, unstructured data is the gateway to data driven underwriting.   > [Nav Kesher](https://www.analyticsinsight.net/machine-learning-will-convert-your-unstructured-data-into-structured-data-for-usable-sources-of-insight/), head of data sciences for the Facebook Marketplace Experience said“…businesses have, in the past, ignored or forgotten about such \[unstructured\] data, that is slowly starting to change,” at the AI Summit in San Francisco. This post is the first of a four part blog series that covers topics in structured and unstructured data for data driven underwriting. This first blog provides an overview of the main differences between structured and unstructured data, its impact within loan underwriting, and how unstructured data is incorporated into machine learning credit underwriting models. The rest of the blog series will educate you on the following topics of unstructured data in underwriting: - The importance of ontology in data - The role of time series data in lending - Common data mistakes hindering your ability to adopt AI and machine learning ## Structured vs. Unstructured Data Two of the key attributes of unstructured data is that it doesn’t adhere to a standard form and definition. Generally, unlike structured data, unstructured data does not fit into traditional tabular form, it often requires transformation into structured data before it can be used. In addition, unstructured data is readily interpretable by humans and structured data is readily usable by computers. Examples of structured data are data that fit into MS Excel sheets and examples of unstructured data are email conversations, audio or speech data, text messages, and images. Unstructured data provides a large volume of insights for. With unstructured data, you need to decide what comprises the data, how to interpret it and implement the data. ### Structured Data We say a dataset is structured when each data element, that is each attribute represented in the dataset, has an associated fixed structure. For underwriting purposes we consider any data in tabular form as structured data. Relational tables are the most popular type of structured data. Examples are data that are stored in MS Excel sheet or comma-delimited data stored in CSV files. This type of data can be stored in classic relational database and thus can be queried by SQL and its variants. ### Unstructured Data For underwriting purposes we treat semistructured data as unstructured data. Semistructured data has a flexible structure while unstructured data is expressed (in writing, image or voice) in natural human languages with no specific structure defined. XML and JSON are the markup languages commonly used to represent semistructured data and images, audio, videos, emails and PDFs are examples of unstructured data. We consider both semistructured and unstructured data as equivalent, for our purposes. Combined they constitute about 80% of all generated data; both types of data require parsing before they can be used to train artificial intelligence/machine learning algorithms. For example, let’s consider a resume in PDF format. It is essentially a big blob of text and requires parsing to make sense of what the actual data means and its hierarchy of information, headers vs body text, etc. You also need to interpret what the information contained within the resume means. For example, a job title such as “Loans Manager” could be interpreted in various ways based on the set of roles and responsibilities that could differ based on the individual’s actual role. ## The Impact of Unstructured Data in Data Driven Underwriting If you only use structured data in your underwriting, then you’re making lending decisions based on less than 20% of the data available to you. Using unstructured data allows you to tap into a large volume of previously unavailable insights. Traditional methods of credit decisioning make use of structured data like bureau data. In today’s digital age, only an estimated 20% of enterprise data is structured data. Furthermore, according to [IDC](https://www.computerweekly.com/feature/Storage-101-Structured-data-and-its-storage-needs), unstructured data is outpacing structured data, growing at 29.8% YoY, against 19.6% for structured data. That’s almost a 50% difference in growth rate. So how do you access all of the data available to you for credit underwriting? By incorporating unstructured data into your underwriting decisioning, you can make far better predictions of what’s a good loan vs what’s a bad loan, giving you a 360 degree view of an applicant’s