Harnessing Collective Intelligence in P2P Lending

Dambanemuya, Henry K.; Horvát, Emőke-Ágnes

doi:10.1145/3292522.3326040

Cited by 5 publications

(5 citation statements)

References 34 publications

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“…Table 4 Quantity of papers per knowledge area Knowledge area References Qtd % Financial [5], [6], [7], [8], [9], [10], [11], [12], [13], [14], [15], [16], [17], [18], [19], [20], [3], [21], [22], [23], [24], [25], [26] 23 71.88%…”

Section: Evaluated Resultsmentioning

confidence: 99%

“…[5], Ball, Kruger and Drevin [6], Dambanemuya and Horvát. [11] and Alam and Ali [26]; • Users validation, through qualitative analysis, to prove the interpretability/explicability of the method. Examples are Jesus et al [16], La Gatta et al [30], Xia et al [32], Yuan et al [34].…”

Section: Interpretability Techniquementioning

confidence: 99%

“…[13], Patil, Framewala and Kazi [20], Payrovnaziri et al [48], Xia et al [32], Zhao et al [33], Wang et al [24] and Yuan et al [34]; • The possibility to improve the used and proposed algorithms, as can be seen in Bui-Thi, Meysman and Laukens [28], Chou [10], Chung et al [29], Dambanemuya and Horvát [11], Ball, Kruger and Drevin [6], Yang, Liu and Liu [49] and Alam and Ali [26]; • The possibility to insert new graphic components or funcionalities to the proposed work, as observed in Carta et al [7]; • The necessity of more tests with the proposed algorithm, as demonstrated in Chi [8],…”

Section: Interpretability Techniquementioning

confidence: 99%

“…Not cited [16] 1 3.12% Available [27], [6], [28], [29], [11], [13], [14], [15], [30], [31], [20], [33], [34], [26], [35] 15 46.88%…”

Section: Interpretability Techniquementioning

confidence: 99%

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Interpretability methods applied to fraud detection intelligent systems: a systematic review

Furlanetto

Baldassin

Manacero

2023

Preprint

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In a technological world, in which data is generated exponentially, financial analysis has gradually become more important to avoid large losses due to fraud. Considering the large volume and the difficulty of human data checking, machine learning technologies have become one of the main tools to solve the problem. However, due to the creation of data protection laws in several countries, in some scenarios the detection of fraud through intelligence algorithms becomes insufficient. Therefore, it is necessary to understand how the algorithm actually labels a transaction as fraudulent or not. In this work, presented as a systematic literature review, we look for answers on how explicable/interpretable fraud detection algorithms have been applied in order to solve the problem of illegal activities in the financial sector. As a result of the mapping of the current state of the art, this work highlights the gaps in the literature and present the scenario of interpretable techniques used for fraud detection comprehension.

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Section: Evaluated Resultsmentioning

confidence: 99%

Section: Interpretability Techniquementioning

confidence: 99%

Section: Interpretability Techniquementioning

confidence: 99%

“…Not cited [16] 1 3.12% Available [27], [6], [28], [29], [11], [13], [14], [15], [30], [31], [20], [33], [34], [26], [35] 15 46.88%…”

Section: Interpretability Techniquementioning

confidence: 99%

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Interpretability methods applied to fraud detection intelligent systems: a systematic review

Furlanetto

Baldassin

Manacero

2023

Preprint

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“…For the isolation paradigm, there are several applications focusing on providing intelligent services by collective decisions of independent individuals. For example, Dambanemuya and Horvát [72] aggregated personal lending behaviors and successful loan payments, which can be seen as individual behavior results, to provide reliable decision support. This can overcome P2P's weakness of information asymmetry about borrowers' credibility.…”

Section: A Applications Of the Isolation Paradigmmentioning

confidence: 99%

Collective Intelligence: A Taxonomy and Survey

et al. 2019

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Collective intelligence (CI) refers to the intelligence that emerges at the macro-level of a collection and transcends that of the individuals. CI is a continuously popular research topic that is studied by researchers in different areas, such as sociology, economics, biology, and artificial intelligence. In this survey, we summarize the works of CI in various fields. First, according to the existence of interactions between individuals and the feedback mechanism in the aggregation process, we establish CI taxonomy that includes three paradigms: isolation, collaboration and feedback. We then conduct statistical literature analysis to explain the differences among three paradigms and their development in recent years. Second, we elaborate the types of CI under each paradigm and discuss the generation mechanism or theoretical basis of the different types of CI. Third, we describe certain CI-related applications in 2019, which can be appropriately categorized by our proposed taxonomy. Finally, we summarize the future research directions of CI under each paradigm. We hope that this survey helps researchers understand the current conditions of CI and clears the directions of future research.

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A Multi-platform Study of Crowd Signals Associated with Successful Online Fundraising

Dambanemuya

Horvát

2021

Proc. ACM Hum.-Comput. Interact.

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The growing popularity of online fundraising (aka "crowdfunding") has attracted significant research on the subject. In contrast to previous studies that attempt to predict the success of crowdfunded projects based on specific characteristics of the projects and their creators, we present a more general approach that focuses on crowd dynamics and is robust to the particularities of different crowdfunding platforms. We rely on a multi-method analysis to investigate the correlates, predictive importance, and quasi-causal effects of features that describe crowd dynamics in determining the success of crowdfunded projects. By applying a multi-method analysis to a study of fundraising in three different online markets, we uncover universal crowd dynamics that ultimately decide which projects will succeed. In all analyses and across three markets, we consistently find that funders' behavioural signals (1) are significantly correlated with fundraising success; (2) approximate fundraising outcomes better than the characteristics of projects and their creators such as credit grade, company valuation, and subject domain; and (3) have significant quasi-causal effects on fundraising outcomes while controlling for potentially confounding project variables. By showing that universal features deduced from crowd behaviour are predictive of fundraising success on different crowdfunding platforms, our work provides design-relevant insights about novel types of collective decision-making online. This research inspires thus potential ways to leverage cues from the crowd and catalyses research into crowd-aware system design.

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Harnessing Collective Intelligence in P2P Lending

Cited by 5 publications

References 34 publications

Interpretability methods applied to fraud detection intelligent systems: a systematic review

Interpretability methods applied to fraud detection intelligent systems: a systematic review

Collective Intelligence: A Taxonomy and Survey

A Multi-platform Study of Crowd Signals Associated with Successful Online Fundraising

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