Grouping what belongs together
Clustering and modelling are two techniques in predictive analytics that let an organisation uncover patterns, predict future trends and make informed decisions. DetectX® productises both, so the output feeds profiling, scoring and monitoring on the same core instead of sitting in a separate analytics project.
What Clustering & Modelling does
Risk rarely sits in one record. Clustering finds the group a record belongs to, and the model says what that group is likely to do next.
Clustering
Group
Groups data points into clusters based on their similarities, identifying the natural groupings within a dataset.
Customer segmentation
Segment
Understands customer behaviours and preferences by categorising them into distinct groups, which is what makes a targeted strategy or a personalised experience possible at all.
Anomaly detection
Outliers
Identifies the outliers in a dataset that may indicate conspicuous activity or a system malfunction. An outlier is only an outlier against a group, which is why this sits here rather than on its own.
Modelling
Model
Creates mathematical representations of real-world processes, then uses historical data to predict future outcomes, so a decision rests on likely scenarios rather than on the last thing that happened.
Risk and opportunity
Assess
Evaluates the potential risk of a business activity by modelling different risk factors and their impacts, and detects sales opportunities by analysing a customer's own history.
Relevance over volume
Focus
Pinpoints relevant and important data and finds the optimised variables that contribute to business outcomes, reducing the need to gather and analyse larger data sets.
Where it is used
Clustering & Modelling is one engine on the DetectX® core. These solution pages draw on it.
Solution
Groups clients by behavioural and financial similarity, which is how the risk models behind the score are calibrated across a portfolio.
Solution
Rule sets expand beyond the static, incorporating machine learning and data-driven model optimisation.
Solution
Customisable model templates tailored to each institution's own business model, so detection is specific to that organisation.
Common questions
The live pages define what clustering and modelling are and what they are used for, but they name no algorithm. This page names none either. Put the question to your solution architect, who can answer it against your data.
Six reasons are stated. Scalability, so understanding keeps pace as the data grows. Association of advanced analytics with the business rules and workflows you already run. Built-in mechanisms for continuous learning, so models adapt and evolve as new data arrives. Lower cost than developing and maintaining an analytics solution of your own. Focus, because the engine pinpoints the relevant variables instead of demanding ever larger datasets. And a centralised platform for analytics, which makes sharing an insight between team members straightforward.
No. Clustering also supports market research, where hidden patterns in consumer data inform product development and service enhancements. Modelling also supports performance optimisation, where key performance indicators are modelled to drive efficiency and productivity, and the detection of sales opportunities in a customer's historical data.
Clustering & Modelling · DetectX®
See the grouping behind the score.
Book a working session on your own portfolio. Bring your questions about how entities are grouped and what that changes downstream.
- Natural groupings
- Patterns and relationships that single-record analysis does not show.
- Continuous
- Built-in learning, so models adapt and evolve as new data becomes available.
- One core
- Feeds profiling, scoring and monitoring on the same platform.