In this paper we present a novel approach to credit scoring of retail customers in the banking industry based on deep learning methods. We used RNNs on fine grained transnational data to compute credit scores for the loan applicants. We demonstrate that our approach significantly outperforms the baselines based on the customer data of a large European bank. We also conducted a pilot study on loan applicants of the bank, and the study produced significant financial gains for the organization. In addition, our method has several other advantages described in the paper that are very significant for the bank.
Credit Scores are ubiquitous and instrumental for loan providers and regulators. In this paper we showcase how micro-loan credit system can be developed in real setting. We show what challenges arise and discuss solutions. Particularly, we are concerned about model interpretability and data quality. In the final section, we introduce semi-supervised algorithm that aids model development and evaluate its performance
Nowadays consumer loan plays an important role in promoting the economic growth, and credit cards are the most popular consumer loan. One of the most essential parts in credit cards is the credit limit management. Traditionally, credit limits are adjusted based on limited heuristic strategies, which are developed by experienced professionals. In this paper, we present a data-driven approach to manage the credit limit intelligently. Firstly, a conditional independence testing is conducted to acquire the data for building models. Based on these testing data, a response model is then built to mea…
Beijing Trusfort Technology Co.,Ltd.（北京信任度科技有限公司）；Beijing University of Posts and Telecommunications（北京邮电大学）
More personal consumer loan products are emerging in mobile banking APP. For ease of use, application process is always simple, which means that few application information is requested for user to fill when applying for a loan, which is not conducive to construct users' credit profile. Thus, the simple application process brings huge challenges to the overdue risk detection, as higher overdue rate will result in greater economic losses to the bank. In this paper, we propose a model named HUIHEN (Hierarchical User Intention-Habit Extract Network) that leverages the users' behavior information …
Data on hundreds of variables related to individual consumer finance behavior (such as credit card and loan activity) is routinely collected in many countries and plays an important role in lending decisions. We postulate that the detailed nature of this data may be used to predict outcomes in seemingly unrelated domains such as individual health. We build a series of machine learning models to demonstrate that credit report data can be used to predict individual mortality. Variable groups related to credit cards and various loans, mostly unsecured loans, are shown to carry significant predict…
In this paper, we investigate the extent to which features derived from bank statements provided by loan applicants, and which are not declared on an application form, can enhance a credit scoring model for a New Zealand lending company. Exploring the potential of such information to improve credit scoring models in this manner has not been studied previously. We construct a baseline model based solely on the existing scoring features obtained from the loan application form, and a second baseline model based solely on the new bank statement-derived features. A combined feature model is then cr…
PDx -- Adaptive Credit Risk Forecasting Model in Digital Lending using Machine Learning Operations
OM & QT Area, Indian Institute of Management Indore（印度管理学院印度尔分校OM与QT部门）；Department of Statistics & Data Science, National University of Singapore（新加坡国立大学统计与数据科学系）
University of New South Wales（新南威尔士大学）；University of Technology Sydney（悉尼科技大学）
Credit risk prediction is an effective way of evaluating whether a potential borrower will repay a loan, particularly in peer-to-peer lending where class imbalance problems are prevalent. However, few credit risk prediction models for social lending consider imbalanced data and, further, the best resampling technique to use with imbalanced data is still controversial. In an attempt to address these problems, this paper presents an empirical comparison of various combinations of classifiers and resampling techniques within a novel risk assessment methodology that incorporates imbalanced data. T…
Application of AI in Credit Risk Scoring for Small Business Loans: A case study on how AI-based random forest model improves a Delphi model outcome in the case of Azerbaijani SMEs
Unifund CCR, LLC（Unifund CCR有限责任公司）；Pamplin College of Business, Virginia Polytechnic Institute（弗吉尼亚理工学院庞普林商学院）；College of Business, University of Cincinnati（辛辛那提大学商学院）
This paper proposes a two-stage scoring approach to help lenders decide their fund allocations in the peer-to-peer (P2P) lending market. The existing scoring approaches focus on only either probability of default (PD) prediction, known as credit scoring, or profitability prediction, known as profit scoring, to identify the best loans for investment. Credit scoring fails to deliver the main need of lenders on how much profit they may obtain through their investment. On the other hand, profit scoring can satisfy that need by predicting the investment profitability. However, profit scoring comple…
