Published Paper Details:

Pharma is increasingly under the microscope to be more transparent with Gross-to-Net forecast and Government Pricing reporting. Errors in these calculations can be expensive, leading to significant revenue leakages and fines from regulators. In this research work, we describe and evaluate a novel architecture that integrates AI into an event driven architecture (EDA) to close the loop between CRM systems and financial compliance engines. Unlike the archaic batch-process approaches, realizes pricing contract and rebate eligibility enforcement at time of sale via real-time feeds. The experiment is performed on a synthetic dataset of 488 real commercial transaction instances. The tech stack that is being used revolves around Python (predictive modelling), Apache Kafka (event streaming) and custom-build CRM interface to ingest the data. The system leverages techniques of anomaly detection to identify potential compliance risks on the fly. Results This AI system results in a significant decrease of the reporting time lag and leads to an improved accuracy of REB forecasting, in comparison to traditional methods. "These study results demonstrate that a proactive, event-driven governance approach to biopharma commercial operations can significantly reduce the compliance risk exposure of moving from retrospective healthcare auditing to prospective scrutiny.