In an increasingly digital landscape, financial data is everywhere, but much of it is messy, incomplete, and hard to make sense of. From cryptic payment descriptions to scattered account records, raw financial data often falls short when it comes to clarity and value. That’s where financial data enrichment comes in.
Financial data enrichment transforms unstructured data into clear, structured, and actionable insights, helping businesses improve reporting, streamline operations, and deliver a better customer experience.
In this article, we’ll explore financial data enrichment, how it works, and why it’s so important. Topics covered include:
What is data enrichment?
Data enrichment is the process of enhancing raw data by adding additional context, attributes, or information from internal sources or third-party data from external sources. It transforms incomplete, ambiguous, or isolated data points into meaningful, connected information, making the data more valuable, insightful, and actionable.
In financial services, data enrichment means taking basic financial data, like a transaction record, and adding layers of information such as merchant names, locations, or customer behaviour insights to make the data useful for analysis, compliance, and customer experience.
It’s used by:
- Banks and digital banking apps for transaction categorisation and spending insights
- Fintech apps for personal finance management tools
- Payment processors for better reporting and risk management
- Corporate treasurers for cleaner financial records and reconciliation
- Regulators and compliance teams for AML monitoring and reporting
How does financial data enrichment work?
Financial data enrichment uses a combination of data sources, rules engines, machine learning models, and APIs to clean, classify, and add meaning to financial transactions and records.
Here’s a breakdown of how it works:
- Data ingestion: Financial data is collected from various sources, such as bank feeds, payment gateways, transaction logs, or open banking APIs. This data often arrives in inconsistent, cryptic formats, full of vague merchant codes, inconsistent descriptions, or missing fields.
- Data cleansing and standardisation: The raw data is cleaned to remove duplicates, correct errors, and standardise date formats, currencies, and fields.
- Merchant identification and categorisation: One of the most important parts of enrichment is turning messy transaction details into clear, recognisable records. Specialised enrichment engines or APIs match transaction strings against a global merchant database or use pattern recognition to identify the correct merchant or company name, classify the transaction into a standard category, and add location data where available.
- Data enrichment and contextualisation: Additional information is layered onto each transaction or data point, such as clean merchant name and logo, business category or sector, transaction type, currency conversion, or subscription identification. This adds valuable context that raw data lacks.
- Insights and analytics: Once enriched, the structured data is fed into analytics dashboards, reporting tools, and financial apps. It enables customer financial insights, transaction-level analytics for businesses, automated reconciliation and cash flow forecasting, and fraud detection and AML pattern analysis.
Types of financial data enrichment
There are many types of financial data enrichment. Here are some of the most common categories:
Financial data enrichment
The broadest category, financial data enrichment, enhances financial records with additional details like:
- Transaction categorisation (e.g., groceries, rent, utilities)
- Geolocation of transactions
- Merchant name standardisation
- Currency conversion rates
- Customer demographic overlays (age, location, risk score)
- Creditworthiness indicators
This ensures better financial reporting, customer analytics, fraud detection, and hyper-personalised financial services (like spending insights or financial health scores).
Customer data enrichment
Customer data enrichment enhances customer records with additional information (from internal or external sources) such as:
- Contact details (email, phone, address updates)
- Demographic info (age, gender, marital status)
- Behavioural data (app usage, website visits, preferences)
- Social media presence or sentiment
- Employment details
- Geo-location info
- Household composition
This improves customer records for better communication, targeting, and relationship management.
Payment data enrichment
This type of data enrichment focuses specifically on payments data, enhancing each payment record by adding:
- Merchant name, address, website
- MCC (Merchant Category Code) standardisation and categorisation
- Device or payment channel info (POS, eCommerce, mobile)
- Recurring payment identification
- Currency and cross-border transaction details
Payment data enrichment improves payment reconciliation, reporting accuracy, fraud monitoring, customer dispute resolution, and customer-facing transaction descriptions in apps.
Transaction data enrichment
Transaction data enrichment takes individual financial transactions and enriches them with context:
- Clean merchant names from messy payment descriptors
- Categorisation by transaction type (e.g. ATM withdrawal, bill payment, online purchase)
- Spend location (city, country)
- Recurrence detection (subscriptions)
- Linking related transactions (e.g. refunds to original purchases)
This enhances financial management tools, makes transaction histories clear for customers, improves loyalty and rewards programme targeting, and enables better AML and risk monitoring.
Banking data enrichment
Finally, banking data enrichment enhances broader banking data beyond transactions, including:
- Account holder demographics and behavioral attributes
- Financial product usage insights (credit cards, loans, savings)
- Aggregation of open banking data across multiple accounts and banks
- Enhanced risk scoring and affordability assessments
- Enriched account statements for business banking clients (e.g. categorising corporate expenses)
This supports smarter credit risk assessment, personalised financial products, open banking integrations, and better Know Your Customer (KYC) and anti-money laundering (AML) compliance.
