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Journal
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2444-8656
First Published
01 Jan 2016
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English
access type Open Access

Interaction design of financial insurance products under the Era of AIoT

Published Online: 20 May 2022
Volume & Issue: AHEAD OF PRINT
Page range: -
Received: 16 Jul 2021
Accepted: 06 Dec 2021
Journal Details
License
Format
Journal
eISSN
2444-8656
First Published
01 Jan 2016
Publication timeframe
2 times per year
Languages
English
Abstract

This study aims to explore how the insurance business can combine artificial intelligence (AI), huge data technology and good design under the ecological background of AIoT to improve the efficiency of double-entry in the insurance business and improve the user experience. It is accomplished through observation, user interview and other methods to explore user needs and distress points, comprehensive use of image and video analysis, face recognition, behaviour detection and other AI technologies. aiCore intelligent dual-recording system is proposed, which integrated mobile terminal dual-recording application and backstage quality control system. The proposed system realised the design optimisation of the whole process of intelligent dual-recording scenario combining the three functions ‘front-end intelligent dual-recording, in-process real-time quality control and post-AI quality control.’ Using in-process detection, post-review and manual final audit, the aiCore intelligent dual-recording system has significantly improved the efficiency of dual-recording, quality inspection pass rate and user experience satisfaction.

Keywords

Introduction

With the rapid development of artificial intelligence (AI) and the Internet of Things (IoT), AIoT (Intelligent Internet of Things) emerged in 2018 and has spread extensively. AIoT stands for artificial intelligence (AI) + internet of things (IoT) which is not a new technology, but the integration of AI and IoT in specific application scenarios. It is another new application form that distinguishes it from traditional IoT applications. To be specific, AIoT means the system collects data in real-time through information sensors, conducts intelligent analysis (such as comparison, prediction, positioning, etc.) on the data collected from various intelligent terminals or the cloud by machine learning and proposes solutions according to specific scenarios. AI is to study the rules of human intelligent behaviour (such as learning, calculation, reasoning, thinking, planning, etc.) and construct artificial systems with certain intelligence capabilities to complete the tasks that previously required human intelligence [2]. From the perspective of technology, AI includes data, algorithms, computing power and application scenarios. IoT can realise the interconnection among devices and share information at the same time. The White Paper of Intelligent Internet of Things (AIoT) in China estimated that there will be nearly 20 billion IoT connections in China in 2025. The Internet of Everything and massive data will promote all industries to be on the road of intelligence. At present, the main application scenarios of AIoT are as follows: (1) residential life scenarios with the core on user experience; (2) industrial operation scenario with the core on improving efficiency and saving cost; and (3) smart city scenario, the core is public service and system security [3].

In the field of finance and insurance, the signing of a policy needs to go through complex communication and interaction between insurance staff and customers. It involves the use of different hardware devices and various operation behaviours of users. The combination of IoT and AI plays an important role in improving intelligence in the financial and insurance industry. At present, the application scenarios and related technologies of AI in the financial industry are mainly in the aspects of obtaining customers intelligently, identifying the identity, intelligent customer service, intelligent investment, intelligent risk control and business process optimisation.

However, technology itself is not enough to achieve all. How to achieve real compliance, rationality and efficiency by combining technology to reasonably design the insurance process and human-computer interaction mode, so as to reduce labour pressure, operating costs, prevent risks and improve user experience? Human-computer interaction design plays a crucial role in this process (see Figure 1).

Fig. 1

Framework diagram of the combination of AIoT and human-computer interaction design under the financial and insurance scenario. AI, artificial intelligence; IoT, internet of things

Analysis on the experience of current insurance dual-recording
Current insurance dual-recording

In 2016, insurance regulatory authorities put forward the requirement of adding ‘dual-recording’ in insurance services for the first time, and various financial institutions such as banks, insurance and securities have gradually established business norms of ‘dual-recording’ for financial product sales. ‘Dual-recording’ refers to video and sound recording, which is a very important part of insurance business flow, and it is human and time-consuming at the same time. After the recording of the insurance process, it is necessary to conduct a ‘quality inspection’ on the recording process and results. Quality inspection means the inspection and control of insurance companies and banking insurance agencies on the authenticity, integrity and accuracy of people's identity information and sales process through dual-recorded audio and visual materials. At present, the ‘compliance risk’ involved in dual-recording and quality inspection is generally high. Compliance risk refers to the fact that the behaviours of insurance companies, insurance intermediaries, insurance agents and their employees shall comply with laws and regulations, regulatory provisions, self-regulation rules, market practices, the company's internal management system and the ethical code of honesty and trustworthiness. Failure to comply with these guidelines may result in the risk of legal or regulatory penalties, significant financial or reputational loss.

