SCORECARD SOLUTION

The OLSPS Scorecard Solution allows for the quick and easy assessment of an individual’s creditworthiness, or their credit risk. With this information, companies who provide financing, such as personal loans and vehicle finance are able to make informed decisions about who receives credit, while limiting risk.

With the power of machine learning and our industry expertise, each solution is customized to the data available and relevant to a specific industry. Data such as credit history, demographics, purchase history, etc. are used in the OLSPS Scorecard Solution to generate a credit score for each individual. Based on this score, business rules dictate whether a credit application is accepted or rejected.

This Scorecard Solution is essential to assess customers’ credit risk for any credit provider

Retail

Personal Loans

Vehicle Finance

Home Loan

Accurately assess your customer’s credibility for retail credit loans, private credit card loans, vehicle loans, and home loans with the OLSPS Scorecard Solution.

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OLSPS SCORECARD SOLUTION PROCESS

OLSPS Analytics Scorecard Solution Process - Once a model is trained, various performance measures, such as coincidence matrices, Gini coeffcients, and gains plots can be accessed in determining the accuracy and robustness of the model.

The OLSPS Scorecard Solution can be configured such that:

• Users without technical expertise in analytics, will easily be able to run new data through the solution to identify favourable credit applicants.
• The more analytical minded user can easily retrain the Scorecard Solution model as and when required.

Once a model is trained, various performance measures, such as coincidence matrices, Gini coeffcients, and gains plots can be accessed in determining the accuracy and robustness of the model. Furthermore, each factor used in the model is given a weighting that can be used to determine which factors are most important when determining credit risk.

OLSPS SCORECARD SOLUTION IMPLEMENTATION

The OLSPS Scorecard Solution requires a minimum of 3 years worth of historic data, in order to maximize both the accuracy and robustness of the model. If historical data are not available, the solution can be rolled out using alternative data sources, such as telecommunications data, value chain data, etc. The implementation and deployment timeline would very much depend on the data availability and readiness.

OLSPS Analytics Scorecard Solution Dirty Data - The OLSPS Scorecard Solution requires a minimum of 3 years worth of historic data, in order to maximize both the accuracy and robustness of the model.

CASE STUDY

P-Cubed is a leading management consulting firm specialising in delivering critical and complex programmes and portfolios for governments, agencies and major institutions across all industry sectors. P-Cubed had a requirement for an automated scorecard development process to facilitate credit-based response modelling.

P-Cubed required a bespoke scorecard to assess customer credit risk using credit history and other demographic variables as inputs. The OLSPS Scorecard was deployed for this purpose, and provided P-Cubed with a facility for the rapid automated development of scorecards alongside performance assessment utilities.