creditworthiness. ## Incorporating Unstructured Data into Machine Learning/AI Based Credit Underwriting Unfortunately, unstructured data is not immediately usable for machine learning/AI based credit underwriting unless it is parsed into structured data. Machine learning models developed by data scientists are computer algorithms that are monolingual, and speak in terms of binary digits, i.e. bits. Unstructured data will need to be broken down into the form of structured data for the model to understand and be trained on. The first step to incorporating unstructured data into your business and underwriting strategy is align stakeholders around the desired benefits of data driven underwriting enabled through the use of unstructured data. In other words, what does success look like for you as a lender? Is it to make better loan predictions and understand whether someone will default or not? Or is it to better identify your key customer profile? Or is it to acquire a new customer set like thin files and credit invisibles? Once the objective is set, it can aid and guide how the unstructured data should be converted parsed to structured data for machine learning. In the next blog of our four part blog series we cover a crucial component regarding the ontology of data, or the agreement between the meaning of data. [Blog Archive](https://trustscience.com/blog/tag/blog-archive) ## Similar posts Blog Archive ### [The Role of Time Series in Loan Data Analysis](https://trustscience.com/blog/the-role-of-time-series-in-loan-data-analysis) Time is very important in unstructured data, as it provides us with time-stamps for loan data analysis and predictions. Trust Science Feb 5, 2020 Blog Archive ### [How Alternative Data Increases Accuracy in Credit Assessment](https://trustscience.com/blog/how-alternative-data-increases-accuracy-in-credit-assessment) The wide range of unstructured data available from alternative data sources, machine learning and AI increases the effectiveness of risk assessment. Trust Science Aug 2, 2022 Blog Archive ### [The Importance of Data Ontology in Big Data Loan Underwriting](https://trustscience.com/blog/the-importance-of-data-ontology-in-big-data-loan-underwriting) Ontologies provide machines with the ability to make sense of big data, a key foundation to how machines learn and understand. Trust Science Jun 4, 2019 Trust Science — Footer Redesign [![Trust Science](https://trustscience.com/hubfs/2025%20Brand%20Assets/cropped%20logo.png)](https://trustscience.com) [10130 103 Street, Floor 11  ·  Edmonton, AB Canada T5J 3N9](https://maps.app.goo.gl/59cAdoUxfEKbET3g8) 1(866) 687-8789 #### Solutions - [T°Score™](https://trustscience.com/t-score) - [CashFlow+™](https://trustscience.com/cashflow) - [Credit Bureau+™](https://trustscience.com/credit-bureau) - [Services](https://trustscience.com/services) #### Industries - [Auto Lenders](https://trustscience.com/auto-lenders) - [Installment & Single Repayment Lenders](https://trustscience.com/installment-single-repayment-models) #### Company - [About Us](https://trustscience.com/about) - [Our Team](https://trustscience.com/team) - [Press Releases](https://trustscience.com/blog/tag/press-releases) - [Careers & Culture](https://trustscience.com/careers) - [Technology & Patents](https://trustscience.com/technology-patents) #### Resources - [Marketing Channel Partners](https://trustscience.com/partners) - [News & Blog](https://trustscience.com/blog) - [Events](https://trustscience.com/events) - [Gartner Research](https://trustscience.com/resources/gartner-research-mentions) #### Support - [Client Support](https://trustscience.com/contact-us/) - [Consumer Disclosure](https://trustscience.com/legal/consumer) - [Consumer Resources](https://trustscience.com/legal/help-page) #### Legal - [Privacy Policy](https://trustscience.com/privacy-policy) - [Terms of Use](https://trustscience.com/terms-of-use) - [Data Contribution](https://trustscience.com/data-contribution-policy) - [Smart Consent™](https://trustscience.com/smart-consent) [![SOC 2 Type II — AICPA](https://trustscience.com/hubfs/2025%20Brand%20Assets/aicpa.webp)](https://trustscience.com/news/press-releases/soc2-type2) [![SAP Ariba Verified](https://trustscience.com/hubfs/2025%20Brand%20Assets/arbia.webp)](https://portal.us.bn.cloud.ariba.com/profile/public?anId=AN11173586953&sourceApplication=SBN) Trust Science® is a member of the American Financial Services Association (AFSA), the Canadian Lenders Association (CLA), the National Automotive Finance Association (NAF), The Online Lenders Alliance (OLA) and the Texas Consumer Finance