Automating Telephone Collection by Implementing Automated Outbound Calls : Case OK Perintä Oy

In the debt collection industry, there is fierce competition between companies. The industry faces a lot of changes in regulations, which obligates continuous development in order to keep the collection efficient and fair at the same time. The debt collection process includes many steps which have a high potential for development. Process automation and Robotic Process Automation offer numerous opportunities for companies to improve efficiency in their operations and this also applies to debt collection companies. Customer calls are a central part of the debt collection process, which is above all a customer-oriented way of contacting the customer. However, this requires resources primarily in terms of time and labor, which affects the profitability of the entire collection process. This master thesis focuses to the telephone collection process automation with automated outbound calls. The purpose of the thesis is to find out does automated calls suit for the tel-ephone collection process. Thesis includes a theory section of key theories and case study, where the functionality of automated calls is being tested with A/B test. The case study is made in cooperation with debt collection company OK Perintä Oy. In the literature review of the work focuses on debt collection process and process automa-tion. The case study is carried out with A/B testing, where the suitability of automated call is tested and compared to a call made by a customer service agents. The research data is col-lected by implementing two test groups for outbound customer calls: automated calls and calls made by human. Based on the research, it was found that the automated call seems to be suitable as part of the telephone collection process. The test results show the functionality of the automated call in speeding up the payment and activating the debtor. The overall profitability of auto-mated calls is also good thanks to the low costs. There were also no complaints from debtors towards automated calls. This thesis has been supported by Vaasan Teknillinen Seura ry (VTS). For the research pro-ject, a scholarship from the Reino Ignatius scholarship fund was awarded in 2023. Special thanks to VTS.Perintäalalla vallitsee kova kilpailu alan toimijoiden välillä. Ala kohtaa paljon muutoksia sään-telyissä, joka velvoittaa jatkuvaan kehittymiseen, jotta perintä saadaan pidettyä samanaikai-sesti tehokkaana ja oikeudenmukaisena. Perintäprosessi sisältää monia vaiheita, joissa on paljon kehityspotentiaalia. Prosessin automatisointi ja Robotic Process Automation tarjoavat lukuisia mahdollisuuksia yrityksille parantaa toiminnan tehokkuutta automaation avulla. Tämä pätee myös perintäalan yrityksiin. Asiakaspuhelut ovat keskeinen osa perintäprosessia, joka ennen kaikkea on asiakaslähtöinen tapa ottaa yhteyttä asiakkaaseen. Tämä kuitenkin vaatii resursseja pääasiassa ajan ja työvoi-man suhteen, mikä vaikuttaa koko perintäprosessin kannattavuuteen. Tämä pro gradu tutkielma keskittyy puhelinperintäprosessin automatisointiin automaattisten ulossoittojen avulla. Tutkielman tarkoituksena on selvittää soveltuvatko automatisoidut pu-helut puhelinperintäprosessiin. Tutkielma sisältää teoriaosion keskeisistä teorioista ja tapaus-tutkimuksen, jossa automatisoitujen puhelujen toimivuutta testataan A/B-testillä. Tapaus-tutkimus on tehty yhteistyössä perintäalan yrityksen OK Perintä Oy:n kanssa. Tutkielman kirjallisuuskatsauksessa keskitytään perintäprosessiin ja prosessin automatisoin-tiin, erityisesti robotiikan avulla. Tapaustutkimus toteutetaan A/B-testillä, jossa testataan automatisoitujen puhelujen sopivuutta ja verrataan niitä asiakaspalvelijoiden tekemiin puhe-luihin. Tutkimusaineisto kerätään toteuttamalla kaksi testiryhmää ulossoittoja varten: auto-matisoidut puhelut ja ihmisen tekemät puhelut. Tutkimuksen perusteella havaittiin, että automaattinen puhelu vaikuttaa sopivan hyvin osaksi puhelinperintäprosessia. Testitulokset osoittavat, että automatisoidun puhelun toiminnalli-suus nopeuttaa maksamista sekä aktivoi velallista. Automatisoitujen puhelujen kokonaiskan-nattavuus on myös hyvä alhaisten kustannusten ansiosta. Reklamaatioita automaattipuhelua kohtaan ei myöskään havaittu. Tätä Pro Gradu tutkielmaa on ollut tukemassa Vaasan Teknillinen Seura ry. Tutkimusprojektia varten on myönnetty tehtaanjohtaja, diplomi-insinööri Reino Ignatiuksen stipendirahaston stipendi vuonna 2023. Erityiskiitos Vaasan Teknilliselle Seuralle

Productivity Measurement of Call Centre Agents using a Multimodal Classification Approach

