DataRobot

I use it for predictive analysis. It helps develop credit risk models to be used for various business operations s (i.e., Products, Credit, and Collections), such as cross-selling, credit limit selling, and collection strategy formulation. On the other hand, it helps perform credit risk analysis to formulate solutions and effective risk management strategies. Manpower can also be maintained while increasing productivity thanks to data robot's capabilities.

I used Data Robot to design a machine learning algorithm that profiled employee's work environment and absenteeism behaviour (as well as other market factors) to determine if they matched the profile of those employees who had left before us. We were able to use this information to understand the emerging turnover risk of our employees across our various facilities, managers, states and tenures.

The result allowed us to target our HR initiatives, provide additional training and support to staff and managers, and implement out of the box solutions to newly discovered issues resulting in turnover. It also allowed us to confirm and quantify the impact of different drivers on turnover, which in turn let us prioritise our responses. Finally, we were able to use the models to estimate the impact on turnover and costs a change initiative might cause by looking at the historical impact of initiatives run by our individual sites and/or how difference between a variable had impacted turnover previously.

Having access to data scientists and project management staff to help design, understand, train and utilize, identify use cases and design the change process was the highlight of their service.

I am using datarobot to develop Application and Behavioural Credit Scorecards for the Bank. Develop credit risk models to be used for various business operations (i.e., Products, Credit, and Collections), such as cross-selling, credit limit selling, and collection strategy formulation. Develop credit risk models to elevate lending decision-making and enhance risk management at CIMB PH.

Python IDLE

This is used for building data models for scorecard monitoring in our department, Risk Management.

Alteryx

Jupyter Notebook, Alteryx, Microsoft Excel

I normally use it for preliminary model checking in order to get an idea to estimate the accuracy or error. This step normally consumes a significant amount of time; nonetheless, it can be determined whether to proceed to the next step of the project or not. My business use cases involve process optimization, anomaly detection, and mainly time series prediction.

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