1 hr 12 min
27. Stephan Stricker & Maxime Kaniewicz - Pair Finance : Reinforcement Learning and Targeted Marketing in debt collection Austrian Artificial Intelligence Podcast
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- Technology
# Summary
Have you every had troubles paying your bills and got some nasty calls or letters about it? Debt collection is surely one area where I would not have thought to find AI, but today on the show, I am talking to Stephan Stricker Founder and CEO of Pair Finance and Maxime Kaniewicz, Data Science Team lead.
On how they combine the insights and methods from targeted marketing with reinforcement learning, to nudge customers towards paying their bills.
I think this episode is of great value to anyone who is thinking of building reinforcement learning systems for real business cases.
We speak about many of the main challenges in reinforcement learning, like how to collect intermediate rewards that match the business objects without running into the alignment problem. Or how to evaluate and compare different agents and policies without loosing revenue and cause damage to the business. We discuss the necessity of historical training data and the continuous flow of new training data in order to improve and optimize the system. We hear about ways to overcome the cold start problem by helping the agent to expand into new environments by providing new actions in combination with new priors and experiences.
I hope you will like this episode, and I can ensure you that there is a lot to learn.
# References
https://www.pairfinance.com/
https://www.linkedin.com/in/stephanstricker/- Stephan Stricker - Founder and CEO of Pair Finance
https://www.linkedin.com/in/maxime-kaniewicz/- Maxime Kaniewicz - Data Science Team Lead
# Summary
Have you every had troubles paying your bills and got some nasty calls or letters about it? Debt collection is surely one area where I would not have thought to find AI, but today on the show, I am talking to Stephan Stricker Founder and CEO of Pair Finance and Maxime Kaniewicz, Data Science Team lead.
On how they combine the insights and methods from targeted marketing with reinforcement learning, to nudge customers towards paying their bills.
I think this episode is of great value to anyone who is thinking of building reinforcement learning systems for real business cases.
We speak about many of the main challenges in reinforcement learning, like how to collect intermediate rewards that match the business objects without running into the alignment problem. Or how to evaluate and compare different agents and policies without loosing revenue and cause damage to the business. We discuss the necessity of historical training data and the continuous flow of new training data in order to improve and optimize the system. We hear about ways to overcome the cold start problem by helping the agent to expand into new environments by providing new actions in combination with new priors and experiences.
I hope you will like this episode, and I can ensure you that there is a lot to learn.
# References
https://www.pairfinance.com/
https://www.linkedin.com/in/stephanstricker/- Stephan Stricker - Founder and CEO of Pair Finance
https://www.linkedin.com/in/maxime-kaniewicz/- Maxime Kaniewicz - Data Science Team Lead
1 hr 12 min