Association (TCFA). Trust Science® is committed to Fair Credit Reporting Act (FCRA) compliance and helping you protect and understand your consumer profile. For more information, please see our [Consumer Disclosure Page](https://trustscience.com/legal/consumer). Trust Science®, Credit Bureau 2.0®, Credit Bureau 3.0® and Troo® are trademarks legally registered to www.TrustScience.com Inc. by the U.S. Patent & Trademark Office. Credit Bureau+™, T°Score™, Six°Score™, CashFlow+™, Smart Consent™, Hidden Prime™, Invisible Prime™, Credit Bureau 4.0™, Personal Credit Bureau™, Personal Data Vault™, Auto Six°Score™, Auto Bureau™, Auto Credit Bureau™, Rating Agency 2.0™, Cashflow Bureau™, One Touch Lending™, Lead to Loan™, Lender in the Cloud™, Flow°Builder™, FCRA-Compliant Insights From Lead to Loan™, Go Beyond the Bureau™, Fixing the Credit Catch-22™, Find Invisible Primes™, and Helping Lenders Find Great Borrowers™ are trademarks of www.TrustScience.com Inc. © 2026 Trust Science Inc. — All rights reserved. ![Trust Science Logo](https://trustscience.com/hs-fs/hubfs/2025%20Brand%20Assets/cropped%20logo.png?width=200&height=36&name=cropped%20logo.png "Trust Science Logo") - [Privacy Policy](https://trustscience.com/privacy-policy) - [Terms of Use](https://trustscience.com/terms-of-use) - [Data Contribution](https://trustscience.com/data-contribution-policy) - [Smart Consent™](https://trustscience.com/smart-consent) - **Solutions** - [T°Score™](https://trustscience.com/t-score) - [CashFlow+™](https://trustscience.com/cashflow) - [Credit Bureau+™](https://trustscience.com/credit-bureau) - [Services](https://trustscience.com/services) - **Industries** - [Auto Lenders](https://trustscience.com/auto-lenders) - [Installment & Single Payment Lenders](https://trustscience.com/installment-single-repayment-models) - **Company** - [About Us](https://trustscience.com/about) - [Our Team](https://trustscience.com/team) - [Press Releases](https://trustscience.com/blog/tag/press-releases) - [Careers & Culture](https://trustscience.com/careers) - [Technology & Patents](https://trustscience.com/technology-patents) - **Resources** - [Marketing Channel Partners](https://trustscience.com/partners) - [News & Blog](https://trustscience.com/blog) - [Events](https://trustscience.com/events) - [Gartner Research](https://trustscience.com/events) - **Support** - [Client Support](https://trustscience.atlassian.net/servicedesk/customer/portal/5/user/login?destination=portal%2F5%2Fgroup%2F13) - [Consumer Disclosure](https://trustscience.com/legal/consumer) - [Consumer Resources](https://trustscience.com/legal/help-page) 10130 103 Street, Floor 11 Edmonton, AB Canada T5J 3N9 1(866) 687-8789 [Follow us on Facebook](https://www.linkedin.com/company/trust-science/?originalSubdomain=ca) [Follow us on Twitter](https://twitter.com/trustscience?lang=ena) [![aicpa](https://trustscience.com/hs-fs/hubfs/2025%20Brand%20Assets/aicpa.webp?width=100&height=99&name=aicpa.webp "aicpa")](https://www.trustscience.com/news/press-releases/soc2-type2) [![arbia](https://trustscience.com/hs-fs/hubfs/2025%20Brand%20Assets/arbia.webp?width=180&height=55&name=arbia.webp "arbia")](https://portal.us.bn.cloud.ariba.com/profile/public?anId=AN11173586953&sourceApplication=SBN) Trust Science® is a member of the American Financial Services Association (AFSA), the Canadian Lenders Association (CLA), the National Automotive Finance Association (NAF), The Online Lenders Alliance (OLA) and the Texas Consumer Finance Association (TCFA).  Trust Science® is committed to Fair Credit Reporting Act (FCRA) compliance and helping you protect and understand your consumer profile. For more information, please see our [Consumer Disclosure Page](https://trustscience-6257140.hs-sites.com/legal/consumer). Trust Science®, Credit Bureau 2.0®, Credit Bureau 3.0® and Troo® are trademarks that are legally registered to www.TrustScience.com Inc. by the U.S. Patent & Trademark Office.  Credit Bureau+™, T°Score™, Six°Score™, CashFlow+™, Smart Consent™, Hidden Prime™, Invisible Prime™, Credit Bureau 4.0™, Personal Credit Bureau™, Personal Data Vault™, Auto Six°Score™, Auto Bureau™, Auto Credit Bureau™, Rating Agency 2.0™, Cashflow Bureau™, One Touch Lending™, Lead to Loan™, Lender in the Cloud™, Flow°Builder™, FCRA-Compliant Insights From Lead to Loan™, Go Beyond the Bureau™, Fixing the Credit Catch-22™, Find Invisible Primes™, and Helping Lenders Find Great Borrowers™ are trademarks of www.TrustScience.com Inc. © 2026 Trust Science Inc. - All rights reserved. ![](https://px.ads.linkedin.com/collect/?pid=7391644&fmt=gif)