Motivations, Classification and Model Trial of Conversational Agents for Insurance Companies

Advances in artificial intelligence have renewed interest in conversational agents. So-called chatbots have reached maturity for industrial applications. German insurance companies are interested in improving their customer service and digitizing their business processes. In this work we investigate the potential use of conversational agents in insurance companies by determining which classes of agents are of interest to insurance companies, finding relevant use cases and requirements, and developing a prototype for an exemplary insurance scenario. Based on this approach, we derive key findings for conversational agent implementation in insurance companies.Comment: 12 pages, 6 figure, accepted for presentation at The International Conference on Agents and Artificial Intelligence 2019 (ICAART 2019

Do Chatbots Dream of Androids? Prospects for the Technological Development of Artificial Intelligence and Robotics

The article discusses the main trends in the development of artificial intelligence systems and robotics (AI&R). The main question that is considered in this context is whether artificial systems are going to become more and more anthropomorphic, both intellectually and physically. In the current article, the author analyzes the current state and prospects of technological development of artificial intelligence and robotics, and also determines the main aspects of the impact of these technologies on society and economy, indicating the geopolitical strategic nature of this influence. The author considers various approaches to the definition of artificial intelligence and robotics, focusing on the subject-oriented and functional ones. It also compares AI&R abilities and human abilities in areas such as categorization, pattern recognition, planning and decision making, etc. Based on this comparison, we investigate in which areas AI&R’s performance is inferior to a human, and in which cases it is superior to one. The modern achievements in the field of robotics and artificial intelligence create the necessary basis for further discussion of the applicability of goal setting in engineering, in the form of a Turing test. It is shown that development of AI&R is associated with certain contradictions that impede the application of Turing’s methodology in its usual format. The basic contradictions in the development of AI&R technologies imply that there is to be a transition to a post-Turing methodology for assessing engineering implementations of artificial intelligence and robotics. In such implementations, on the one hand, the ‘Turing wall’ is removed, and on the other hand, artificial intelligence gets its physical implementation

HR Shared Services (HRSS): Model and Trends

[Excerpt] The findings of this research project are based on interviews with 44 Human Resources (HR) leaders across 39 national and international companies within 15 industries ranging from manufacturing to consulting services. The interviews ranged from 45 minutes to one hour, and sought to understand models, best practices, and trends. The interview included questions about employee experience, technology, and the integration between HR Shared Services (HRSS) and the overall HR Organization. To provide background information and data, the HR leaders answered a short survey, giving details about the structure of their HRSS, locations, areas of HR that had work performed in the shared services organization, systems, and technology capabilities

Artificial Intelligence for the Financial Services Industry: What Challenges Organizations to Succeed?

As a research field, artificial intelligence (AI) exists for several years. More recently, technological breakthroughs, coupled with the fast availability of data, have brought AI closer to commercial use. Internet giants such as Google, Amazon, Apple or Facebook invest significantly into AI, thereby underlining its relevance for business models worldwide. For the highly data driven finance industry, AI is of intensive interest within pilot projects, still, few AI applications have been implemented so far. This study analyzes drivers and inhibitors of a successful AI application in the finance industry based on panel data comprising 22 semi-structured interviews with experts in AI in finance. As theoretical lens, we structured our results using the TOE framework. Guidelines for applying AI successfully reveal AI-specific role models and process competencies as crucial, before trained algorithms will have reached a quality level on which AI applications will operate without human intervention and moral concerns

Chatbot Quality Assurance Using RPA

Chatbots are becoming mainstream consumer engagement tools, and well-developed chatbots are already transforming user experience and personalization. Chatbot Quality Assurance (QA) is an essential part of the development and deployment process, regardless of whether it’s conducted by one entity (business) or two (developers and business), to ensure ideal results. Robotic Process Automation (RPA) can be explored as a potential facilitator to improve, augment, streamline, or optimize chatbot QA. RPA is ideally suited for tasks that can be clearly defined (rule-based) and are repeating in nature. This limits its ability to become an all-encompassing technology for chatbot QA testing, but it can still be useful in replacing part of the manual QA testing of chatbots. Chatbot QA is a complex domain in its own right and has its own challenges, including the lack of streamlined/standardized testing protocols and quality measures, though traits like intent recognition, responsiveness, conversational flow, etc., are usually tested, especially at the end-user testing phase. RPA can be useful in certain areas of chatbot QA, including its ability to increase the sample size for training and testing datasets, generating input variations, splitting testing/conversation data sets, testing for typo resiliency, etc. The general rule is that the easier a testing process is to clearly define and set rules for, the better it's a candidate for RPA-based testing. This naturally increases the lean towards technical testing and makes it moderately unfeasible as an end-user testing alternative. It has the potential to optimize chatbot QA in conjunction with AI and ML testing tools