Perlindungan Konsumen oleh Otoritas Jasa Keuangan Terhadap Nasabah Asuransi yang Dirugikan

Insurance is an institution to invest funds with a specific purpose so that if in the future it is needed by the insurance customer, the funds can be withdrawn by the insurance customer. However, in reality, many insurance customers suffer losses because the funds they have invested in the insurance company are not disbursed by the insurance company. This is the subject of discussion in this study so that the research method is carried out with an analytical approach and a statutory approach. The research materials are primary, secondary and tertiary legal materials so that the data collection techniques use literature studies and qualitative analysis techniques. In this case, discussing the regulation of the duties of the Financial Services Authority in overcoming the occurrence of a loss insurance customers is regulated in Law Number 21 of 2011 concerning the Financial Services Authority, Regulation of the Financial Services Authority Number 1/POJK.07/2013 concerning Consumer Protection Sector Financial Services and OJK Circular Letter Number 2/SEOJK.07/2014 Regarding Consumer Complaint Service and Settlement and consumer protection by the Financial Services Authority for insurance customers who are harmed is that there is no consumer protection carried out by Financial Services cooperatives for insurance customers who are harmed by the company insurance

For all to see: social risk and observable complaining on Facebook

Purpose Online consumer complaint behaviour that is observable to other consumers provides the firm with an opportunity to demonstrate transparency and service quality to the public eye. The purpose of this paper is to assist practitioners with a strategy to increase perceived accommodativeness in complaint management on social media and reduce the social risk associated with online consumer complaint behaviour using a social exchange theory perspective. Design/methodology/approach Six online experiments with 1,350 US Facebook users were conducted to investigate the effect of supportive and non-supportive virtually present others, and employee intervention on a consumer’s choice to complain, likelihood to make an observable complaint (on the Facebook page) and likelihood to make a non-observable complaint (via Facebook Messenger). The mediating role of perceived accommodativeness and subsequent social risk is also examined. Findings Supportive comments made to the complainant by virtually present others were found to influence participants’ decision to complain, heighten participants’ likelihood to complain about the Facebook page and reduce their likelihood to complain via Facebook Messenger. This effect was reversed in the presence of non-supportive virtually present others and was explained by perceived social risk. Further, a participant’s likelihood to complain about the Facebook page was increased when an employee intervention was directed at a non-supportive comment made to a complainant, by a virtually present other. This effect was explained by the perceived accommodativeness of the employee interaction. Research limitations/implications The findings advance research on online consumer complaint behaviour by investigating how employee intervention can be used to increase the likelihood of an observable complaint. This research is limited in that it does not incorporate individual characteristics, such as introversion/extroversion and propensity to respond to peer pressure, which may affect participant responses. Practical implications This research shows that perceptions of social risk are most effectively reduced by employee intervention directed at a non-supportive comment (made to a complainant) of a virtually present other. Consumer complaint management strategies aimed at minimising perceptions of social risk and encouraging observable online complaint behaviour are proposed. Originality/value This research extends the consumer complaint behaviour taxonomy by introducing the term “observable complaining”, that is, visible complaints made on a Facebook page, and broadens understanding of the organisation’s role in managing non-supportive virtually present others to assuage perceptions of social risk in potential complainants.

HaS-Net: A Heal and Select Mechanism to Securely Train DNNs against Backdoor Attacks

<div>We have witnessed the continuing arms race between backdoor attacks and the corresponding defense strategies on Deep Neural Networks (DNNs). However, most state-of-the-art defenses rely on the statistical sanitization of <i>inputs</i> or <i>latent DNN representations</i> to capture trojan behavior. In this paper, we first challenge the robustness of many recently reported defenses by introducing a novel variant of the targeted backdoor attack, called <i>low-confidence backdoor attack</i>. <i>Low-confidence attack</i> inserts the backdoor by assigning uniformly distributed probabilistic labels to the poisoned training samples, and is applicable to many practical scenarios such as Federated Learning and model-reuse cases. We evaluate our attack against five state-of-the-art defense methods, viz., STRIP, Gradient-Shaping, Februus, ULP-defense and ABS-defense, under the same threat model as assumed by the respective defenses and achieve Attack Success Rates (ASRs) of 99\%, 63.73%, 91.2%, 80% and 100%, respectively. After carefully studying the properties of the state-of-the-art attacks, including low-confidence attacks, we present <i>HaS-Net</i>, a mechanism to securely train DNNs against a number of backdoor attacks under the data-collection scenario. For this purpose, we use a reasonably small healing dataset, approximately 2% to 15% the size of training data, to heal the network at each iteration. We evaluate our defense for different datasets---Fashion-MNIST, CIFAR-10, Celebrity Face, Consumer Complaint and Urban Sound---and network architectures---MLPs, 2D-CNNs, 1D-CNNs---and against several attack configurations---standard backdoor attacks, invisible backdoor attacks, label-consistent attack and all-trojan backdoor attack, including their low-confidence variants. Our experiments show that <i>HaS-Nets</i> can decrease ASRs from over 90% to less than 15%, independent of the dataset, attack configuration and network architecture.</div>

HaS-Net: A Heal and Select Mechanism to Securely Train DNNs against Backdoor Attacks

