A machine-learning (ML) based, predictive, digital underwriting system providing an automated parameter-driven predictive underwriting process based on measured probability values associated with individuals of a cohort or portfolio, the individuals being exposed to a probability of occurrence of one or more predefined medical and/or health and/or life events having the probability value with a predefined severity within a future measuring time-window, the ML-based, predictive, digital underwriting system comprising:
- processing circuitry configured to implement
- a data pre-processing engine providing pre-processed datasets of individuals measured or captured in a past measuring time window, wherein a plurality of data sources are accessed by means of the data pre-processing engine via a data-interface capturing parameter values to each individual of the cohort or portfolio and assigning the parameter values to a corresponding data set associated with an individual, and wherein the data set at least comprises measuring parameter related to demographic characteristics parameters and/or in-force risk-transfer characteristics parameters and/or claims characteristics parameters and/or UW process characteristics parameters and/or extended condition parameters,
- wherein the extended condition parameters comprise at least wearable telematics parameters at least comprising log in/out time and/or activities and/or body characteristics data during/between activities and the demographic characteristics parameters comprise at least an occupation class value and/or a height value and/or a weight value and/or a gender value and/or a smoking status value and/or an education indication value and/or a date of birth (DOB) value and/or a race indication value and/or a nationality indication value and/or a marital status value and/or a number of children and/or occupation (sub-) classes level 1 value provided by industry code mapping and/or an issue date and/or an annual income value and/or location value comprising geo-coding data or address data and/or payment frequency and/or payment method indication data,
- wherein the data sources of the system at least comprise laboratory/clinical/medical measuring devices, the laboratory and/or clinical and/or medical measuring devices and/or sensors at least comprise measuring devices or sensors measuring platelet distribution width and/or mean platelet volume and/or mean red blood cells volume and/or glucose concentration and/or red blood cells and/or monocytes, lymphocytes and/or mean hemoglobin per red blood cell concentration and/or white blood cells and/or platelets and/or creatinine concentration and/or hemoglobin concentration and/or mean hemoglobin per red blood cell and/or red blood cells distribution width and/or hematocrit, and/or neutrophils,
- wherein the processing circuitry is further configured to implement a risk-labelling engine comprising an unsupervised machine-learning (ML) structure for automated clustering the pre-processed datasets of individuals, the risk-labelling engine providing datasets of individuals segmented by clusters by processing the pre-processed datasets of individuals by the unsupervised machine-learning (ML) structure, wherein the unsupervised machine-learning (ML) structure at least comprise a K-means machine-learning (ML) structure, the K-means machine-learning structure using a vector quantization process partitioning the measured and pre-processed datasets of individuals into a number k of clusters, where each measured and pre-processed dataset of individuals is associated with a cluster having the nearest means to its cluster center or to its cluster centroid serving as a prototype of said cluster,
- wherein the ML-based, predictive, digital underwriting system comprises a supervised machine-learning (ML) structure for automated classification and/or validation of datasets of individuals segmented by the clusters, wherein the clusters are automatically classified by a supervised machine-learning structure or by a pattern recognition module based on historically measured and validated datasets, and wherein the learning phase of the supervised machine-learning (ML) structure comprises a feedback learning loop based on classified historical datasets of individuals, and
- wherein the ML-based, predictive, digital underwriting system comprises an artificial-intelligence (AI) module with a supervised machine-learning structure trained during a training phase by supervised machine-learning, the AI-module generating in the application phase for each inputted dataset of an individual a measured risk score value as the measured occurrence probability value for the occurrence of one or more predefined medical and/or health and/or life events to the individual assigned to the inputted dataset.