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Have you ever been in a meeting or conference, nodding along as industry experts toss around terms and abbreviations that sound like a foreign language? It can feel overwhelming, especially in the financial world, where terms and slang evolves fast.
But here’s the truth: understanding the key components of transaction data enhancement is essential for businesses in the financial sector. Whether you're a neobank, a fintech startup, or a traditional bank looking to offer enhanced customer experiences, mastering the ABCs of digital banking can be a “make it or break it” situation.
So, how do you make sense of it all? Let’s break it down together, from A to Z, and explore the key terms and concepts that are must-know in the world of fintech.
APIs are the backbone of modern banking. They enable secure communication between software systems, allowing banks, fintech companies, and third-party developers to share data and functionalities. In the context of payment data, APIs are essential for transaction enrichment, enabling systems to access raw transaction data and enhance it with meaningful insights.
For example, Tapix offers a seamless cloud-based REST API that provides banks and fintechs with transaction data enrichment, enabling them to build their own solutions on top of this data.
Behavioral analytics focuses on understanding customer actions and preferences through data. By analysing payment patterns - such as how often a customer shops online versus in-store or their frequency of recurring purchases - financial institutions can tailor marketing efforts, recommend personalised products, and predict future needs.
For example, Netflix uses behavioral analytics to recommend personalised content, showcasing how data insights improve user engagement. Banks can use it to predict their client’s needs and offer appropriate services like cashback, or avoid potential trouble on the way.
Categorisation transforms raw payment data into actionable insights and is absolutely vital for a precise payment enhancement. Transactions are sorted into meaningful groups, such as dining, utilities, or travel, based on predefined rules and machine learning models.
For customers, this means clear and easy-to-understand financial statements. Banking apps like Revolut or bunq categorise transactions automatically, helping user’s budget and manage their financial life effectively.
Data quality is critical for accurate decision-making in fintech. Without clean, consistent, and complete data, any analysis or enrichment efforts will lead to flawed outcomes since the quality of the output directly correlates to data quality banks put in.
To learn more about data quality, check out our deep dive into this matter!
Embedded finance refers to the integration of financial services into non-financial platforms or apps. This allows businesses to offer banking-like features directly within their ecosystem, such as payments, lending, or insurance. For example, ride-sharing apps like Uber embed payment options and driver loan programs into their platform, streamlining transactions and enhancing user experience. Many digital banks like Revolut started with embedded finance before getting their own licences.
Embedded finance is reshaping digital banking by enabling seamless, context-driven financial interactions.
For this particular case, enhanced payment data is a game-changer with fraud detection being only the tip of the iceberg. By providing more context about each transaction, such as merchant type, location, and frequency, fraud detection algorithms can more accurately identify anomalies.
Green banking refers to financial practices that prioritise environmental sustainability by supporting eco-friendly initiatives and reducing the carbon footprint of banking operations. This approach includes a range of strategies aimed at promoting environmental responsibility within the financial sector, such as carbon footprint tracking, automatic carbon offsetting, eco-friendly payment cards, green loans and mortgages or sustainable investment options.
Hyper-personalisation in banking involves leveraging advanced data analytics and artificial intelligence to deliver highly tailored products, services, and experiences to individual customers. This approach goes beyond traditional segmentation and MCCs by utilising real-time data to meet the unique needs and preferences of each customer. This also requires comprehensive data utilisation, real-time analytics and machine learning integration that can predict customer behaviors and preferences, enabling proactive engagement and offerings.
Input Data refers to the raw transactional information that financial institutions collect from various sources. This data serves as the foundation for generating meaningful insights through enrichment processes. The quality and comprehensiveness of input data are crucial, as they directly influence the accuracy and value of the information. Common sources of input data include card transactions and ATM withdrawals, bank transfers or QR payments.
Curious to understand how it works? Learn more at Tapix!
JSON is a lightweight data format widely used for exchanging data between systems. In fintech, JSON is the preferred format for APIs delivering enriched payment data due to its simplicity and compatibility.
To learn more about all things data, head to our developers portal for a comprehensive read.
Know Your Customer is a fundamental regulatory and compliance process used by financial institutions, fintech companies, and businesses to verify the identity of their customers. The primary goal of KYC is to prevent financial crimes such as fraud, money laundering, and identity theft. Enhanced payment data can bring additional layer into this process by providing deeper insights into customer behavior, such as spending patterns and transactional anomalies.
Labeling involves tagging transactions with specific attributes, such as "business expense," "personal spending," or "recurring payment." This helps customers better understand their financial habits and enables businesses to analyse payment data with greater granularity. Enhanced data is also used in many PFM tools (personal finance management) in digital banking apps from Revolut, Raiffeisen or bunq.
Accurate merchant names are essential for providing clarity in digital payment records. They ensure that customers can easily recognise and understand their transactions, reducing confusion and enhancing trust in financial services. Instead of displaying a vague description like "PAYPAL *ABOUTYOUSEC," an accurate merchant name would present it as "ABOUT YOU," clearly identifying the retailer.
Accurate name helps in many cases, like complying with Mastercard's AN 4569 Revised Standards for the Display of Enhanced Merchant Data.
