13,829 research outputs found
Ethical Implications of Predictive Risk Intelligence
open access articleThis paper presents a case study on the ethical issues that relate to the use of Smart Information Systems (SIS) in predictive risk intelligence. The case study is based on a company that is using SIS to provide predictive risk intelligence in supply chain management (SCM), insurance, finance and sustainability. The pa-per covers an assessment of how the company recognises ethical concerns related to SIS and the ways it deals with them. Data was collected through a document review and two in-depth semi-structured interviews. Results from the case study indicate that the main ethical concerns with the use of SIS in predictive risk intelli-gence include protection of the data being used in predicting risk, data privacy and consent from those whose data has been collected from data providers such as so-cial media sites. Also, there are issues relating to the transparency and accountabil-ity of processes used in predictive intelligence. The interviews highlighted the issue of bias in using the SIS for making predictions for specific target clients. The last ethical issue was related to trust and accuracy of the predictions of the SIS. In re-sponse to these issues, the company has put in place different mechanisms to ensure responsible innovation through what it calls Responsible Data Science. Under Re-sponsible Data Science, the identified ethical issues are addressed by following a code of ethics, engaging with stakeholders and ethics committees. This paper is important because it provides lessons for the responsible implementation of SIS in industry, particularly for start-ups. The paper acknowledges ethical issues with the use of SIS in predictive risk intelligence and suggests that ethics should be a central consideration for companies and individuals developing SIS to create meaningful positive change for society
Multiclass Classification of Motor Insurance Customers in Portugal
The insurance market is highly competitive. To stay in line with other companies in today's world, it is
not enough for a company to have the best price. The most important move now is to make a
personalized offer to each client. Insurance companies have an enormous amount of data that can be
used to understand their customers better. What do they want? What offer would attract new clients,
and what offer would keep existing customers from leaving? The project aims to classify customersâ
profiles based on their individual preferences in motor insurance.Internship Report presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytic
Choosing how to discriminate: navigating ethical trade-offs in fair algorithmic design for the insurance sector
Here, we provide an ethical analysis of discrimination in private insurance to guide the application of non-discriminatory algorithms for risk prediction in the insurance context. This addresses the need for ethical guidance of data-science experts, business managers, and regulators, proposing a framework of moral reasoning behind the choice of fairness goals for prediction-based decisions in the insurance domain. The reference to private insurance as a business practice is essential in our approach, because the consequences of discrimination and predictive inaccuracy in underwriting are different from those of using predictive algorithms in other sectors (e.g., medical diagnosis, sentencing). Here we focus on the trade-off in the extent to which one can pursue indirect non-discrimination versus predictive accuracy. The moral assessment of this trade-off is related to the context of applicationâto the consequences of inaccurate risk predictions in the insurance domain
Boosting insights in insurance tariff plans with tree-based machine learning methods
Pricing actuaries typically operate within the framework of generalized
linear models (GLMs). With the upswing of data analytics, our study puts focus
on machine learning methods to develop full tariff plans built from both the
frequency and severity of claims. We adapt the loss functions used in the
algorithms such that the specific characteristics of insurance data are
carefully incorporated: highly unbalanced count data with excess zeros and
varying exposure on the frequency side combined with scarce, but potentially
long-tailed data on the severity side. A key requirement is the need for
transparent and interpretable pricing models which are easily explainable to
all stakeholders. We therefore focus on machine learning with decision trees:
starting from simple regression trees, we work towards more advanced ensembles
such as random forests and boosted trees. We show how to choose the optimal
tuning parameters for these models in an elaborate cross-validation scheme, we
present visualization tools to obtain insights from the resulting models and
the economic value of these new modeling approaches is evaluated. Boosted trees
outperform the classical GLMs, allowing the insurer to form profitable
portfolios and to guard against potential adverse risk selection
Attribute Identification and Predictive Customisation Using Fuzzy Clustering and Genetic Search for Industry 4.0 Environments
Today´s factory involves more services and customisation. A paradigm shift is towards âIndustry 4.0â (i4) aiming at realising mass customisation at a mass production cost. However, there is a lack of tools for customer informatics. This paper addresses this issue and develops a predictive analytics framework integrating big data analysis and business informatics, using Computational Intelligence (CI). In particular, a fuzzy c-means is used for pattern recognition, as well as managing relevant big data for feeding potential customer needs and wants for improved productivity at the design stage for customised mass production. The selection of patterns from big data is performed using a genetic algorithm with fuzzy c-means, which helps with clustering and selection of optimal attributes. The case study shows that fuzzy c-means are able to assign new clusters with growing knowledge of customer needs and wants. The dataset has three types of entities: specification of various characteristics, assigned insurance risk rating, and normalised losses in use compared with other cars. The fuzzy c-means tool offers a number of features suitable for smart designs for an i4 environment
'It's Reducing a Human Being to a Percentage'; Perceptions of Procedural Justice in Algorithmic Decisions
Data-driven decision-making consequential to individuals raises important questions of accountability and justice. Indeed, European law provides individuals limited rights to âmeaningful information about the logicâ behind significant, autonomous decisions such as loan approvals, insurance quotes, and CV filtering. We undertake three studies examining people's perceptions of justice in algorithmic decision-making under different scenarios and explanation styles. Dimensions of justice previously observed in response to human decision-making appear similarly engaged in response to algorithmic decisions. Qualitative analysis identified several concerns and heuristics involved in justice perceptions including arbitrariness, generalisation, and (in)dignity. Quantitative analysis indicates that explanation styles primarily matter to justice perceptions only when subjects are exposed to multiple different stylesâunder repeated exposure of one style, scenario effects obscure any explanation effects. Our results suggests there may be no 'bestâ approach to explaining algorithmic decisions, and that reflection on their automated nature both implicates and mitigates justice dimensions
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