Enterprise organizations have large amounts of data which is utilized by multiple Machine Learning (ML) models over various software frameworks. These models provide trends and insights from the data that can help enterprises define business rules around their processes. However, if certain aspects of this data are removed from the datasets, it could influence the business rules and policies in place. When a user requests data to be removed, the model retraining may be required called Machine Unlearning (MU). Recent research works in the area of MU include different methods of retraining the machine learning models. It turns out that there is lack of work in removing certain aspects of data, and quantifying its impact on the models. This paper aspires to provide a novel methodology IDMU (Impact Driven Machine Unlearning) that performs quantification of the impact of data removal requests while performing MU. Our method provides recommendations for data removal requests, factoring in underlying features of data. The results from the industrial application and evaluation of our method on a financial services dataset are encouraging. The overall IDMU had a mean MAPE of 10.25% over a set of 120 data removal requests. It also saved ~1900 hours of model retraining time by factoring in urgency and impact of data removal requests over a period of three years.

The transactions of goods and services between enterprise service providers are often driven by contracts and purchase orders. Every month thousands of invoices are billed to customers who settle them based on the usage of services. Considering the vast number of purchase orders that are signed, it requires considerable manual effort by the service provider to process and manage them. Moreover, the invoice’s billed data may not be maintained in the same cloud system as the purchase orders. This leads to complexity with data mapping between the two data sets. Sometimes the invoices may get into a dispute due to over exhaustion of allocated funds or may be billed to an expired purchase order. Hence managing the billing service is a huge undertaking along with increased cost.To address these challenges, we developed an order manage- ment system that transforms the monitoring of purchase orders to increase renewals as well as decrease disputes. The system includes an automated purchase order-invoice data mapping model along with a risk analytics model that evaluates the orders against the invoices billed. The output is the set of actionable and non actionable insights based on customer portfolio, risk level as well as market trends in usage of services. We illustrate our method with some promising results on data of one of the world’s largest IT service providers.

When Information Technology (IT) service providers cater services to their customers, there’s a common practice to document the intent of buyer to purchase them through a Purchase Order (PO). The details of a PO document are complex in nature, as they include hierarchical structure of sub-services, price points over the duration of PO, base setup cost, billing frequency, renewal terms etc. In large enterprises with high volume PO’s, the traditional approach of managing PO’s with their invoices involved a great deal of inefficiency and labor-intensive manual work due to lack of automation and disconnected processing. As a result, they often suffered tedious manual monitoring and failed PO renewals, resulting in delays and added costs.In this paper, we propose a joint time-series learning framework that tackles the high dimensionality PO data. It identifies the metrics to monitor, while using time-series prediction coupled with tone analysis to reach out to customers, in order to maximise PO renewals. We demonstrate utility of our approach by implementing our method on a dataset from a global IT service provider that indicates encouraging results.

In the face of emergent external factors (e.g., supply chain disruptions or public health crises like COVID-19), businesses must adapt their business model quickly in order to ensure service continuity. However, providing recommendations regarding changes should be made to the business model is a challenging problem. First, it requires details of interactions between different components of the business (e.g., service offerings, inventory, staffing, demand) to understand what possible courses of action will have the most business impact. Second, automated models may provide recommendations on changes required in the business operations. However, with lack of human insight, it will be hard to verify the feasibility of these recommendations. Third, a generic model may not be able to provide good recommendations for diverse set of business models. Fourth, the model may not have enough features or training data to provide good recommendations.In this paper, we propose a novel approach to provide actionable items that can be recommended to business users given their business features and recommendations given to businesses in similar domain. Here we first use clustering to find the business domain and similar feature set of the domain. Then, we build a machine-learning model with explainable insights to provide recommendations on different business actions that can be taken to ensure business operations in the face of emergent external factors. Next we augment our approach with human-in-the-loop to improve its performance. Finally, we federate the machine-learning model in a similar domain to add more explainable and trusted insights and recommendations by other businesses. We describe our method, illustrate its utility with results from our implementation, and discuss areas for future work.