<div>We have witnessed the continuing arms race between backdoor attacks and the corresponding defense strategies on Deep Neural Networks (DNNs). However, most state-of-the-art defenses rely on the statistical sanitization of <i>inputs</i> or <i>latent DNN representations</i> to capture trojan behavior. In this paper, we first challenge the robustness of many recently reported defenses by introducing a novel variant of the targeted backdoor attack, called <i>low-confidence backdoor attack</i>. <i>Low-confidence attack</i> inserts the backdoor by assigning uniformly distributed probabilistic labels to the poisoned training samples, and is applicable to many practical scenarios such as Federated Learning and model-reuse cases. We evaluate our attack against five state-of-the-art defense methods, viz., STRIP, Gradient-Shaping, Februus, ULP-defense and ABS-defense, under the same threat model as assumed by the respective defenses and achieve Attack Success Rates (ASRs) of 99\%, 63.73%, 91.2%, 80% and 100%, respectively. After carefully studying the properties of the state-of-the-art attacks, including low-confidence attacks, we present <i>HaS-Net</i>, a mechanism to securely train DNNs against a number of backdoor attacks under the data-collection scenario. For this purpose, we use a reasonably small healing dataset, approximately 2% to 15% the size of training data, to heal the network at each iteration. We evaluate our defense for different datasets---Fashion-MNIST, CIFAR-10, Celebrity Face, Consumer Complaint and Urban Sound---and network architectures---MLPs, 2D-CNNs, 1D-CNNs---and against several attack configurations---standard backdoor attacks, invisible backdoor attacks, label-consistent attack and all-trojan backdoor attack, including their low-confidence variants. Our experiments show that <i>HaS-Nets</i> can decrease ASRs from over 90% to less than 15%, independent of the dataset, attack configuration and network architecture.</div>

AN ANALYSIS OF ASPECTS OF THE PROPOSED REFORM OF THE FINANCIAL CONSUMER COMPLAINT RESOLUTION MECHANISMS IN THE SOUTH AFRICAN BANKING SECTOR

One objective of the revised South African market conduct regulatory framework for the financial sector is to introduce an effective dispute resolution framework. The Financial Sector Regulation Act, and the Act to follow the Draft Conduct of Financial Institutions Bill, potentially address some of the deficiencies associated with the old dispute resolution framework. This article makes a distinction between the old and new regulatory provisions concerning internal dispute resolution and external dispute resolution structures in the banking sector. This research highlights the changes involved in the new regulatory framework and identifies which aspects of the amended regulatory framework aim to address a particular issue associated with the old dispute resolution framework. It is not argued whether this legislation will achieve the fair treatment and protection of financial customers in the banking sector, as only time will tell. However, it is acknowledged that the new structure will improve consistency and efficacy in this dispute resolution structure.

Cambodia’s New Digital Signature, E-Commerce and Consumer Protection Laws: Key Components of a Bold Strategy for Dramatic Future Economic Growth

Cambodia has a bold new strategy to stimulate E-commerce and to grow the economy. The Digital Signature (DSL), Consumer Protection (CPL), and E-Commerce Laws (ECL) are important components of that strategy. The DSL provides for licensing of certifying authorities and does not prohibit other types of E-signatures. The CPL prohibits deceptive advertising and creates a consumer complaint procedure. The ECL recognizes the legal validity of secure E-documents and E-signatures, including as evidence in a court of law. The ECL states requirements of secure E-signatures and secure E-documents; E-contract rules; rules for liability of internet service providers and E-sellers; E-government provisions; E-payments services rules; and computer crimes. The ECL should be improved by: (a) recognizing electronic wills, powers of attorney, and real estate documents; (b) adding attribution rules and acknowledge receipt rules for E-contracts; (c) adding mandatory E-government; (d) adding a comprehensive computer crimes law; and (e) adding IT Courts.

Qualitative Analysis of Consumer Redressal Practices in Indian Regulated Sectors

This paper aims to study and analyseconsumer complaint resolution mechanisms and ombudsman frameworks in three Indian regulated sectors and tries to compare it with that of telecom sector. Here an analysis of regulatory data is carried out. The paper is both theoretical and analytical in nature.This research sheds light on complaint resolution frameworks in Indian regulated sectors such as “Banking, Insurance, Electricity and telecom. Role of Ombudsman and alternates dispute resolution mechanism in the sector is also studied. It is necessary for Ombudsman to perform its duties and responsibilities for overall growth of the sector.Visible, sharp complaint resolution structure and noticeable, orderly decision making entity are truly necessary component of complaint solving mechanism. This Paper also analyses statistics, facts of complaint resolution rates etc. Consumer complaint solving framework is a regulatory vehicle for discarding of grievances. This research is indicator of eight principles of effective consumer resolution mechanism.

Service complaint identification in hotel social media: A two-step classification approach

Hotel social media provides access to dissatisfied customers and their experiences with services. However, due to massive topics and posts in social media, and the sparse distribution of complaint-related posts and, manually identifying complaints is inefficient and time-consuming. In this study, we propose a supervised learning method including training samples enlargement and classifier construction. We first identified reliable complaint and noncomplaint samples from the unlabeled dataset by using small labeled samples as training samples. Combining the labeled samples and enlarged samples, classification algorithms support vector machine and k-nearest neighbor were then adopted to build binary classifiers during the classifier construction process. Experimental results indicate the proposed method can identify complaints from social media efficiently, especially when the amount of labeled training samples is small. This study provides an efficient approach for hotel companies to distinguish a certain kind of consumer complaint information from large number of unrelated information in hotel social media.