Now, most financial institutions still adopt the traditional way of manual dual-recording and quality inspection in their dual-recording business processes and products, which are characterised by low efficiency, high cost, poor user experience and high compliance risk. With the development of information technology, major financial and insurance products are seeking more efficiency to solve the problems of long-recording time, low efficiency, high-quality inspection cost and high compliance risk.

Research

Observation, recording and interviews were used in this research to analyse the overall process of ‘dual-recording’ and it is divided into two parts: recording and quality inspection. User roles fall into two categories namely insurance agent and policyholder. During the recording process, the insurance agent needs to listen, introduce the product orally, show the product to the customer, confirm the identity of the customer, operate the software and take pictures for the customer. Policyholders need to listen, check information, answer questions, identify the identity, read text, take pictures, sign the document etc. The whole process is relatively tedious, and the error rate is high.

Recording stage

In this research, the recording process is divided into four stages: (A) ask for willingness, (B) mutual verification, (C) product introduction and (D) policy confirmation (as shown in Figure 2). Each stage is divided into several small steps for analysis.

Fig. 2

Process analysis diagram of insurance recording stage

Mutual verification is relatively complex and cumbersome, including presenting ID cards and confirming written documents, which involves many steps in checking and confirmation stages. The content to be filled involves personal information which makes policy seekers have vigilance and doubt, thus leading to a decrease in desire to buy.

During the product introduction stage, the insurance agents tend to be unfriendly or emotional as this stage requires them to introduce the policy content repeatedly, which takes a long time. Particularly speaking, during the ‘guarantee scheme description’ and ‘clause introduction,’ agents will have frequent interaction with policy-holders. After listening to various clauses, policyholders need to make a ‘clear or unclear, do not understand’ answer. Some policyholders answer at will in order to go through the process as quickly as possible; Some people will be cautious and will ask for more details, and they tend to doubt the content of the policy and agent; Some people easily will give up the idea of purchasing the policy as they cannot understand the benefits and value of the insurance policy well. Therefore, how to use technology and design to help optimise the overall steps and experience of product introduction is particularly important.

In the order confirmation stage, policyholders need to put a series of signatures, and this is the last step of order completion. After a variety of complex interaction processes between the insurance agent and the policyholder, policyholders will sign immediately if the previous process has been smooth; If they have doubts, they will easily abandon the purchase. Therefore, a pleasant purchase experience for the policyholder is very important at this time.

Quality inspection stage

After dual-recording is completed, the recorded results will be put into the quality inspection, which is checked by in-house quality inspection personnel. The review of ‘dual-recording’ will take a lot of time, and the average time for each order is 30–40 minutes. This method not only is human consuming with high management costs but also brings poor experience to policyholders because of long-recording time and repeated recording. According to statistics, more than 10% of the insurance business lost their customers in the ‘dual-recording’ which obviously disobeys the principle of the insurance company for pursuing ‘high-quality service.’ In addition to the long-recording time and poor efficiency, there are also other problems such as low video quality and low inspection accuracy. If the video cannot pass the inspection, the staff should find policyholders to record again. This is a waste of time for both sides, and it will dissatisfy users.

A human-computer collaborated insurance dual-recording design
Human-computer collaboration

The earliest concept of Human-Computer Interaction (HCI) originated from the interaction between humans and computers. HCI is defined by the Association for Computing Machinery as the discipline of the design, evaluation, implementation and phenomena associated with computer systems. Professor Alandix from the University of Birmingham believes that human-computer interaction is a discipline that studies human beings, computers and the interaction between them. The purpose of HCI is to make computers better serve human beings. Now, it is generally believed that HCI is a process of exchanging information between humans, hardware and software systems to accomplish some specific tasks. Later, it gradually expanded to a wider range of applications. It is a discipline that studies the interaction between systems and users. A system consists of hardware machines and computerised software. Professor Sun Xiaohua divided HCI into three stages in his paper A Review of Collaborative Research on Human-Computer Intelligence: human-physical system interaction, human-digital system interaction and human-intelligent system interaction [4]. According to Hornbæk et al., HCI research is mainly divided into two aspects: interaction mode and interaction quality. Among them, interaction mode is mainly driven by technology, including interaction mode, information equipment, application field, etc. Interaction quality is related to people's perception and cognition, and its emphasis is on the improvement of usefulness, usability, efficiency, aesthetics and other aspects.

