Taran Decision

The ultimate competitive advantage for your business.

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.

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Do you feel that the proces of deploying predictive models and complex business decision logic in you decision management tool is complicated? Lengthy? Error-prone?
Meet the decision engine of the future.

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One - click deployment of any model


Standard decision managers

  • Usually only logistic regression models are supported
  • Predictor binning or transformations need to be manually coded into the decision manager
  • Deployment of other techniques (XGBoost, Neural Networks) is very difficult of impossible, external modules are often needed


  • Direct support for one-click deployment of any predictive model (logistic regression, gradient boosting, neural networks)
  • Model specifications, predictor bins, and transformations are saved during the modelling phase to a deployment file and are automatically processed by TaranDM

Flexible & and modular architecture


Standard decision managers

  • Usually need to receive lots of data from core banking systems Common issues: IT says that it…
  • is hard to add new data aggregations
  • will slow down the other system
  • cannot be on real-time basis


Works with a minimal set of input data, which is client identification and application form data

  • All other data, such as antifraud predictors, behavioural preditors, and blacklists are calculated and stored in separate modules
  • Each module can use the technology that is most appropriate for its task

Unified and automated testing framework


Standard decision managers

  • Frequent IT releases influence the data integrations and input data calculations used by decision manager system
  • Due to mismatched testing and production environments, bugs and issues are often found in production only once they influence performance metrics (approval rate, default rate, ...)


Any change in the decision strategy, data integration or predictor calculation is automatically tested:

  • Assessment and reporting of impact on production data
  • Integration tests for all data sources
  • Unit tests for predictors, core system, hard checks, scorecards, decision tables, expected results, etc.

Single environment (analytics & production)


Standard decision managers

  • Analytical tasks are performed on data marts in the data warehouse
  • Usually only a subset of all predictors that are used in analytics is available in the production layer
  • No access to the raw data that serves as the base for building data marts


  • Is capable of working with large input objects like XML or JSON files
  • Contains one unified predictor catalogue that serves both the analytical layer ('calculate all') and the production layer 'calculate only those needed by the current strategy')
  • All raw data are stored and any new aggregation can be coded, tested, and deployed

Instant deployment of new predictors


Standard decision managers

When a new predictor is identified by data scientists:

  • A new IT request is needed to get it to production
  • Implementation can take months
  • There is often a mismatch between implementation and specifications


  • All predictors are Python-based and can be developed by analysts or data scientists
  • An automated testing framework to validate new predictors
  • Little or no dependence on IT

Smart data source caller


Standard decision managers

  • No data source caller included
  • IT has to integrate every data source, implement caching logic, and perform upgrades to any new versions


Contains a data source caller where:

  • Data are collected in multiple iterations and cached to save costs
  • Templates allow for rapid implementation of new data sources, and the OpenAPI format is used to define protocols
  • An automated solution upgrades stored data in older formats to the latest one

Automated reporting layer


Standard decision managers

  • When a new model is deployed it has to be added to the reporting framework manually
  • Various A/B tests of hard checks, predictive models, and decision
  • rules need to be evaluated


TaranDM includes a reporting module

  • Detailed data about every iteration of the decision process are generated and used as input for reporting
  • Files with models contain all information for reporting, allowing reports to be updated automatically
  • Reporting includes evaluation of A/B tests

Open code, no vendor lock-in


Standard decision managers

Making changes, for example adding new functionality to the decision manager:

  • has to be done by the vendor
  • is often impossible
  • if it is possible, is protracted and costly


  • Built on a carefully selected technology stack with its core and main modules in Python
  • Emphasizes optimum performance, open source, and scalability
  • A licensing option allows modification of the TaranDM core or modules

Workflow management included


Standard decision managers

  • Are usually called from core IT systems in specific phases of the approval process
  • Need an orchestrating system that gathers all data for the decision-making system
  • Any change in the workflow is complicated and takes months to implement


  • Includes persistent storage of all data used in the decision-making process
  • No need for an orchestrating system, any additional data can be sent to the system alone
  • Workflow is managed by TaranDM; there is no limit on the number of iterations or need to pre-define phases

No code duplication, no copying and pasting


Standard decision managers

  • Implementation of a Champion/Challenger test requires copying and modifying the entire strategy or a relevant part thereof
  • Subsequent changes to the Champion strategy are not passed on to the Challenger strategy


Uses a template system to compile strategies:

  • Base templates for various scenarios are included
  • In Challenger, you can change only the parts that you want to change
  • Parts shared by both Champion and Challenger remain consistent

TaranDM Components

Core Scoring

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.

Data Sources Adapter

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.

Data Lake with Analytical Layer

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.

About Taran

  • Executive business consulting & advanced data analytics
  • Solution for technical excellence
  • Delivering measurable business impact
  • Clients in 20+ countries on 5 continents

Contact us

Email: info@taran.ai
Phone: +420 724 224 127 (Europe), +65 9052 1518 (Singapore)
WhatsApp: +420 607 209 443

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