Mechanism and Model for Decision-Making in Credit Risk Management

Orlova, Ekaterina V.

doi:10.2991/ahcs.k.191206.007

Cited by 2 publications

(6 citation statements)

References 6 publications

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“…Credit approval forecast is assessed and evaluated in [18]. Credit risk management makes changes in the modeling and application of business management decision-making [19]. The credit management practice of banking authorities is discussed in [20,21].…”

Section: Review Of Credit Management Policiesmentioning

confidence: 99%

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Quantitative Planning Credit Policy for Business Management

STOILOV,

Stoilova

2023

Preprint

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This study considers the values of the credit payments that are made at random values for both payment size and number. The simultaneous existence of the two random variables increases the total value of the volatility of the payment process. The dependence for the simultaneous influence of the two random variables is derived by applying conditional probabilistic formulations. Formal relations apply to animal husbandry credit payments. The credit policy was analyzed for the cases of regular payments and with a stochastic number of payments. A recommendation for the upper level of credit payments is proposed based on a model predictive approach using historical payment data. A credit management algorithm is derived, in which decision-making for credit payments is formally derived and numerically discussed. The application of derived quantitative relations is empirically applied to real data on animal husbandry. The recommendations from the dependencies obtained allow a reduction in the mean values of credit payments, which correspond to a reduction in the credits used for business management, without a change in business policy.

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Section: Review Of Credit Management Policiesmentioning

confidence: 99%

“…Now relation (19) is further developed by replacing the random argument (T | N) with the random mean E(T | N). The resulting substitution gives the equality…”

Section: 𝐸(𝑁) = 𝐸[𝐸( 𝑁 | 𝐿 )]mentioning

confidence: 99%

Quantitative Planning Credit Policy for Business Management

STOILOV,

Stoilova

2023

Preprint

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“…Clustering was executed in two stages: qualitative analysis using hierarchical methods and analysis using the k-means method [32,47]. Exploratory analysis to find out the possible number of groups was conducted by the hierarchical classification method.…”

Section: Model For Borrower Clusteringmentioning

confidence: 99%

“…For that, we used the k-means method. The k-means algorithm is applicable to clustering only numeric data [47]. If there are categorical (qualitative) variables in the initial data, modifications of this algorithm are used, such as the k-modes and k-prototypes algorithm [65,67].…”

Section: Model For Borrower Clusteringmentioning

confidence: 99%

“…The analysis of existing methodological approaches and analytical tools showed that existing models for credit risk assessment do not allow for revealing trends in customer behavior with a similar economic profile [27,47]. The formation of such homogeneous borrower groups will allow, on the one hand, for identifying general behavior patterns of borrowers in diffrent groups, and on the other, for designing a system of heterogeneous conditions for borrowers in different groups, including credit value, interest rates, and others.…”

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confidence: 99%

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Methodology and Models for Individuals’ Creditworthiness Management Using Digital Footprint Data and Machine Learning Methods

Orlova

2021

Mathematics

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This research deals with the challenge of reducing banks’ credit risks associated with the insolvency of borrowing individuals. To solve this challenge, we propose a new approach, methodology and models for assessing individual creditworthiness, with additional data about borrowers’ digital footprints to implement comprehensive analysis and prediction of a borrower’s credit profile. We suggest a model for borrowers’ clustering based on the method of hierarchical clustering and the k-means method, which groups actual borrowers having similar creditworthiness and similar credit risks into homogeneous clusters. We also design the model for borrowers’ classification based on the stochastic gradient boosting (SGB) method, which reliably determines the cluster number and therefore the risk level for a new borrower. The developed models are the basis for decision making regarding the decision about lending value, interest rates and lending terms for each risk-homogeneous borrower’s group. The modified version of the methodology for assessing individual creditworthiness is presented, which is to reduce the credit risks and to increase the stability and profitability of financial organizations.

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Mechanism and Model for Decision-Making in Credit Risk Management

Cited by 2 publications

References 6 publications

Quantitative Planning Credit Policy for Business Management

Quantitative Planning Credit Policy for Business Management

Methodology and Models for Individuals’ Creditworthiness Management Using Digital Footprint Data and Machine Learning Methods

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