Normalisation ensures that payment data from different sources is standardised for consistency, accuracy, and usability. It harmonises diverse terminologies, formats, and categories, making the data uniform and reliable for analysis. For instance, a transaction labelled as "grocery" in one dataset and "supermarket" in another would be normalised to a single category, such as "Groceries." This process is critical for financial institutions and fintech companies handling vast amounts of transaction data sourced from multiple payment gateways, banks, and processors.
Open banking enables third-party providers to access financial data through secure APIs, with user consent. It is a regulatory-driven initiative aimed at promoting transparency, competition, and innovation in the financial services sector. By allowing customers to share their banking data with authorised third parties, open banking gives users the ability to benefit from tailored financial solutions in the process. Moreover, open banking lays the groundwork for innovative features like subscription management.
Payment processors handle the technical aspects of transactions, such as authorisation, clearing, and settlement, acting as the intermediary between merchants, customers, and banks. They ensure that funds are transferred securely and efficiently from a customer's account to a merchant's account. Examples of leading payment processors include Stripe, PayPal, and Adyen. Enriching data from payment processors provides deeper insights into spending behaviors and merchant performance, especially helping with merchant hidden behind payment gateways.
Queryable data is structured and organised to facilitate efficient searching, filtering, and analysis. In the context of large datasets like payment records, queryable data ensures that financial institutions can quickly extract insights and make data-driven decisions without manual intervention or delays. For example, SQL databases and big data platforms like Snowflake and Elasticsearch enable complex queries on massive datasets in real-time. This capability is essential for various use cases, such as to segment customers based on their spending habits, identify trends across geographic regions, and track performance metrics.
Recurring Payments are automatic transactions that occur at regular intervals, enabling customers to authorize businesses to withdraw funds from their accounts on a predetermined schedule. This model is fundamental in subscription-based services, utilities, and other sectors requiring consistent payment collection. Digital banking platforms have integrated recurring payment features to enhance user experience.
For recurring payments to work perfectly, proper subscription labelling through enhanced transaction data is vital.
Segmentation divides data into meaningful groups based on various criteria such as demographics, spending habits, transaction frequency, geographic location, and even lifestyle preferences. This analytical approach transforms raw data into actionable insights, empowering businesses to better understand their customer base and meet their specific needs. By analysing payment data, banks can identify underbanked populations and create products suited to their financial behaviors, such as micro-loans or no-frills accounts.
Transaction data enrichment transforms raw, often messy transaction data into actionable insights by adding contextual information such as merchant names, business categories, geo-locations, and transaction descriptions. This process allows financial institutions to better understand customer behavior, streamline operations, and offer tailored services that drive engagement and satisfaction. By analysing data, banks can identify spending trends, predict customer needs, and design products that cater to specific behaviors - such as travel-focused credit cards for frequent flyers or savings recommendations for high-spending categories.
Tapix specialises in advanced transaction data enrichment services, offering APIs that seamlessly integrate into banking systems within many layers, such as adding complete merchant details, enhancing data with geo-location information, assigning accurate categories or adding features like ATM locator or carbot footprint tracker.
A URL address in the context of payment data refers to the web address associated with a transaction, often used to provide additional details about a merchant or service provider. URL enrichment helps with matching payment transactions with accurate and verified merchant websites. By ensuring that each payment entry contains a relevant web address, this data helps financial institutions improve clarity, reduce customer confusion, and enhance fraud detection efforts.
Visualisation tools have an important role in transforming complex payment data into easily digestible formats, such as interactive dashboards, charts, and graphs. These tools enable decision-makers to identify trends, monitor performance, and devise effective strategies with confidence. Banking apps like Revolut or bunq use visualization to help users understand their spending patterns, investment performance, and savings progress. Features such as pie charts for expense categorisation make financial data intuitive and engaging.
Withdrawal fees are charges imposed by financial institutions when customers withdraw cash from ATMs, bank branches, or other cash access points. These fees can vary based on factors such as the withdrawal method, transaction amount, and whether the ATM or bank is within the customer’s network.
How to make a withdrawal experience smoother? Learn more about ATM Nearby!
While JSON dominates modern APIs due to its lightweight nature and ease of use, XML (eXtensible Markup Language) remains a vital tool in legacy systems and specific industries requiring robust and highly structured data formats. XML is widely used in payment data systems to ensure compatibility with older platforms while maintaining a machine-readable structure that supports complex data hierarchies. In payment processing, XML plays a key role in facilitating transactions across systems that have yet to migrate to modern APIs.
A yearly spending overview is a financial summary that provides users with a comprehensive breakdown of their spending habits over the past year. This feature, commonly found in digital banking apps like Monzo, and personal finance tools, helps individuals and businesses analyse their financial behavior, optimise budgets, and plan for future expenses.
Zero-day detection uses enhanced payment data and advanced analytics to identify vulnerabilities, risks, or threats as soon as they emerge, providing financial institutions with a proactive defense mechanism. By analysing transactional patterns, identifying anomalies, and correlating data points, zero-day detection systems can pinpoint issues that might otherwise go unnoticed until significant damage occurs. It also ensures compliance with regulatory frameworks like PSD2 and anti-money laundering (AML) guidelines.
For more details on how enhanced transaction data can benefit your bank, explore the Tapix offerings.
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Michal Maliarov
Senior insider
