TaranDM is a tool that is based on our extensive experience with credit risk management and results in simplification of the entire process, faster and more flexible development of models, lower costs, and improvement thanks to error-proofing.
Standard decision managers
TaranDM
Standard decision managers
TaranDM
Works with a minimal set of input data, which is client identification and application form data
Standard decision managers
TaranDM
Any change in the decision strategy, data integration or predictor calculation is automatically tested:
Standard decision managers
TaranDM
Standard decision managers
When a new predictor is identified by data scientists:
TaranDM
Standard decision managers
TaranDM
Contains a data source caller where:
Standard decision managers
TaranDM
TaranDM includes a reporting module
Standard decision managers
Making changes, for example adding new functionality to the decision manager:
TaranDM
Standard decision managers
TaranDM
Standard decision managers
TaranDM
Uses a template system to compile strategies:
Strategies are easy to configure and saved in human-readable JSON files. Default strategy templates for various business problems are pre-configured and re-usable. Their adjustment and customization by business owners is user-friendly with effortless maintenance.
The Data Source Adapter provides a platform for two-way communication between the Core Scoring component and any external/internal data source. it uses an OpenAPI standard interface specification and can thus handle structured and unstructured data types. The Data Source Adapter has cache functionality that enhances speed and reduces costs (for example avoiding duplicate requests to credit bureaus).
AntiFraud is a separate module that has been created to mitigate the risk of fraud in real-time on-line processes. A credit applicant's personal data, device information, fingerprint, and IP address are collected and multiple concentration and cross-checks are run to prevent fradulent applications, which often come as a series of requests from a single IP address or device. As AntiFraud is a stand-alone module, concentration and cross-checks can be built on a technology stack that is suitable for performing real-time aggregation tasks.
All inputs, outputs, and other unstructured and structured data generated during a TaranDM run are stored in a highly scalable Hadoop Data Lake. The Analytical Spark layer, which is a part of this component, enables data scientists to work in the familiar environment of Jupyter notebooks and develop new models using raw data directly from the Data Lake (as well as other predictors and components of TaranDM). Such models can easily be propagated to the Core Scoring component, including their predictors, and used in production.
The TaranDM Reporting component uses Kafka to gather structured and unstructured data from various data sources such as a Data Lake, scoring runs, decisioning, external data sources, etc. The data... Another part of the Reporting component are predefined BI tool templates (e.g. Tableau) that are used to visualize stored data. Reporting allows users to access information about decision process outcomes, KPIs, strategy comparisons, model stability and performance, benefits of external data sources, etc.
Taran
Email: info@taran.ai
Phone: +420 724 224 127 (Europe), +65 9052 1518 (Singapore)
WhatsApp: +420 607 209 443