Conditional Generative Modeling for Enhanced Credit Risk Management in Supply Chain Finance
School of Management, Fudan University（复旦大学管理学院）；Department of Decision Analytics and Operations, College of Business, City University of Hong Kong（城市大学商学院决策分析与运作部门）；Department of Industrial and Systems Engineering, University of Minnesota（明尼苏达大学工业与系统工程系）；The Laboratory for AI-Powered Financial Technologies（人工智能金融技术实验室）
Amirkabir University of Technology (Tehran Polytechnic)（阿米尔卡比尔理工大学（德黑兰理工大学））
Credit scoring models, which are among the most potent risk management tools that banks and financial institutes rely on, have been a popular subject for research in the past few decades. Accordingly, many approaches have been developed to address the challenges in classifying loan applicants and improve and facilitate decision-making. The imbalanced nature of credit scoring datasets, as well as the heterogeneous nature of features in credit scoring datasets, pose difficulties in developing and implementing effective credit scoring models, targeting the generalization power of classification m…
Loan default prediction is one of the most important and critical problems faced by banks and other financial institutions as it has a huge effect on profit. Although many traditional methods exist for mining information about a loan application, most of these methods seem to be under-performing as there have been reported increases in the number of bad loans. In this paper, we use an Extreme Gradient Boosting algorithm called XGBoost for loan default prediction. The prediction is based on a loan data from a leading bank taking into consideration data sets from both the loan application and th…
The University of Western Ontario（西安大略大学）；University of Basque Country（巴斯克大学）；University of Chile（智利大学）
For more than a half-century, credit risk management has used credit scoring models in each of its well-defined stages to manage credit risk. Application scoring is used to decide whether to grant a credit or not, while behavioral scoring is used mainly for portfolio management and to take preventive actions in case of default signals. In both cases, network data has recently been shown to be valuable to increase the predictive power of these models, especially when the borrower's historical data is scarce or not available. This study aims to understand the creditworthiness assessment performa…
University of Southampton（南安普顿大学）；The University of Western Ontario（西安大略大学）
Compared to consumer lending, Micro, Small and Medium Enterprise (mSME) credit risk modelling is particularly challenging, as, often, the same sources of information are not available. Therefore, it is standard policy for a loan officer to provide a textual loan assessment to mitigate limited data availability. In turn, this statement is analysed by a credit expert alongside any available standard credit data. In our paper, we exploit recent advances from the field of Deep Learning and Natural Language Processing (NLP), including the BERT (Bidirectional Encoder Representations from Transformer…
Addis Ababa Science and Technology University（亚的斯亚贝巴科技大学）；Chapa Financial Technologies（查帕金融科技公司）；Chapa AI Research (ChAIR)（查帕人工智能研究院（ChAIR））
Predicting the probability of non-performing loans for individuals has a vital and beneficial role for banks to decrease credit risk and make the right decisions before giving the loan. The trend to make these decisions are based on credit study and in accordance with generally accepted standards, loan payment history, and demographic data of the clients. In this work, we evaluate how different Machine learning models such as Random Forest, Decision tree, KNN, SVM, and XGBoost perform on the dataset provided by a private bank in Ethiopia. Further, motivated by this evaluation we explore differ…
University of California - Irvine（加州大学欧文分校）；Bank of Communications BBM（交通银行BBM）
We use machine learning techniques to investigate whether it is possible to replicate the behavior of bank managers who assess the risk of commercial loans made by a large commercial US bank. Even though a typical bank already relies on an algorithmic scorecard process to evaluate risk, bank managers are given significant latitude in adjusting the risk score in order to account for other holistic factors based on their intuition and experience. We show that it is possible to find machine learning algorithms that can replicate the behavior of the bank managers. The input to the algorithms consi…
Enhanced Credit Score Prediction Using Ensemble Deep Learning Model
University of Chicago（芝加哥大学）；Lowell University（洛厄尔大学）；University of California Berkeley（加州大学伯克利分校）；UMass University（马萨诸塞大学）；Yale University（耶鲁大学）；Purdue University（普渡大学）
Manual approvals are still used by banks and other NGOs to approve loans. It takes time and is prone to mistakes because it is controlled by a bank employee. Several fields of machine learning mining technologies have been utilized to enhance various areas of credit rating forecast. A major goal of this research is to look at current sentiment analysis techniques that are being used to generate creditworthiness.