Call centre channels play a cornerstone role in business communications and transactions, especially in challenging business situations. Operations’ efficiency, service quality, and resource productivity are core aspects of call centres’ competitive advantage in rapid market competition. Performance evaluation in call centres is challenging due to human subjective evaluation, manual assortment to massive calls, and inequality in evaluations because of different raters. These challenges impact these operations' efficiency and lead to frustrated customers. This study aims to automate performance evaluation in call centres using various deep learning approaches. Calls recorded in a call centre are modelled and classified into high- or low-performance evaluations categorised as productive or nonproductive calls. The proposed conceptual model considers a deep learning network approach to model the recorded calls as text and speech. It is based on the following: 1) focus on the technical part of agent performance, 2) objective evaluation of the corpus, 3) extension of features for both text and speech, and 4) combination of the best accuracy from text and speech data using a multimodal structure. Accordingly, the diarisation algorithm extracts that part of the call where the agent is talking from which the customer is doing so. Manual annotation is also necessary to divide the modelling corpus into productive and nonproductive (supervised training). Krippendorff’s alpha was applied to avoid subjectivity in the manual annotation. Arabic speech recognition is then developed to transcribe the speech into text. The text features are the words embedded using the embedding layer. The speech features make several attempts to use the Mel Frequency Cepstral Coefficient (MFCC) upgraded with Low-Level Descriptors (LLD) to improve classification accuracy. The data modelling architectures for speech and text are based on CNNs, BiLSTMs, and the attention layer. The multimodal approach follows the generated models to improve performance accuracy by concatenating the text and speech models using the joint representation methodology. The main contributions of this thesis are: • Developing an Arabic Speech recognition method for automatic transcription of speech into text. • Drawing several DNN architectures to improve performance evaluation using speech features based on MFCC and LLD. • Developing a Max Weight Similarity (MWS) function to outperform the SoftMax function used in the attention layer. • Proposing a multimodal approach for combining the text and speech models for best performance evaluation

NASA space station automation: AI-based technology review. Executive summary

Research and Development projects in automation technology for the Space Station are described. Artificial Intelligence (AI) based technologies are planned to enhance crew safety through reduced need for EVA, increase crew productivity through the reduction of routine operations, increase space station autonomy, and augment space station capability through the use of teleoperation and robotics

Simplification of Health and Social Services Enrollment and Eligibility: Lessons for California From Interviews in Four States

Explores state officials' and advocates' views on issues involved in streamlining enrollment and eligibility processes, including the importance of staff buy-in, community partners' outreach efforts, and technological challenges and lessons learned

Enabling stream processing for people-centric IoT based on the fog computing paradigm

The world of machine-to-machine (M2M) communication is gradually moving from vertical single purpose solutions to multi-purpose and collaborative applications interacting across industry verticals, organizations and people - A world of Internet of Things (IoT). The dominant approach for delivering IoT applications relies on the development of cloud-based IoT platforms that collect all the data generated by the sensing elements and centrally process the information to create real business value. In this paper, we present a system that follows the Fog Computing paradigm where the sensor resources, as well as the intermediate layers between embedded devices and cloud computing datacenters, participate by providing computational, storage, and control. We discuss the design aspects of our system and present a pilot deployment for the evaluating the performance in a real-world environment. Our findings indicate that Fog Computing can address the ever-increasing amount of data that is inherent in an IoT world by effective communication among all elements of the architecture

Performance Measures Using Electronic Health Records: Five Case Studies

Presents the experiences of five provider organizations in developing, testing, and implementing four types of electronic quality-of-care indicators based on EHR data. Discusses challenges, and compares results with those from traditional indicators

Automating Mitigation of Amplification Attacks in NFV Services

The combination of virtualization techniques with capillary computing and storage resources allows the instantiation of Virtual Network Functions throughout the network infrastructure, which brings more agility in the development and operation of network services. Beside forwarding and routing, this can be also used for additional functions, e.g., for security purposes. In this paper, we present a framework to systematically create security analytics for virtualized network services, specifically targeting the detection of cyber-attacks. Our framework largely automates the deployment of security sidecars into existing service templates and their interconnection to an external analytics platform. Notably, it leverages code augmentation techniques to dynamically inject and remove inspection probes without affecting service operation. We describe the implementation of a use case for the detection of DNS amplification attacks in virtualized 5G networks, and provide extensive evaluation of our innovative inspection and detection mechanisms. Our results demonstrate better efficiency with respect to existing network monitoring tools in terms of CPU usage, as well as good accuracy in detecting attacks even with variable traffic patterns

The Evolution of Technology in Call Centers

Historical research was conducted through literature. The report traces the evolution of technology in call centers (CCs) from their early inception to 2018. CCs are integrated into many facets of multidisciplinary areas of business, industry, and public and private institutions of higher education. Three research questions were addressed: What technologies enabled the start of CCs? How did the communications between customers and CSRs take place? What was the content of the earlier communications? How did services and communications evolve as technology matured? What are the current state-of-the-art technologies that exist in CCs? Which industries appear to have the best solutions? What are these solutions? Photograph Analysis Worksheets and Written Document Analysis Worksheets from the National Archives and Records Administration were used to analyze primary source materials. Also, used were Primary Source Analysis Tools from the Library of Congress. The final report offers a comprehensive history of the technology evolution within the industry. Included are a discussion of state-of-the-art technologies, the range of their applications and suggestions for staff training