“It’s data chaos!” Finance leaders are not strangers to that. Their teams juggle data from countless sources – bank accounts spanning multiple countries, ERPs, AP platforms, treasury management system -each system often speaking its own language, using different file formats and varying levels of metadata. And the problem grows exponentially with international payments. And when payments cross borders, crucial details often get lost, making reconciliation a nightmare.
A wire transfer from the UK to the US, for example, might travel through intermediary banks in France or Cyprus, losing crucial details at each stage. By the time the transaction reaches its destination, essential data – such as counterparties or payment context – may be missing, making it difficult to trace or categorize accurately.
This ‘data disarray’ is no use to anyone, especially not finance teams who are already overstretched. Thankfully, this is where data enrichment shines.
How data enrichment works
At its core, data enrichment is about understanding and standardizing raw financial data. It goes beyond simply categorizing transactions – it links disparate data points, fills in missing details, and provides context. And by transforming raw, unstructured data into a cohesive, usable format, data enrichment provides the foundation to make smarter, faster, and more accurate financial decisions.
For instance, consider a payment labelled ‘transfer’ in a bank account. On its own, this is meaningless - is it payroll? A vendor payment? Something else entirely?
With data enrichment, this vague line item is transformed into an actionable insight. The system links it to an invoice in the ERP, identifies the counterparty in the CRM , and categorizes it appropriately in the ledger. Now finance teams can see that it is a vendor payment related to a specific contract.
Statement’s approach to data enrichment
What sets Statement.io apart is our advanced pattern recognition. Unlike traditional enrichment methods that rely on rigid rules, we leverage advanced machine learning to adapt and improve continuously. Our system doesn’t just categorize transactions it learns from past patterns to enhance accuracy over time
So, rather than hard-coding rules like ‘if a transaction contains ‘Uber,’ categorize it as transportation’ our platform analyzes patterns based on multiple characteristics – amounts, frequencies, counterparties, and timing –.to autonomously categorize transactions into meaningful clusters.
And critically, our system knows when to involve the human team. For example, when uncertain, it asks a user to validate whether a transaction belongs to a specific category. Once confirmed, that feedback is incorporated into future enrichment improving accuracy over time.
In short, it’s a system that gets smarter with every transaction.
The real-life impact of enriched data
Done right, data enrichment can significantly reduce the time spent on manual tasks, improve accuracy, and unlock opportunities for deeper analysis.
Practical applications of data enrichment include:
- Reconciliation: Enriched data makes it possible to automate reconciliation processes, reducing manual effort and errors.
- Cash forecasting: With contextually enriched data, forecasts are more dynamic and precise, enabling better financial planning.
- Risk monitoring: Anomalies become easier to spot against a backdrop of enriched, standardized data.
- Compliance: Enriched data ensures that transactions are properly categorized and traceable, making audits and regulatory reporting less painful.
Customer Scenario: Reconciliation
Imagine a finance team at a fast-growing global company managing payments across multiple bank accounts and platforms. Every month, they faced the tedious task of manually reconciling transactions, often spending hours matching payments to invoices and flagging discrepancies.
After implementing Statement’s data enrichment solution, reconciliation became a largely automated process. The system connected payments to relevant invoices, filled in missing details, and categorized them accurately. The result? A 70% reduction in processing time and significantly fewer errors, freeing up the finance team to focus on more strategic tasks.
Customer Scenario: Risk Monitoring
A multinational corporation noticed rising instances of unexpected payment anomalies, which posed a risk of fraud or accounting errors. However, spotting these anomalies in raw, unstructured data was like finding a needle in a haystack.
By adopting Statement’s data enrichment solution, the company could standardize its financial data and apply automated anomaly detection. The system flagged unusual patterns in real-time – such as an unexpected payment to a new vendor or duplicate payments – allowing the finance team to intervene quickly and reduce risk exposure.
Building a solid foundation
To unlock the full potential of data enrichment, connectivity is key. This is why we, at Statement.io prioritize connecting to all our customer’s relevant data sources, whether through APIs, SFTP, or host-to-host connections, to gather comprehensive datasets.
Once connectivity is established, their teams can focus on enrichment, creating a foundation that supports advanced analytics and AI applications. This creates a virtuous circle. With enriched data, AI systems can then deliver more reliable forecasts, better risk assessments, and actionable insights.
Your new secret weapon
While data enrichment might not grab headlines like AI, it forms the backbone of intelligent finance operations. And for organizations looking to future-proof their finance function, embracing data enrichment is often the missing link to growth, precision, and strategic excellence.
For an informal chat about how data enrichment could help your finance team, get in touch with the Statement team. Or for more insights on making the most of smart tech, read our recent blog post on AI agents.
Ready to take control of your financial data? Let’s chat! Our team is here to show you how enriched data can transform how you work and more efficiently.
