A Column generation approach for product targeting optimisation within the banking industry
ORiON: Product Targeting Optimisation
AbstractProduct targeting optimisation within the financial sector is becoming increasingly complex as optimisation models are being exposed to an abundance of data-driven analytics and insights generated from a host of customer interactions, statistical and machine learning models as well as new operational, business, and channel requirements. However, given the expeditious change in the data environment, it is evident that the product targeting formulation cited throughout the literature has not yet been updated to align with the realistic modeling dynamics required by financial institutions. In this paper, an enhanced product targeting formulation is proposed that incorporates a large set of new modeling constraints and input parameters to try and maximise the economic profit generated by a financial institution. The proposed formulation ensures that the correct product is offered to the desired customers at the best time of day through their preferred communication medium. To solve the foregoing product targeting formulation, a novel column generation approach is presented that is capable of reducing problem complexity and in turn allowing for significantly larger problems to be solved to global optimality within a reasonable time frame.
Download data is not yet available.
The following license applies:
Attribution CC BY
This license lets others distribute, remix, tweak, and build upon your work, even commercially, as long as they credit you for the original creation.