The objective of this product is to find patterns of credit recoveries that maximizes the defaults to be recovered.
It is a question of analyzing the "old balances sheets" and finding, through statistical algorithms, the profiles of clients with a greater probability of credit recovery by channel.
The regulations across the world demand a risk profile questionnaire to measure the risk appetite. How can one do that? We can create products that do not exist, but that seems credible to the consumer eyes, and see how people react to them.
The advantage of this product creation is that we see how people make trade-offs. If we ask people what they want without making trade-offs, the survey will never be accurate, since people will want the most positive factors, with no cost. People always want Nirvana!!!! In real life people face trade-offs, and we need to replicate them if we want to understand their behavior.
Big data cannot do the trick either, since people are rarely face new market conditions and new products, so there is now variability. *This is why Facebook and Google and Linkedin and every social network provider conduct A&B testing - this is not a buzz word.
What we are offering (and what we are doing in the insurance sector) is A&B testing at a larger scale or at a specific context like Risk Profiling or risk appetite measure with a survey.
For pricing models is even easier - is similar to what we are doing in the insurance sector. We are can decompose the product in different features and see how people (or different client segments) react to them. Is measure the willingness to pay or willingness to a feature.
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