Use Case: Transaction Intelligence
Granular, AI-enhanced financial records under personal control.
Overview
How individuals can transform their messy, incomplete bank transaction data into rich, useful, and privacy-controlled insights — and share them securely with others on their own terms.
DataPal Transaction Intelligence transforms raw, incomplete bank transaction data into rich, structured, and user-controlled financial insights. By enabling individuals to ingest, clean, and augment their own transaction records — while maintaining full control over who can access them and for what purpose — this Use Case demonstrates how personal data can be made truly portable, usable, and privacy-preserving.
Context
Open Banking and the explosive growth of Fintech have shown that transaction data has immense value — both to the individual and to the wider digital economy. Yet in practice, this potential remains unrealised. Bank transaction feeds are often poorly formatted, incomplete, or limited to summary entries without line-item purchase details. The result is fragmented data that is theoretically powerful but practically limited.
Challenge
The current Open Banking consent and sharing model compounds these issues.
Access to a user’s data is typically “all or nothing”, often granted for fixed periods (such as 90 days). This leads to over-sharing and unnecessary exposure of personal financial information.
For example, why should someone have to share every transaction from all their accounts simply to enable one specific service — such as paying their child’s rent at university?
Solution
DataPal Transaction Intelligence addresses these challenges by shifting control to the individual.
Instead of data being pushed out indiscriminately to external apps, DataPal enables approved apps and services to “come to the data” — securely, selectively, and on the individual’s terms.
How It Works (Flow)
How it works in terms of a data flow and the actors involved:
Data Ingestion: Individuals import their transaction data via Open Banking feeds, file uploads (bank exports, receipts), or even manual entry.
Data Cleaning & Parsing: DataPal automatically standardises formats, matches related transactions, and fills in missing context.
AI-Based Augmentation: External data sources and machine learning enrich transaction records with product, merchant, and contextual metadata.
Human-in-the-Loop Review: Users can review and enhance records — adding personal notes, receipts, or clarifications.
Granular Permissioning: The user may choose to share all, some, or even just one transaction record with third parties — time-bound, purpose-specific, and revocable at any time.
Ongoing Learning: Once a transaction type has been augmented, DataPal auto-recognises and enriches future similar records.
Actors
Individual / Data Owner: Ingests, enriches, and manages their transaction data and permissions.
Organisation / Service Provider: Requests specific, purpose-limited access to verified transaction data (e.g., for lending, loyalty, or service fulfilment).
DataPal Platform: Provides ingestion, cleaning, AI augmentation, and permission management under the IEEE 7012 MyTerms model.
AI Agent / Developer: Builds value-added services — such as warranty reminders, expense automation, or price-comparison assistants — using DataPal’s APIs.
Benefits
Individuals Gain clear, usable financial records; control exactly what’s shared; unlock new value from their own data. Longer term individuals build an automated, personalised data vault that continuously learns and enriches itself over time.
Organisations / Service Providers Access accurate, consented, and purpose-specific transaction data. Longer term lower compliance risk, improved customer trust, and better service personalisation.
Developers / Ecosystem Access a composable, consent-based data platform for building innovative financial and AI services. Llonger term grow new business models based on privacy-first, customer-controlled data collaboration.
Society / Regulators Demonstrates practical privacy innovation and ethical data sharing. Longer term this encourages transparent, compliant data ecosystems aligned with public trust.
Outcomes
Up to 90% reduction in over-shared data compared with conventional Open Banking APIs.
50% improvement in transaction data quality and completeness through AI augmentation.
Increased user trust and engagement, measured by active consent renewals and sharing frequency.