A Case Study of Innovation in the Implementation of a DSS System for Intelligent Insurance Hub Services

Abstract

This paper presents a case study of the Project 'DSS INSURANCE HUB'. Specifically, research activities are carried out in the context of digital transformation in the insurance service sector. In the first part of the paper, a core of Key Performance Indicators (KPIs) of insurance service performance is identified, mainly tracking agents' activities, starting to the Plan, Do, Check, Act (PDCA) process mapping of the insurance activities about claims. Then the study focused on the implementation of a Long Short Term Memory (LSTM) artificial neural network predicting the value of agent-related KPIs. In particular, the neural network is tested on the prediction of the KPI called SP defined by the ratio between the cost of the claims and the insurance premium collected. In order to validate the LSTM model, further artificial records (AR) are added for the training dataset construction, by generating 2.800 records of variables. The LSTM-AR increases of 25% the LSTM performance. The adopted approach is typical for real cases of study where often no much data is available. The LSTM model, created for the SP prediction, is suitable to calculate the value of other KPIs. The formulated KPI dashboards are implemented in a Decisional Support System (DSS) platform providing the agent activity and company information, and opportunities to improve the business processes. .

Original Data Vs High Performance Augmented Data for ANN Prediction of Glycemic Status in Diabetes Patients

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Augmented Data and XGBoost Improvement for Sales Forecasting in the Large-Scale Retail Sector

“…In order to further improve the performance of the model, the augmented data technique was used. This approach was already validated in the case of LSTM neural networks [43][44][45], where data were artificially created to feed a data-poor training dataset. In [44], the training dataset was increased from 768 records to 10,000 records.…”

“…In our case the data were not enough, so we used the augmented data technique. This new technique, which has been tested in different sectors [43][44][45], is being applied for the first time for sales forecasting.…”