    TRAC represents a new, modern approach to managing structural analytics.. but what is structural analytics?

Structural Analytics in Banking

We specialise in Banking applications but TRAC is use case agnostic and structural analytics is a challenge in many industries, including Insurance, Healthcare, Pharma, Telecom's and many others. Contact us if you would like to discuss a non-FS application of TRAC.

DISCUSS A USE CASE

Defining Structural Analytics

EXPLORATORY ANALYTICS

Identifying patterns, trends, and relationships in data to inform decisions and build hypotheses. Open-ended and iterative, using techniques like data mining, clustering, and visualisation.

PRIORITIES: Data interactivity, speed of prototyping and visualisation.

GENERATIVE ANALYTICS

Creating new data and content using AI/ML algorithms, where the system automatically adapts and adjusts as new data is introduced.

PRIORITIES: Scalability and the ability to automate pipelines and model training.

STRUCTURAL ANALYTICS

Applying complex, semi-rigid operations to generate data outputs that have predetermined business definitions and acceptance criteria.

PRIORITIES: Lineage, auditability and explainability.

Common Challenges With Structural Analytics

COMPLEX WORKFLOWS

Calculations built from several independently specified elements which require multiple sources, levels and types of data input.

COMPLEX ANALYSIS

Need to explore the drivers and sensitivities of final outputs and model performance, possibly both in aggregate and on sub-populations .

COMPLEX GOVERNANCE

Multiple control frameworks to satisfty, such as model governance, internal audit, financial control and data lineage reporting.

Take a Tour

See how TRAC's unique design solves all three of the challenges associated with structural analytics.