DyCOD - Determining Cash on Delivery Limits for Real-Time E-Commerce Transactions via Constrained Optimisation Modelling

Abstract

Paying for deliveries using cash after the delivery is made is a popular mode of payment employed by customers transacting online for the first time or those that prefer to have more control, especially in emerging economies like India. While the cash (or pay)-on-delivery (COD or POD) option helps e-commerce platforms, for example in our food delivery platform, tap into new customers, it also opens up substantial risk in the form of fraud and abuse. A common risk mitigation strategy is to impose a limit on the order value that can be paid using COD. In our experience and survey, these limits are typically blunt (a single limit for a city or zip code) and set by business teams using heuristics and primarily from a risk-management-backwards view. This one-size-fits-all approach means we leave money on the table on customer groups where the limits are too strict and lose money on groups where they are lax. We need to balance the risk-management and the customer-preference angles simultaneously and dynamically. Note that this is different from a typical credit-scoring approach due to at least two major reasons - 1) the information available in e-commerce, especially online food delivery, is much sparser, 2) the limit needs to be calculated dynamically in real-time depending on the transaction value, restaurant and marketplace constraints and network effects. To this end, we present a framework called DyCOD that maps this to a non-linear constrained optimisation problem. To the best of our knowledge there are no published results in this area and our work is the first. We solve this using both heuristic and model driven approaches and run large-scale A/B experiments. Our approaches delivered a 2.1% lift in margin per order vs. the baseline while not increasing any risk metrics, which is highly significant at our scale.