We are in the process of moving from HCI to human-computer intelligent collaboration [4]. The relationship between the intelligent system and humans has changed from the simple ‘tool’ relationship in the past to the mutual ‘collaboration.’ AI is not to completely replace human beings with machines, but to use intelligent computing to improve efficiency and accuracy, make up for human's shortage and reduce manpower wastage, and so machines and humans can work together intelligently. Synergy refers to the fact that the application of Information Communications Technology (ICT) products in the production process can improve compatibility among other elements and improve operational efficiency [6,7,8]. AI product refers to the product or system that uses the theory, method and technology of AI to deal with problems [9]. An intelligent system has four characteristics: situational awareness, adaptive learning, autonomous decision-making and active interaction and collaboration [10]. As designers, we should consider how to design a more rational human-computer collaborative process and experience with the help of intelligent technology through design thinking. ‘Human’ in human-computer collaboration refers to ‘multiple user groups,’ that is, there will be diversified user experiences. Gartner put forward the concept of ‘Total Experience’ (TX) [11] in its Strategic Technology Trends Report of 2021. It is predicted that enterprises that can provide TX in the future will outperform far better than their competitors in terms of satisfaction index, thus it can be concluded that experience is an implicit but important element in human-computer collaboration.

The design for insurance dual-recording solution

AiCore intelligent dual-recording solution is an intelligent solution for optimising insurance dual-recording developed under the overall framework of AIoT, relying on aiCore AI big data platform. Specifically, as shown in Figure 3, the data of audio and video recordings are acquired through mobile devices will be monitored and stored in real-time. We accomplish this by the comprehensive use of biometric recognition, video image analysis and other AI and big data analysis, and text, voice, face and behaviour detection and recognition; it is done through a well-articulated human-computer interaction design and reasonable insurance process design so that the overall optimisation of user experience in the ‘dual-recording’ during insurance business is realised. Moreover, the dual-recording solution provides installation, configuration, monitoring and operation of a complete set of dual-recording services. Mark Purdy et al. [12] believe that AI is not only a tool to improve productivity, but also a new factor of production. In aiCore intelligent dual-recording solution, AI makes recorded video data more compliant, which is a practical example of using AI as a new factor of production.

Fig. 3

AiCore intelligent dual-recording solution architecture diagram based on AIoT framework

The aiCore intelligent dual-recording solution fully considers the participation rate and experience of different roles (including policyholder, insurance agent, quality inspector and administrator) in the whole recording process, which is the comprehensive experience proposed in Gartner's report, to realise the complete dual-recording interaction process combined with information technology (Figure 4). The design focuses on (1) Intelligent dual-recording: using voice, face recognition and other technologies to improve the accuracy and efficiency of insurance; (2) In-process real-time quality inspection: real-time quality inspection and feedback are carried out for each recording step to reduce post-recording repetition and improve efficiency and customer satisfaction; and (3) AI check at backstage where the recorded video is stored and managed in the backstage to reduce risks.

Fig. 4

aiCore intelligent dual-recording solution interaction system

Mobile intelligent dual-recording

The mobile intelligent dual-recording is chiefly completed by the interaction between agents and policy-holders. The core demand is to be able to smoothly and finely record the dual-recording video, submit the video to backstage for quality inspection and receive feedback on the effectiveness of quality inspection. The development of big data and intelligent technology presents opportunities for the interactive development of natural language dialogue. Voice replaces the text input and it has gradually become an interactive behaviour of users using intelligent software. The Voice User Interface (VUI) also plays a role in the process of dual-recording.

As shown in Figure 5, the interaction interface of dual-recording system uses not only the visual senses of people, also voice introduction, face recognition and behaviour identification technology into the interaction design process of the insured, and also takes advantage of people's multichannel, such as voice, gestures, eye, face, etc., so that users can complete the above-mentioned human identification processes such as ‘mutual verification, product introduction’ more naturally and harmoniously and save the insurance agent from repetitive work. At the same time, intelligent voice playback can provide more standard dual-recording guidance, ensure process and speech standards and reduce the error rate in oral speech.

