A Proxy-Information Bottleneck Strategy for Cognitive Credit Risk Detection.
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| Title: | A Proxy-Information Bottleneck Strategy for Cognitive Credit Risk Detection. |
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| Authors: | Chiappino, Simone1 chiappino.simone@gmail.com |
| Source: | Engineering Letters. Feb2026, Vol. 34 Issue 2, p576-590. 15p. |
| Subjects: | Artificial intelligence, Credit analysis, Real-time computing, Financial services industry, Anomaly detection (Computer security), Dynamical systems, Feature selection |
| Abstract: | The digital transformation of financial services is rapidly increasing the volume and complexity of data that banks must process to deliver personalized and secure digital experiences. As banking services become increasingly digitized and remote, bridging the gap between trustworthy client information and effective fraud prevention remains a critical challenge. While artificial intelligence (AI) has proven effective in datadriven analytics, insights from cognitive science suggest that anomaly detection--including credit risk--can benefit from adaptive, context-aware mechanisms inspired by human cognition. Building on the Cognitive Dynamic Systems (CDS) framework of Haykin and Fuster, this paper introduces a Cognitive Node (CN) as a core element of a broader bio-inspired architecture for intelligent and trust-oriented financial ecosystems. The proposed model integrates a proxy Information Bottleneck (IB) principle within the CN (referred to as IB-CN), enabling the dynamic selection of the most relevant representations for credit risk detection. By optimizing an IB-driven objective, the system balances the trade-off between feature informativeness and predictive efficiency, thereby enhancing the identification of salient data. Experimental results show that the IB-CN improves truepositive detection while reducing false alarms in real-world credit risk scenarios. The model selectively discards noninformative features without sacrificing accuracy and consistently outperforms state-of-the-art machine learning baselines. These properties make the IB-CN particularly suitable for realtime credit-risk applications that require adaptive, contextsensitive decision-making and trust-aware risk strategies. [ABSTRACT FROM AUTHOR] |
| Copyright of Engineering Letters is the property of International Association of Engineers (IAENG) and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 191342740 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: A Proxy-Information Bottleneck Strategy for Cognitive Credit Risk Detection. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Chiappino%2C+Simone%22">Chiappino, Simone</searchLink><relatesTo>1</relatesTo><i> chiappino.simone@gmail.com</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Engineering+Letters%22">Engineering Letters</searchLink>. Feb2026, Vol. 34 Issue 2, p576-590. 15p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Credit+analysis%22">Credit analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Real-time+computing%22">Real-time computing</searchLink><br /><searchLink fieldCode="DE" term="%22Financial+services+industry%22">Financial services industry</searchLink><br /><searchLink fieldCode="DE" term="%22Anomaly+detection+%28Computer+security%29%22">Anomaly detection (Computer security)</searchLink><br /><searchLink fieldCode="DE" term="%22Dynamical+systems%22">Dynamical systems</searchLink><br /><searchLink fieldCode="DE" term="%22Feature+selection%22">Feature selection</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: The digital transformation of financial services is rapidly increasing the volume and complexity of data that banks must process to deliver personalized and secure digital experiences. As banking services become increasingly digitized and remote, bridging the gap between trustworthy client information and effective fraud prevention remains a critical challenge. While artificial intelligence (AI) has proven effective in datadriven analytics, insights from cognitive science suggest that anomaly detection--including credit risk--can benefit from adaptive, context-aware mechanisms inspired by human cognition. Building on the Cognitive Dynamic Systems (CDS) framework of Haykin and Fuster, this paper introduces a Cognitive Node (CN) as a core element of a broader bio-inspired architecture for intelligent and trust-oriented financial ecosystems. The proposed model integrates a proxy Information Bottleneck (IB) principle within the CN (referred to as IB-CN), enabling the dynamic selection of the most relevant representations for credit risk detection. By optimizing an IB-driven objective, the system balances the trade-off between feature informativeness and predictive efficiency, thereby enhancing the identification of salient data. Experimental results show that the IB-CN improves truepositive detection while reducing false alarms in real-world credit risk scenarios. The model selectively discards noninformative features without sacrificing accuracy and consistently outperforms state-of-the-art machine learning baselines. These properties make the IB-CN particularly suitable for realtime credit-risk applications that require adaptive, contextsensitive decision-making and trust-aware risk strategies. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Engineering Letters is the property of International Association of Engineers (IAENG) and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 15 StartPage: 576 Subjects: – SubjectFull: Artificial intelligence Type: general – SubjectFull: Credit analysis Type: general – SubjectFull: Real-time computing Type: general – SubjectFull: Financial services industry Type: general – SubjectFull: Anomaly detection (Computer security) Type: general – SubjectFull: Dynamical systems Type: general – SubjectFull: Feature selection Type: general Titles: – TitleFull: A Proxy-Information Bottleneck Strategy for Cognitive Credit Risk Detection. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Chiappino, Simone IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 02 Text: Feb2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 1816093X Numbering: – Type: volume Value: 34 – Type: issue Value: 2 Titles: – TitleFull: Engineering Letters Type: main |
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