Why is data enrichment important in financial services?
Raw financial data is often messy, incomplete, or poorly structured. Payment transactions might contain cryptic merchant codes or incomplete descriptions, and bank transactions could show up as a string of numbers or indecipherable merchant IDs.
Without financial data enrichment, businesses struggle with everything from inefficient reconciliation processes and a poor customer experience to compliance risks due to incomplete or unstructured data.
However, with financial data enrichment, businesses can turn fragmented, ambiguous financial records into structured, reliable, and actionable insights.
Benefits of financial data enrichment
Financial data enrichment comes with a range of benefits:
- Clearer transaction histories for customers: Data enrichment translates vague transaction descriptions into clear, recognisable, and categorised records, reducing customer confusion and complaint volumes.
- Improved financial reporting and analytics: Accurate, categorised, and enriched transaction data improves internal reporting and performance tracking for banks, fintechs, and corporates, enabling better revenue attribution, spend analysis, customer segmentation, and trend and anomaly detection.
- Enhanced fraud detection and AML compliance: Financial crime detection relies heavily on context. Enriched data provides the additional context needed to flag high-risk patterns.
- More personalised financial products and advice: When financial institutions know not just how much customers spend, but where, when, and on what, they can deliver tailored services to increase customer engagement and loyalty.
- Simplified reconciliation for finance teams: In corporate finance and payment processing, reconciliation is time-consuming when transaction records lack detail or consistency. Data enrichment reduces manual reconciliation, accelerates month-end close processes, and minimises human errors in accounting and reporting.
- Greater data-driven decision-making for banks and fintechs: Clean, enriched, and categorised data fuels more sophisticated analytics, forecasting, and business strategy, driving operational efficiency and risk mitigation and supporting regulatory reporting and financial planning.
In essence, financial data enrichment transforms raw financial data into a valuable asset for operational efficiency, customer experience, and data-driven decision-making.
Open banking and data enrichment explained
Data enrichment technologies, such as account information services (AIS), are at the heart of the open banking revolution. They allow third parties to use APIs to access customer account data, enabling a range of new and exciting services, including account information services (AIS).
Authorised AISPs (account information service providers) use API technology to access a customer’s bank account and view account information. This service is particularly useful in customer onboarding and verification. At Brite we call this part of our open banking products Data Solutions.
When financial institutions combine data enrichment with payment data at the onboarding stage, they gain richer, real-time insights into a customer’s financial behaviour and identity. This creates a faster, smoother, and more secure onboarding experience while strengthening compliance and fraud controls.
Here’s how:
- Faster, more accurate identity verification: Enriched payment data speeds up identity checks, reduces onboarding friction, and prevents fraudulent sign-ups.
- Enhanced KYC risk profiling: Data enrichment improves risk-based onboarding, ensuring high-risk customers undergo enhanced due diligence without slowing down low-risk applicants.
- Improved fraud detection from day one: Enriched payment data during onboarding can instantly highlight suspicious indicators, such as payments from blacklisted accounts or transactions originating from flagged countries or regions.
- Smoother customer experience: With enriched data, banks and fintechs can automate much of the onboarding process, reducing drop-off rates and improving conversion in digital onboarding journeys.
- Personalised financial services: Enriched payment insights captured from the start allow businesses to tailor their services based on transaction behaviour, improving customer engagement, loyalty, and lifetime value.
To summarise, combining payment data enrichment with customer onboarding and KYC turns a basic identity check into an opportunity to gain richer insights, enhance compliance, and deliver a faster, smarter customer experience.
For an idea of what that looks like in practice, take Suicide Zero: a Swedish non-profit organisation dedicated to reducing the number of suicides in Sweden.
Faced with the challenge of ensuring a continuous and reliable stream of donations, when signing up as a monthly donor was cumbersome, Suicide Zero implemented AIS through Brite Data Solutions.
Brite’s Data Solutions product automates the collection of address and account information via BankID, significantly reducing the number of form fields required and eliminating manual entry errors. This integration has made the donation process more user-friendly and efficient, reducing dropouts and administrative workload.
Learn more about Data Solutions and data enrichment
Data enrichment and open banking go hand in hand, fitting like a glove, with many European companies benefiting from new and exciting payment services. From car charging payments, charity donations, and insightful consumer lending, businesses can now offer new and exciting payment solutions for consumers like never before.
Contact one of our in-house payment experts if you want to learn more about how open banking and data enrichment technologies like AIS can improve your payment infrastructure.
