An Explanation Framework for Interpretable Credit Scoring

Demajo, Lara Marie; Vella, Vince; Dingli, Alexiei; Demajo, Vince Vella Lara Marie

doi:10.5121/ijaia.2021.12102

Cited by 6 publications

(5 citation statements)

References 25 publications

(31 reference statements)

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“…The present paper deals with the XAI aspects of a real-time system different than the usual off-line systems. [15] The user-friendliness of interfaces of anti-drone systems is very important for facing invasions of large swarms of drones that unaided humans are unable to cope with due the clutter of traditional screen interfaces. The proposal of the present paper shows a direction that such research must follow for improving the human-system interface used.…”

Section: Discussionmentioning

confidence: 99%

Human-System Interface with Explanation of Actions for Autonomous Anti-UAV Systems

Kontos¹

2021

IJAIA

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Research on explanation is currently of intense interest as documented in the DARPA 2021 investments reported by the USA Department of Defense. An emerging theme for explanation techniques research is their application to the improvement of human-system interfaces for autonomous anti-drone or C-UAV defense systems. In the present paper a novel proposal based on natural language processing technology concerning explanatory discourse using relations is briefly described. The proposal is based on the use of relations pertaining to the possible malicious actions of an intruding alien drone swarm and the defense decisions proposed by an autonomous anti-drone system. The aim of such an interface is to facilitate the supervision that a user must exercise on an autonomous defense system in order to minimize the risk of wrong mitigation actions and unnecessary spending of ammunition.

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Section: Discussionmentioning

confidence: 99%

Human-System Interface with Explanation of Actions for Autonomous Anti-UAV Systems

Kontos¹

2021

IJAIA

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“…As artificial intelligence (AI) models increasingly permeate the fintech industry, there is a growing need for interpretability and explainability to ensure transparency and trust in decision-making processes. This literature review explores the existing body of research and techniques related to the generation of explanations for AI models in the fintech industry (Demajo et al, 2021).…”

Section: Methodsmentioning

confidence: 99%

“…This transparency ensures that discriminatory factors are eliminated, promoting fairness in lending. Similarly, in algorithmic trading, XAI can offer insights into why specific trades were executed, which is essential for compliance and risk management (Demajo et al, 2021).…”

Section: Role Of Xai In Fintech Industrymentioning

confidence: 99%

“…Credit assessment is the process of evaluating an individual's or a company's credit worthiness to determine their ability to repay debt obligations. It is a crucial step in the lending and borrowing process as it helps financial institutions, such as banks, credit unions, or lending agencies, make informed decisions about extending credit to borrowers (Demajo and Dingli, 2021). When a loan application is processed by a bank using AI/ML model: • If the loan application is rejected, the customer (loan applicant) wants to know the reason for the rejection.…”

Section: Credit Assessment Modelmentioning

confidence: 99%

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A framework for generating explanations of machine learning models in Fintech industry

Kalyanathaya,

2024

The Scientific Temper

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Artificial Intelligence is making significant inroads into various aspects of business and life bringing the transformation in many ways. The convolution of technology in finance is often called FINTECH rapidly growing area of transformation. In the FINTECH industry, AI can automate several financial processes and services such as fraud detection, customer services, credit assessment, price predictions, customer churning, trading services, risk management, underwriting, market forecasting. These processes and services are critical to financial sectors such as banking, insurance, currency, stock and commodity markets, wealth management, payment clearing houses, payment regulators etc. Regulations control these processes and should be transparent in their operations. AI models are inherently opaque in their outcomes and unable to be fully plugged into the financial processes and services. Explainable AI is the key area of research that can help to provide transparency to enable these AI models as fully operational business models to automate financial products and services. In this paper we will broadly outline the framework of explainable artificial intelligence (XAI) in finance sectors and services. We then look into one use case of credit assessment and develop an XAI framework to provide transparent outcomes from the AI models.

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“…For example, an XAI system could thoroughly explain why a specific credit decision was made or a specific stock was recommended for investment. Credit-based score decisions are being studied to make them more understandable [19][20][21][22][23][59][60][61][62][63][64][65][66][67].…”

Section: 3mentioning

confidence: 99%

A Systematic Literature Review of Explainable Artificial Intelligence (XAI) in Software Engineering (SE)

khan,

Ali,

Khan

et al. 2023

Preprint

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Artificial intelligence (AI) is the most advanced developing area for enhancing Machine Intelligence and replicating the intelligence of humans. In this regard, Machine Learning (ML) is used to develop algorithms and models that help machines learn from data and predict problems. Although ML models provide accurate predictions, they are frequently considered black boxes due to their lack of interpretability. This can undermine trust and acceptance of AI systems, particularly in critical domains requiring transparency and accountability, such as Healthcare. Explainable Artificial Intelligence (XAI) techniques, which have emerged to make ML models more transparent and interpretable, can address the lack of interpretability challenge. They shed light on how ML models make decisions and explain and justify the results. This builds trust and makes AI systems more accessible to implement in various industries. The proposed research study investigates how much XAI is used in Software Engineering (SE). It intends to present a comprehensive view of the most recent advancements and address challenges and future directions for further investigation. This Systematic Literature Review (SLR) investigates the application of XAI techniques in SE. It is based on empirical studies published between January 2020 and September 2022 to analyze the XAI’s overall illustration. We developed a search string and six research questions, each answered briefly. According to our SLR findings, 14 of the 131 research studies extracted from various databases addressed XAI techniques. Additionally, 14 research studies using XAI techniques in the Healthcare and Finance domains were chosen to compare with the findings of this literature review. These studies were chosen because the researchers frequently cited them. Following our findings, XAI approaches were mainly employed in the Software Fault Predictions (SFP) subdomain of SE, and all studies used local explanations. Python programming libraries were used for implementation, with “sci-kit” being the most widely used, followed by “caret” of the R programming language. In addition, the “LIME” tool is the most commonly used in the SFP domain for local explanations, followed by the “SHAP” tool. The findings also show that local and global explanations were used in the Healthcare and Finance domains. The most widely used Python programming library is the “sci-kit learn” library, with the “SHAP” tool being the most commonly used explanation tool in the Finance and Healthcare domains. Finally, whereas XAI in SE is new, XAI methods have been used in conjunction with traditional machine learning models. However, there is a lack of benchmark evaluation metrics in the existing literature, leading to researcher confusion and unreliable comparison standards.

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An Explanation Framework for Interpretable Credit Scoring

Cited by 6 publications

References 25 publications

Human-System Interface with Explanation of Actions for Autonomous Anti-UAV Systems

Human-System Interface with Explanation of Actions for Autonomous Anti-UAV Systems

A framework for generating explanations of machine learning models in Fintech industry

A Systematic Literature Review of Explainable Artificial Intelligence (XAI) in Software Engineering (SE)

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