Smart Action Recommendations Using Data
Operational teams face challenges in decision-making across many cases due to manual reviews and disconnected analyses, leading to inconsistent decisions and slow action. This use case offers a unified framework that blends rule-based analytics with machine learning to enable consistent prioritization, explainable recommendations, and adaptable strategies. Built with Alteryx, it ensures repeatability, governance, and scalability
Where It Breaks Down
Risk and compliance operations teams responsible for prioritisation and follow-up commonly face:
Reactive follow-up
Actions are triggered after issues surface, rather than proactively guided by data.
Low transparency
Stakeholders struggle to understand why certain actions were recommended.
Scalability constraints
Manual review processes do not scale as data volumes increase.
Inconsistent decision-making
Similar cases are treated differently due to manual judgment or fragmented rules.
Limited prioritisation clarity
Large volumes make it difficult to identify which cases require immediate attention.
How We Elevate It
How ORTECH Powers This Use Case
ORTECH delivered an analytics-driven framework using Alteryx that supports three complementary applications under a single solution family:
- Rule-based decision analytics for deterministic action selection.
- Predictive machine learning models for behavioural scoring
- Operationalised outputs that feed prioritisation and follow-up workflows.
The framework automates data preparation, applies either rules or trained models as appropriate, and produces structured outputs that are refreshed regularly. This enables teams to move from ad-hoc decisions to consistent, explainable, and scalable action recommendations.
FRAMEWORK APPLICATION
A. Rule-Based Action Prioritisation
Applies deterministic, rule-driven logic to evaluate case attributes and prior outcomes, assigning each case to a predefined action pathway. Designed for scenarios where transparency, consistency, and explainability are critical.
B. Predictive Outcome Likelihood Scoring
Uses supervised machine learning to generate a predictive score representing the likelihood of fulfilling payment obligations. Feature engineering spans financial and behavioural indicators, with periodic rescoring to reflect the latest data.
C. Predictive Behavioural Risk Scoring
Applies predictive modelling to
assess expected compliance
behaviour, producing relative risk
classifications that support
differentiated follow-up strategies
and prioritisation.
Key Capabilities Delivered
Rule-based action recommendation
Deterministic decision logic for transparent and consistent prioritisation.
Predictive scoring models
Machine learning–driven scores to differentiate behaviour and risk levels.
Automated data preparation & execution
Repeatable workflows for recurring scoring and decision runs.
Explainable outputs
Structured results that support review, justification, and operational use.
From Raw Data to Operational Insight
Your data inputs
- Transactional and behavioural records.
- Historical outcomes and reference datasets.
What ORTECH does
- Automates data preparation and feature engineering.
- Applies rules or trained models based on use-case needs.
- Generates prioritised action lists and predictive scores
- Refreshes outputs on a recurring schedule.
How does it work for you
- Clear prioritisation of what to act on next.
- Consistent and defensible decision-making.
- Earlier identification of higherrisk or higher-impact cases
- Reduced manual review effort at scale.
What It Delivers
Business Value & ROI
• Improve consistency and transparency in operational decision-making.
• Enable proactive prioritisation using predictive insights.
• Reduce manual effort in large-volume case review.
• Support scalable operations as data volume and complexity grow
