A Novel Cognitive Computing Technique Using Convolutional Networks for Automating the Criminal Investigation Process in Policing

Schiliro, Francesco; Beheshti, Amin; Moustafa, Nour

doi:10.1007/978-3-030-55180-3_39

Cited by 12 publications

(7 citation statements)

References 19 publications

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“…Although correct identification of pieces is easy but consistent recognition of a state is complex due to the wide variety of paper forms. Therefore, they used multiple approaches for this application based on recent advances using convolutional neural networks 17,48,70,82 . They claimed that for specific scenarios, general-purpose OCR failed, so data classification was done first to make data recovery easier.…”

Section: Id Document Classificationmentioning

confidence: 99%

An Intelligent Hybrid Model for Identity Document Classification

Khandan

2021

Preprint

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Digitization, i.e., the process of converting information into a digital format, may provide various opportunities (e.g., increase in productivity, disaster recovery, and environmentally friendly solutions) and challenges for businesses. In this context, one of the main challenges would be to accurately classify numerous scanned documents uploaded every day by customers as usual business processes. For example, processes in banking (e.g., applying for loans) or the Government Registry of BDM (Births, Deaths, and Marriages) applications may involve uploading several documents such as a driver's license and passport. There are not many studies available to address the challenge as an application of image classification. Although some studies are available which used various methods, a more accurate model is still required. The current study has proposed a robust fusion model to define the type of identity documents accurately. The proposed approach is based on two different methods in which images are classified based on their visual features and text features. A novel model based on statistics and regression has been proposed to calculate the confidence level for the feature-based classifier. A fuzzy-mean fusion model has been proposed to combine the classifier results based on their confidence score. The proposed approach has been implemented using Python and experimentally validated on synthetic and real-world datasets. The performance of the proposed model is evaluated using the Receiver Operating Characteristic (ROC) curve analysis.

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Section: Id Document Classificationmentioning

confidence: 99%

An Intelligent Hybrid Model for Identity Document Classification

Khandan

2021

Preprint

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“…Artificial intelligence (AI) enables machines to learn from human experience, adjust to new inputs, and perform human-like tasks. AI is progressing rapidly and is transforming the way businesses operate, from process automation to cognitive augmentation of tasks and intelligent process/data analytics 3,10,60 . However, the main challenge for human users would be to understand and appropriately trust the result of AI algorithms and methods.…”

Section: Problem Statementmentioning

confidence: 99%

Towards Explainable Artificial Intelligence in Banking and Financial Services

Hanif¹

2021

Preprint

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Artificial intelligence (AI) enables machines to learn from human experience, adjust to new inputs, and perform human-like tasks. AI is progressing rapidly and is transforming the way businesses operate, from process automation to cognitive augmentation of tasks and intelligent process/data analytics. However, the main challenge for human users would be to understand and appropriately trust the result of AI algorithms and methods. In this paper, to address this challenge, we study and analyze the recent work done in Explainable Artificial Intelligence (XAI) methods and tools. We introduce a novel XAI process, which facilitates producing explainable models while maintaining a high level of learning performance. We present an interactive evidence-based approach to assist human users in comprehending and trusting the results and output created by AI-enabled algorithms. We adopt a typical scenario in the Banking domain for analyzing customer transactions. We develop a digital dashboard to facilitate interacting with the algorithm results and discuss how the proposed XAI method can significantly improve the confidence of data scientists in understanding the result of AI-enabled algorithms. KEYWORDS Business Process Analytics; Explainable AI; Machine learning 1 INTRODUCTIONThis section present an overview of the completed study and explains the author's rationale to conduct this research. We will go through the problem we are addressing and discuss the impacts of the proposed approach for the business organizations. Furthermore, we present the project's contribution to research and academia to meet the goals and its response to the demands of the finance industry. OverviewIn the last decade, the world has envisioned tremendous growth in technology with the improved accessibility of data, cloud resources, and machine learning (ML) algorithms evolution. The intelligent system has achieved significant performance with this growth. The super performance of these algorithms in various domains has increased the popularity of artificial intelligence (AI). However, alongside these achievements, the non-transparent, ambiguity, and inability to expound and interpret the majority of the state-of-the-art techniques are considered ethical issues. These flaws in AI algorithms impede the acceptance of complex ML models in a variety of fields such as medical, banking and finance, security, and education and have prompted many concerns about the security and safety of ML system users. According to the current regulations and policies, these systems must be transparent to meet the right to explanation. Due to a lack of trust in existing ML-based systems, explainable artificial intelligence (XAI)-based methods are gaining popularity. Although neither the domain nor the methods are novel, they are gaining popularity due to their ability to unbox the black box.

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“…This means that these software programs are designed based on pre-defined rules and thresholds of average and standard deviation to detect Laundering transactions. In other words, the conventional approach to detecting unusual transactions is applying some rules about a specific transaction attribute or a set of them 73 . These attributes may contain transaction features such as transaction type, amount, statement, time, location, frequency, origin, and destination.…”

Section: Rule-based Modelsmentioning

confidence: 99%

Towards Artificial Intelligence Enabled Financial Crime Detection

Rouhollahi

2021

Preprint

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Recently, financial institutes have been dealing with an increase in financial crimes. In this context, financial services firms started to improve their vigilance and use new technologies and approaches to identify and predict financial fraud and crime possibilities. This task is challenging as institutions need to upgrade their data and analytics capabilities to enable new technologies such as Artificial Intelligence (AI) to predict and detect financial crimes. In this paper, we put a step towards AI-enabled financial crime detection in general and money laundering detection in particular to address this challenge. We study and analyse the recent works done in financial crime detection and present a novel model to detect money laundering cases with minimum human intervention needs.

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A Novel Cognitive Computing Technique Using Convolutional Networks for Automating the Criminal Investigation Process in Policing

Cited by 12 publications

References 19 publications

An Intelligent Hybrid Model for Identity Document Classification

An Intelligent Hybrid Model for Identity Document Classification

Towards Explainable Artificial Intelligence in Banking and Financial Services

Towards Artificial Intelligence Enabled Financial Crime Detection

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