Reject inference comprises techniques to infer the possible repayment behavior of rejected cases. In this paper, we model credit in a brand new view by capturing the sequential pattern of interactions among multiple stages of loan business to make better use of the underlying causal relationship. Specifically, we first define 3 stages with sequential dependence throughout the loan process including credit granting(AR), withdrawal application(WS) and repayment commitment(GB) and integrate them into a multi-task architecture. Inside stages, an intra-stage multi-task classification is built to me…
Explainable Artificial Intelligence Credit Risk Assessment using Machine Learning
Shreya Department of Computer Science and Engineering Specialisation in Data Science and Artificial Intelligence（Shreya 计算机科学与工程系 数据科学与人工智能专攻）；Mr. Harsh Pathak Assistant Professor Department of Computer Science and Engineering（Harsh Pathak 助理教授 计算机科学与工程系）；SRM University, Sonepat, Haryana, India（SRM 大学, Sonepat, Haryana, India）
QFDNN: A Resource-Efficient Variational Quantum Feature Deep Neural Networks for Fraud Detection and Loan Prediction
Department of Physics, Indian Institute of Science Education and Research（印度科学教育与研究学院物理系）；Bikash’s Quantum (OPC) Pvt. Ltd.（比克ash量子（OPC）私人有限公司）；Nottingham Trent University（诺丁汉特伦特大学）；Qatar Center for Quantum Computing, College of Science and Engineering（卡塔尔量子计算中心，科学与工程学院）；Hamad Bin Khalifa University（哈马德·本·卡西姆大学）；Department of Computer Science, Faculty of Computers and Artificial Intelligence（计算机科学系，计算机与人工智能学院）
CREST-ENSAI；LARODEC-ISG；ESSEC, Université de Tunis（突尼斯大学ESSEC）
The credit scoring risk management is a fast growing field due to consumer's credit requests. Credit requests, of new and existing customers, are often evaluated by classical discrimination rules based on customers information. However, these kinds of strategies have serious limits and don't take into account the characteristics difference between current customers and the future ones. The aim of this paper is to measure credit worthiness for non customers borrowers and to model potential risk given a heterogeneous population formed by borrowers customers of the bank and others who are not. We…
Does machine learning and AI ensure that social biases thrive ? This paper aims to analyse this issue. Indeed, as algorithms are informed by data, if these are corrupted, from a social bias perspective, good machine learning algorithms would learn from the data provided and reverberate the patterns learnt on the predictions related to either the classification or the regression intended. In other words, the way society behaves whether positively or negatively, would necessarily be reflected by the models. In this paper, we analyse how social biases are transmitted from the data into banks loan…
A Multimodal Approach to SME Credit Scoring Integrating Transaction and Ownership Networks
Department of Statistical and Actuarial Sciences, Western University（西敏大学统计与精算科学系）；University of Southampton Business School（南安普顿大学商学院）；Centre for Operational Research, Management Sciences and Information Systems（运营研究、管理科学与信息系统中心）；School of Mathematical Sciences, University of Southampton（南安普顿大学数学科学学院）；Department of Computer Science, Reykjavík University（雷克雅未克大学计算机科学系）
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；Carnegie Mellon University（卡内基梅隆大学）；Ant Group（蚂蚁集团）
In financial credit scoring, loan applications may be approved or rejected. We can only observe default/non-default labels for approved samples but have no observations for rejected samples, which leads to missing-not-at-random selection bias. Machine learning models trained on such biased data are inevitably unreliable. In this work, we find that the default/non-default classification task and the rejection/approval classification task are highly correlated, according to both real-world data study and theoretical analysis. Consequently, the learning of default/non-default can benefit from rej…