Fig. 5

Video recording interface of intelligent dual-recording APP on the mobile terminal

In-process real-time quality inspection

The video stream generated by the dual-recording is accessed and analysed in real-time through the cloud. A variety of AI and big data processing technologies such as biometric recognition and video image analysis are adopted to solve the difficulties of real-time detection in dual-recording. Through the form of voice, text and image feedback, real-time display of the result in each step of the dual-recording process can be achieved. This can remind abnormal conditions precisely, standardise the insurance process, solve compliance problems from the source and reduce duplicate recording.

AI check and inspection at the backstage

The main users of the backstage include quality inspectors and managers, and the main function is to conduct a quality inspection of the video and personnel management. The insurance agent uploads the recorded video to backstage through APP, and the backstage system uses AI to quickly review and locate the key nodes of the video. In the interface design of quality inspection, a variety of video screening methods are provided to help quality inspectors quickly find video files that need to be re-checked for quick review (as shown in Figure 6). The core function of managers is to manage the accounts of agents and quality inspectors. Face recognition and other technologies are used to collect the basic information of agents, and through a flexible user management interface, the functions of adding, deleting, modifying and checking user accounts are realised.

Fig. 6

Video playback interface for dual-recording at backstage quality inspection

AI algorithm design for smart dual-recording

The aiCore smart dual-recording requires the algorithm to focus on facial detection, identification, document detection and recognition, document detection and recognition, motion detection, etc. Based on the content of the detection and recognition, smart dual-recording uses a target detection algorithm and OCR recognition algorithm to detect key information that is to be checked in videos. In the process of text detection and recognition, images will be input before detecting, correcting and recognising text. The text recognition framework is shown in Figure 7.

Fig. 7

The overall framework of text recognition

Target detection focuses on a specific object, requiring that the category and location (classification + localisation) of this target be obtained at the same time. Therefore, the output of the detection model is a list, where each item uses an array to give the category and position of the detected target (the coordinates of the rectangular detection frame are commonly used). There are three steps involved in the target detection algorithm: extraction of image features, generation of candidate regions and their classification. The image feature is the basis of extracting the detection process. The target detection algorithm based on deep learning has two research branches: (1) a two-stage algorithm represented by R-CNN and (2) a one-stage algorithm represented by SSD and YOLO. The smart dual-recording uses the one-stage algorithm. The block diagram of the target detection process is shown in Figure 8:

Fig. 8

One-stage Detector

Design evaluation

The research group applied the dual-recording solution in the business process of a well-known domestic insurance company. By recording the use of this solution from 6 January to 11 January 2021 consecutively, the comparison between the use of manual inspection and the use of aiCore intelligent dual-recording solution (with AI involved) for inspection of the quality control was conducted in three aspects namely efficiency, security, and user experience (as shown in Table 1). They especially compare the ‘one-time record for compliance testing’ of dual-recording to evaluate how the new solution has optimised the process and experience of original dual-recording.

The overall comparison between manual quality inspection and aiCore quality inspection

Compared perspectives Manual quality inspection aiCore quality inspection
Efficiency The one-time pass rate for recording 68.76% or so Over 99%
Manual reinspection 1/2 the length of the video No need for manual reinspection
The time span for recording Recording + quality inspection timeBoth execute asynchronouslyUncertain time span About 30 minutes to complete a compliance video recording
Security Data security Risk for information leakage No risk for information leakage
Quality inspection standard No unified standard Unified standard
User experience Interaction Recording interaction, no compliance evaluation Real-time reminders, quickly guide the user to record correctly

Fig. 9

The graph for recording times and recording compliance of manual and aiCore quality inspection

In order to compare the pass rate of one-time recording of various testing items (including the detection of face, certificates, instructions, clauses and signatures), the research team randomly selected 573 one-time testing videos for manual quality inspection, and the results showed that the qualified one-time recordings are 394 and 179 are unqualified. The qualified rate of one-time recordings by manual quality inspection was about 68.76% (394/573=68.76%). However, with the use of aiCore intelligent dual-recording (with AI involved in quality inspection), the qualified rate of one-time recording can be more than 99%. Figure 9 shows the curve of recording times and recording compliance. If the qualified rate of the first manual quality inspection is 68.70%, then it should be recorded four times manually before achieving a 99% of compliance rate. For quality inspection with AI, 99% can be achieved for the first time and a 100% compliance rate can be achieved for the third time.

