Multi process prediction model for customer behaviour analysis

Kalaivani, D.; Arunkumar, T.

doi:10.1504/ijwbc.2018.090918

Cited by 3 publications

(2 citation statements)

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“…They presented a method to estimate customer internet usage for online shopping activities and the amount customers spend online. Then, for analyzing customer behavior, the regression method is applied [54].…”

Section: Related Workmentioning

confidence: 99%

Customer Behaviour Analysis to Improve Detection of Fraudulent Transactions Using Deep Learning

Baratzadeh

Hasheminejad

2022

Preprint

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With the advancement of technology, the daily use of bank credit cards has been increasing exponentially. Therefore, the fraudulent use of credit cards by others as one of the new crimes is also growing fast. For this reason, detecting and preventing these attacks has become an active area of study. This article discusses the challenges of detecting fraudulent banking transactions and presents solutions based on deep learning. Transactions are examined and compared with other traditional models in fraud detection. According to the results obtained, optimal performance is related to the combined model of deep convolutional networks and short-term memory, which is trained using the aggregated data received from the generative adversarial network. This paper intends to produce sensible data to address the unequal class distribution problem, which is far more effective than traditional methods. Also, it uses the strengths of the two approaches by combining deep convolutional network and Long Short Term Memory network to improve performance. Due to the inefficiency of evaluation criteria such as accuracy in this application, the measure of distance score and the equal error rate has been used to evaluate models more transparent and more precise. Traditional methods were compared to the proposed approach to evaluate the efficiency of the experiment.

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Section: Related Workmentioning

confidence: 99%

Customer Behaviour Analysis to Improve Detection of Fraudulent Transactions Using Deep Learning

Baratzadeh

Hasheminejad

2022

Preprint

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“…For instance, a CoP initiative that involves customer feedback or sharing online shopping experience could be measured. Online behavior analysis of consumers could be performed using appropriate mathematical models and metrics [65]. This way KM performance measurements can be aligned to measure the core business objectives to realise the benefits of CoPs.…”

Section: Recommendationmentioning

confidence: 99%

Communities of Practice Approach for Knowledge Management Systems

Venkatraman¹,

Venkatraman

2018

Systems

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In this digital world, organisations are facing global competition as well as manpower pressures leading towards the knowledge economy, which heavily impacts on their local and international businesses. The trend is to foster collaboration and knowledge sharing to cope with these problems. With the advancement of technologies and social engineering that can connect people in the virtual world across time and distance, several organisations are embarking on knowledge management (KM) systems, implementing a community of practice (CoP) approach. However, virtual communities are relatively new paradigms, and there are several challenges to their successful implementation from an organisation’s point of interest. There is lack of CoP implementation framework that can cater to today’s dynamic business and sustainability requirements. To fill the gap in literature, this paper develops a practical framework for a CoP implementation with a view to align KM strategy with business strategy of an organization. It explores the different steps of building, sharing, and using tacit and explicit knowledge in CoPs by applying the Wiig KM cycle. It proposes a practical CoP implementation framework that adopts the Benefits, Tools, Organisation, People and Process (BTOPP) model in addressing the key questions surrounding each of the BTOPP elements with a structured approach. Finally, it identifies key challenges such as organizational culture and performance measurements, and provides practical recommendations to overcome them for a successful CoP implementation.

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