Traditional credit rating and behavioural credit models use a handful of variables. Methods used include logistic regression, decision trees and traditional statistical analysis. Today however the explosion of data makes it possible to learn so much more about customers and how to treat them in a personalized way. Technologies such as Machine Learning facilitate this.
AdviceRobo uses machine learning (ML) and advanced analytics on structured and unstructured data and combines this with deep financial knowledge and behavioural science research.
Better scores and better predictions;
Actionable insights: scores and predictions on an individual customer level;
Continuous improvement through machine learning;
More actual, individual and accurate credit scores and significant better risk;
Modelling for default, churn, loss giving default, and bad debt.
AdviceRobo Advanced analytics solutions
Advanced data scan
From international research by Finextra and AdviceRobo we learned that 83% of lenders see machine learning as a major help to improve credit risk assessments however trust in people and data quality present the biggest hurdles in grabbing the opportunities. Curious to know what insights are hidden in your data but unsecure if your data is good enough?
AdviceRobo offers a data scan that will reveal the quality, the completeness, the richness. Part of the data scan is a RoboStorm to share the analyses and insights and explore together the opportunities for your business.
Tailor made model development
Next to the ready-to-use-models of the AdviceRobo platform we also develop models aimed at realizing your specific business goals. Whether you are looking at improving the current risk models, starting a project to learn what Machine Learning (ML) can bring to the business or wish to have your own model for a specific marketing goal, we can design this for you.
Based on our experience with and knowledge of big behavioural data, Machine Learnings, robo advice, psychographics etc. we can design any intelligent descriptive, predictive or prescriptive model.
It all starts with a investigation of your business objectives and their context.
Step 1 and 2 are driven by the business objectives and will take approximately 6 to 8 weeks.
The results, individuals scoring lists and profiles, are input for a RoboStorm during which we explore how risk and marketing management can apply the scoring lists and profiles in digital personalized proactive servicing, treatments and tools.
Next step could is to further improve the models and scores by adding (unstructured) data and implementing the servicing, treatments and tools.
To close the loop, the closely monitoring of the behaviour of customers and feed backing this in the models lead to an ever improving cycle.