In terms of efficiency, the aiCore intelligent dual-recording solution can increase the one-time quality inspection compliance rate from around 68.7% to more than 99%. The process of manual re-inspection takes 1/2 times the time of recording the whole video, but the new solution saves the time of re-inspection without manual re-inspection. Typically, it takes up to 2 days after a video is recorded by a human to produce a composite result. If non-compliance is found, customers need to re-record, resulting in a waste of time and manpower, poor user experience and other problems. AiCore quality inspection realises the synchronisation of video recording and quality inspection, which saves a lot of time and improves efficiency.

In terms of security, there are problems of data security and non-standard quality inspection in manual quality inspection. While aiCore intelligent dual-recording solution can ensure that information will not be leaked due to human factors and can provide unified video recording standards.

In terms of user experience, aiCore intelligent dual-recording solution realises real-time reminder, provides correct and friendly step-by-step guidance for users, reduce duplication, decrease the possibility of re-recording afterwards and enable a good product experience through the interaction of using voice, text and images.

Based on the evaluation results, aiCore intelligent dual-recording solution realises the intelligent transformation of financial and insurance dual-recording business scenarios in the whole process, improves the efficiency and compliance rate, optimises user experience and reduces labour costs. Besides meeting the compliance requirements, the system enables customers of financial institutions to increase revenue, reduce costs, improve quality and efficiency and promote the sustainable and healthy development of the financial industry. At present, aiCore intelligent dual-recording system has been applied in the world's top 500 insurance companies. Based on the business scale that such enterprises need to process 3000 videos a day, it is expected to save about 20 million yuan of labour costs for enterprises every year. What's more, it can ensure the compliance of each order, establish a unified quality inspection standard and improve the ability to control financial risks.

Conclusion

With the growth of the insurance business, the increase of human cost and the development of digitalisation and intelligence, banks need to make full use of AI, huge data and IoT to intelligently upgrade ‘dual-recording’ so as to achieve the goal of cost reduction and increasing the efficiency. By analysing the user experience problems and technical difficulties in the dual-recording process, this research proposes an intelligent dual-recording solution based on aiCore AI big data platform under the framework of AIoT. This plan realises the intelligent transformation of the whole process of the dual-recording scenario by combining three functions ‘intelligent dual-recording, in-process real-time quality inspection, and AI check after the event.’ This can solve problems such as high cost, low efficiency and low compliance rate existing in the current financial dual-recording, also effectively helps financial institutions reduce cost and increase efficiency, improves the compliance rate of dual-recording video and user experience, ensures the compliance of the financial products sales, and enables sustainable and healthy development of the financial industry.

Fig. 1

Framework diagram of the combination of AIoT and human-computer interaction design under the financial and insurance scenario. AI, artificial intelligence; IoT, internet of things
Framework diagram of the combination of AIoT and human-computer interaction design under the financial and insurance scenario. AI, artificial intelligence; IoT, internet of things

Fig. 2

Process analysis diagram of insurance recording stage
Process analysis diagram of insurance recording stage

Fig. 3

AiCore intelligent dual-recording solution architecture diagram based on AIoT framework
AiCore intelligent dual-recording solution architecture diagram based on AIoT framework

Fig. 4

aiCore intelligent dual-recording solution interaction system
aiCore intelligent dual-recording solution interaction system

Fig. 5

Video recording interface of intelligent dual-recording APP on the mobile terminal
Video recording interface of intelligent dual-recording APP on the mobile terminal

Fig. 6

Video playback interface for dual-recording at backstage quality inspection
Video playback interface for dual-recording at backstage quality inspection

Fig. 7

The overall framework of text recognition
The overall framework of text recognition

Fig. 8

One-stage Detector
One-stage Detector

Fig. 9

The graph for recording times and recording compliance of manual and aiCore quality inspection
The graph for recording times and recording compliance of manual and aiCore quality inspection

The overall comparison between manual quality inspection and aiCore quality inspection

Compared perspectives Manual quality inspection aiCore quality inspection
Efficiency The one-time pass rate for recording 68.76% or so Over 99%
Manual reinspection 1/2 the length of the video No need for manual reinspection
The time span for recording Recording + quality inspection timeBoth execute asynchronouslyUncertain time span About 30 minutes to complete a compliance video recording
Security Data security Risk for information leakage No risk for information leakage
Quality inspection standard No unified standard Unified standard
User experience Interaction Recording interaction, no compliance evaluation Real-time reminders, quickly guide the user to record correctly

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