Sensitivity based Neural Networks Explanations

Although neural networks can achieve very high predictive performance on various different tasks such as image recognition or natural language processing, they are often considered as opaque "black boxes". The difficulty of interpreting the predictions of a neural network often prevents its use in fields where explainability is important, such as the financial industry where regulators and auditors often insist on this aspect. In this paper, we present a way to assess the relative input features importance of a neural network based on the sensitivity of the model output with respect to its input. This method has the advantage of being fast to compute, it can provide both global and local levels of explanations and is applicable for many types of neural network architectures. We illustrate the performance of this method on both synthetic and real data and compare it with other interpretation techniques. This method is implemented into an open-source Python package that allows its users to easily generate and visualize explanations for their neural networks

ENTERPRISE CREDIT RISK ASSESSMENT ANALYZING THE DATA OF SHORT TERM ACTIVITY PERIOD

This research investigates the possibility to classify the companies into default and non-default groups analyzing the financial data of 1 year. The developed statistical model enables banks to predict the default of new companies that have no sufficient financial information for the credit risk assessment using other models. The classification and regression tree predicts the default of companies with the 96% probability. The complementary analysis the financial data of 2 years by probit model allows to increase the classification accuracy to 99%. DOI: https://doi.org/10.15544/ssaf.2012.2

Explainable AI for Interpretable Credit Scoring

With the ever-growing achievements in Artificial Intelligence (AI) and the recent boosted enthusiasm in Financial Technology (FinTech), applications such as credit scoring have gained substantial academic interest. Credit scoring helps financial experts make better decisions regarding whether or not to accept a loan application, such that loans with a high probability of default are not accepted. Apart from the noisy and highly imbalanced data challenges faced by such credit scoring models, recent regulations such as the `right to explanation' introduced by the General Data Protection Regulation (GDPR) and the Equal Credit Opportunity Act (ECOA) have added the need for model interpretability to ensure that algorithmic decisions are understandable and coherent. An interesting concept that has been recently introduced is eXplainable AI (XAI), which focuses on making black-box models more interpretable. In this work, we present a credit scoring model that is both accurate and interpretable. For classification, state-of-the-art performance on the Home Equity Line of Credit (HELOC) and Lending Club (LC) Datasets is achieved using the Extreme Gradient Boosting (XGBoost) model. The model is then further enhanced with a 360-degree explanation framework, which provides different explanations (i.e. global, local feature-based and local instance-based) that are required by different people in different situations. Evaluation through the use of functionallygrounded, application-grounded and human-grounded analysis show that the explanations provided are simple, consistent as well as satisfy the six predetermined hypotheses testing for correctness, effectiveness, easy understanding, detail sufficiency and trustworthiness.Comment: 19 pages, David C. Wyld et al. (Eds): ACITY, DPPR, VLSI, WeST, DSA, CNDC, IoTE, AIAA, NLPTA - 202

Loan products and credit scoring methods by commercial banks

This study describes the loan products offered by the commercial banks and credit scoring techniques used for classifying risks and granting credit to the applicants in India. The loan products offered by commercial banks are: Housing loans, Personal loans, Business loan, Education loans, Vehicle loans etc. All the loan products are categorized as secures and unsecured loans. Credit scoring techniques used for both secured as well as unsecured loans are broadly divided into two categories as Advanced Statistical Methods and Traditional Statistical Methods.peer-reviewe

Моделювання оцінювання кредитоспроможності позичальників банківських установ

У роботі досліджено сутність банківського кредитування та кредитоспроможність позичальника, проведений аналіз існуючих моделей для розгляду та дослідження кредитної політики банку, в тому числі методу CAMPARI. Наведені основні вимоги до створюваної моделі. Проведена апробація моделі та оцінка її адекватності на прикладі діяльності «Сумського НВО ім. М.В.Фрунзе».The essence of bank lending and borrower's creditworthiness has been investigated in the work, an analysis of existing models for reviewing and researching the bank's credit policy, including the CAMPARI method, has been carried out. The basic requirements for the created model are given. Approbation of the model and assessment of its adequacy on the example of PJSC «Sumy NPO» was estimated

Моделювання оцінювання кредитоспроможності позичальників банківських установ

У роботі досліджено сутність банківського кредитування та кредитоспроможність позичальника, проведений аналіз існуючих моделей для розгляду та дослідження кредитної політики банку, в тому числі методу CAMPARI. Наведені основні вимоги до створюваної моделі. Проведена апробація моделі та оцінка її адекватності на прикладі діяльності «Сумського НВО ім. М.В.Фрунзе».The essence of bank lending and borrower's creditworthiness has been investigated in the work, an analysis of existing models for reviewing and researching the bank's credit policy, including the CAMPARI method, has been carried out. The basic requirements for the created model are given. Approbation of the model and assessment of its adequacy on the example of PJSC «Sumy NPO» was estimated

A Neural Network Approach to Estimating the Allowance for Bad Debt

The granting of credit is a necessary risk of doing business. If companies only accepted cash, sales would be negatively impacted. In a perfect world, all consumers would pay their bills when they become due. However, the fact is that some consumers do default on debt. Companies are willing to accept default risk because the value of defaults does not exceed the value of the additional sales generated. This creates an issue in regards to the valuation of uncollectible accounts. In order for a company to disclose the true value of its accounts receivable, it must establish an allowance for bad debt. Traditionally, companies estimate their bad debt expense and the related allowance for doubtful account by one of two methods: 1) As a percentage of total credit sales or 2) An aging of accounts receivable (that assesses a higher likely rate of default, the older the account becomes past due). By their very nature, these methods take into account only endogenous variables based on past experiences. For many years, the aforementioned methods of estimating bad debt were the only viable ways of determining the allowance for bad debts. However, with the explosion of technology and the easy availability of information, a more comprehensive method of determining bad debts seems appropriate. Neural network computer systems, which mimic some of the characteristics of the human brain, have been developed and may offer an alternative method for estimating the allowance for bad debt. These systems can predict what events may happen, analyze what did happen, and adjust the factor weights accordingly for the next set of event predictions. Thus, it is noteworthy to explore the use of neural networks to predict what a reasonable allowance for bad debt should be for an entity based on an array of interacting variables. Since, a neural network can incorporate both endogenous and exogenous variables one would expect to use such a system to develop a tool which gives a better estimation of the allowance for bad debt than the traditional approaches. In the current study, the findings indicate that neural networks over the balance of the time are better predictors of a company’s ending allowance for bad debt than regression. On a case by case basis, even when neural networks provide a less accurate estimate than regression, statistical analyses demonstrated the neural networks are a less volatile method and their predictions are less likely to result in a significant difference from actual allowance. Neither approach provides results that are exactly the same as the actual ending balance of the allowance for bad debt amount. Even though regression provides a more accurate estimate 45 percent of the time, this result is mitigated by two items: 1) On average, the absolute difference between actual and predicted is much lower when neural networks are used and 2) The standard deviation derived when using neural networks is only a third of the standard deviation derived from regression when applied to the absolute differences between the actual and predicted allowance