Detecting Unusual Financial Activity Automations
Organisations managing many transaction records often struggle to extract insights from unstructured reports. Manual review is slow, inconsistent, and hard to scale, especially across multiple entities and periods. This use case shows how analytics automation and AI can turn complex reports into structured, analysis-ready data. Automating extraction, structuring, and summarisation enables teams to quickly identify risks and relationships, reducing manual effort.
Where It Breaks Down
Risk analytics teams responsible for transaction analysis and risk review commonly encounter:
High manual review effort
Analysts must manually read, interpret, and summarise large volumes of reports.
Limited consistency in findings
Insights and observations vary across reviewers and reporting cycles.
Slow turnaround times
Manual processing delays downstream analysis and decision-making.
Unstructured data formats
Transaction reports are often narrative-heavy and inconsistent, limiting direct analysis.
Difficulty identifying patterns
Relationships between entities, counterparties, and transactions are hard to detect without structured data.
How We Elevate It
How ORTECH Powers This Use Case
ORTECH uses Alteryx, combined with applied AI techniques, to turn complex transaction reports into structured, analysis-ready data. Instead of manually reading and interpreting narrative-heavy documents, the process is automated to extract key details in a consistent format. This allows risk teams to focus on identifying patterns and making informed decisions rather than preparing data.
What this means in practice:
- Raw transaction reports are automatically ingested and organised.
- AI-assisted extraction converts unstructured content into structured datasets.
- Key figures and observations are summarised in a consistent format.
- Results are consolidated into analysis-ready outputs.
- Teams can review patterns and trends without manually reading every document.
Key Capabilities Delivered
Automated information extraction
Converts unstructured transaction narratives into structured data fields
Transaction aggregation and summarisation
Consolidates credits, debits, frequencies, and counterparties into consistent analytical views.
Entity and relationship profiling
Structures entity attributes and counterparty relationships for downstream analysis.
Standardised analytical outputs
Produces repeatable, review-ready datasets and summaries across reporting cycles
From Raw Data to Operational Insight
Your data inputs
- Unstructured or semistructured transaction reports.
- Transactional and reference information.
What ORTECH does
- Automates data ingestion and cleaning.
- Applies AI-assisted extraction and structuring.
- Aggregates transactions and identifies key observations.
- Produces structured analytical outputs for review.
How does it work for you
- Faster analysis of complex transaction data.
- Reduced reliance on manual report reading.
- More consistent and repeatable insights.
- Improved ability to detect patterns and relationships.
What It Delivers
Business Value & ROI
• Reduce manual effort in transaction analysis and review.
• Improve consistency and reliability of analytical outputs.
• Enable faster identification of potential risk patterns.
• Support scalable analytics as transaction volumes increase.
