--- title: Finding Age in FS Data description: Explore how age attributes appear in data and learn strategies to ensure algorithmic fairness in financial services. image: https://riskinsights.com.au/hubfs/Growth%20rings%20and%20cracks%20in%20weathered%20wooden%20post.jpeg --- [Skip to content](https://riskinsights.com.au/blog/finding-age-in-data#main-content) [![Risk Insights - Logo - (RGB, Horizontal, Small)](https://riskinsights.com.au/hs-fs/hubfs/Risk%20Insights%20-%20Logo%20-%20(RGB%2c%20Horizontal%2c%20Small).png?width=200&height=40&name=Risk%20Insights%20-%20Logo%20-%20(RGB%2c%20Horizontal%2c%20Small).png)](https://riskinsights.com.au) - [Articles](https://riskinsights.com.au/blog) - [Approach](https://riskinsights.com.au/approach) Show submenu for Approach - [Commissions (Accuracy)](https://riskinsights.com.au/broker-partner-commission-accuracy-reviews) - [Claims Fraud (Fairness)](https://riskinsights.com.au/claims-fraud-fairness-reviews) - [Fees and Charges (Accuracy)](https://riskinsights.com.au/fee-and-charge-accuracy-reviews-risk-insights) - [About](https://riskinsights.com.au/about) - [Contact](https://riskinsights.com.au/contact) Open main navigation Close main navigation - [Articles](https://riskinsights.com.au/blog) - [Approach](https://riskinsights.com.au/approach) Show submenu for Approach - [Commissions (Accuracy)](https://riskinsights.com.au/broker-partner-commission-accuracy-reviews) - [Claims Fraud (Fairness)](https://riskinsights.com.au/claims-fraud-fairness-reviews) - [Fees and Charges (Accuracy)](https://riskinsights.com.au/fee-and-charge-accuracy-reviews-risk-insights) - [About](https://riskinsights.com.au/about) - [Contact](https://riskinsights.com.au/contact) --- 17 Jul 2025 # Finding Age in FS Data ![Picture of Yusuf Moolla](https://riskinsights.com.au/hs-fs/hubfs/Yusuf_Moolla_Headshot-transformed.png?width=50&name=Yusuf_Moolla_Headshot-transformed.png) [Yusuf Moolla](https://riskinsights.com.au/blog/author/yusuf) Share: [linkedin-in icon](http://www.linkedin.com/shareArticle?mini=true&url=https://riskinsights.com.au/blog/finding-age-in-data) [envelope icon](mailto:?body=https://riskinsights.com.au/blog/finding-age-in-data) **TL;DR** • Unlike other discrimination categories, Age has/is a single attribute. • It's often visible in data, but can also be inferred or hidden. • Age can be derived in different ways, sometimes inconsistently.   This is the fifth article in a series about [finding protected attributes in data](https://riskinsights.com.au/blog/finding-protected-attributes-in-data). We need to manage 4 key discrimination categories in ensuring that our algorithms are fair. We previously explored [Race](https://riskinsights.com.au/blog/finding-race-in-data), [Sex/Gender](https://riskinsights.com.au/blog/finding-sex-gender-in-data), and Disability.  Another key category is Age. Unlike the other categories, age is a single attribute. This article explores where age might appear in our data and what we can do about it.   ## Age in Data Legislation in many jurisdictions make it unlawful to discriminate against a person because of their age, unless an exception applies (such as age-based eligibility for certain products or services). Age is as simple as it sounds. It refers to how old a person is. Algorithmic systems can discriminate by age even if they never see the age field directly. For example, using proxies like years since graduation. So, even though age is a simple concept, it can show up in data in many ways: ### **Directly captured** Age is often collected as date of birth on forms, applications, identity documents or driver’s licenses. This is straightforward, and most banking and insurance systems will hold this data. For both existing customers and prospective customers, we often need this for KYC purposes. ### **Inferred** Age can be estimated, or age buckets can be inferred, from other sources. For example: - school or university graduation dates - product eligibility (e.g., “over 55s” accounts or "youth accounts") - life events (e.g., retirement or first home purchases) - the type of driver's license held (e.g., "provisional" driver's licenses often correlate with a certain age bucket). ### **Derived** Systems can calculate age from other data, e.g., subtracting year of birth from the current year. Again, this is straightforward and fairly common. But it can introduce inconsistencies, for instance where data is missing. This can affect algorithms: how we handle the anomalies can affect decisions.   ## What We Can Do Age is often visible, but sometimes it’s inferred or hidden. In practice, we can inspect our algorithmic systems, and: - **Avoid inferring age from indirect data**, such as transaction history or product choices, unless there’s clear (legal and ethical) business rationale. - **Question assumptions built into structured data**, such as age-based eligibility or marketing segments. - **Check third-party data for age-related predictions**, especially where demographic data is used. - **Monitor for patterns that might disadvantage people based on age**, such as higher insurance premiums or reduced access to services. --- Disclaimer: The information in this article does not constitute legal advice. It may not be relevant to your circumstances. It was written for specific algorithmic contexts within banks and insurance companies, may not apply to other contexts, and may not be relevant to other types of organisations. ---   ![Weekly Articles                                                                                             Get weekly emails in your inbox.  ](https://hubspot-no-cache-ap1-prod.s3.amazonaws.com/cta/default/44032070/interactive-182785203045.png) ## Related posts [![Lego people of different types - Photo by Jason Leung on Unsplash](https://riskinsights.com.au/hs-fs/hubfs/Lego_people%20(Medium).jpg?height=200&name=Lego_people%20(Medium).jpg)](https://riskinsights.com.au/blog/finding-sex-gender-in-data) ## [Finding Sex/Gender in FS Data](https://riskinsights.com.au/blog/finding-sex-gender-in-data) 03 Jul 2025 TL;DR • Sex has eight key attributes, some direct in data, but mostly inferred. • Many attributes... [Read more](https://riskinsights.com.au/blog/finding-sex-gender-in-data) [![Finding National Origin in FS Data](https://riskinsights.com.au/hs-fs/hubfs/Finding%20National%20Origin%20in%20FS%20Data.png?height=200&name=Finding%20National%20Origin%20in%20FS%20Data.png)](https://riskinsights.com.au/blog/finding-national-origin-in-fs-data) ## [Finding National Origin in FS Data](https://riskinsights.com.au/blog/finding-national-origin-in-fs-data) 02 Jul 2026 TL;DR • National origin is protected in many places. • Some of it is inferred via proxies that... [Read more](https://riskinsights.com.au/blog/finding-national-origin-in-fs-data) [![Review triggers and practical checks for algorithmic systems (Part 3)](https://riskinsights.com.au/hs-fs/hubfs/Review%20triggers%20and%20practical%20checks%20for%20algorithmic%20systems%20Part%204.png?height=200&name=Review%20triggers%20and%20practical%20checks%20for%20algorithmic%20systems%20Part%204.png)](https://riskinsights.com.au/blog/review-triggers-and-practical-checks-for-algorithmic-systems-part-4) ## [Review triggers and practical checks for algorithmic systems (Part 4)](https://riskinsights.com.au/blog/review-triggers-and-practical-checks-for-algorithmic-systems-part-4) 23 Apr 2026 TL;DR • Part 4 of a 5-part series. • Part 1 explained triggers for testing and deep dives: new... [Read more](https://riskinsights.com.au/blog/review-triggers-and-practical-checks-for-algorithmic-systems-part-4) We acknowledge Aboriginal and Torres Strait Islander peoples as the Traditional Custodians of the lands on which we live and work. We pay our respects to Elders past and present, and honour the rich diversity of the world's oldest living cultures. [View our LinkedIn page](https://www.linkedin.com/company/riskinsightsap) [email us](mailto:info@riskinsights.com.au) [Call us](tel:+61408946143) 2026 Risk Insights Pty. Ltd. | [Privacy](https://riskinsights.com.au/privacy-policy) | [Disclaimer](https://riskinsights.com.au/disclaimer) ```json { "@context" : "https://schema.org", "@type" : "BlogPosting", "author" : { "@type" : "Person", "name" : "Yusuf Moolla", "url" : "https://riskinsights.com.au/blog/author/yusuf" }, "dateModified" : "2025-07-16T20:30:00.782Z", "datePublished" : "2025-07-16T20:30:00.000Z", "headline" : "Finding Age in FS Data", "image" : [ "https://riskinsights.com.au/hubfs/Growth%20rings%20and%20cracks%20in%20weathered%20wooden%20post.jpeg" ], "mainEntityOfPage" : { "@id" : "https://riskinsights.com.au/blog/finding-age-in-data", "@type" : "WebPage" }, "publisher" : { "@type" : "Organization", "logo" : { "@type" : "ImageObject", "url" : "https://riskinsights.com.au/hubfs/Risk%20Insights%20-%20Logo%20-%20(RGB%2c%20Horizontal%2c%20Small).png" }, "name" : "Risk Insights Pty. Ltd." } } ```