Credit risk analysis for credit card applicants

Credit Risk analysis by using Python and ML

Modeled the credit risk associated with consumer loans. Performed exploratory data analysis (EDA), preprocessing of continuous and discrete variables using various techniques depending on the feature. Checked for missing values and cleaned the data. Built the probability of default model using Logistic Regression. Visualized all the results. Computed Weight of Evidence and price elasticities.

An ANN is a model based on a collection of connected units or nodes called "artificial neurons", which loosely model the neurons in a biological brain. Each connection, like the synapses in a biological brain, can transmit information, a "signal", from one artificial neuron to another. An artificial neuron that receives a signal can process it and then signal additional artificial neurons connected to it. In common ANN implementations, the signal at a connection between artificial neurons is a real number, and the output of each artificial neuron is computed by some non-linear function of the sum of its inputs. The connections between artificial neurons are called "edges". Artificial neurons and edges typically have a weight that adjusts as learning proceeds. The weight increases or decreases the strength of the signal at a connection. Artificial neurons may have a threshold such that the signal is only sent if the aggregate signal crosses that threshold. Typically, artificial neurons are aggregated into layers. Different layers may perform different kinds of transformations on their inputs. Signals travel from the first layer (the input layer) to the last layer (the output layer), possibly after traversing the layers multiple times. The original goal of the ANN approach was to solve problems in the same way that a human brain would. However, over time, attention moved to performing specific tasks, leading to deviations from biology. Artificial neural networks have been used on a variety of tasks, including computer vision, speech recognition, machine translation, social network filtering, playing board and video games and medical diagnosis. Deep learning consists of multiple hidden layers in an artificial neural network. This approach tries to model the way the human brain processes light and sound into vision and hearing. Some successful applications of deep learning are computer vision and speech recognition.[68] Decision trees Main article: Decision tree learning Decision tree learning uses a decision tree as a predictive model to go from observations about an item (represented in the branches) to conclusions about the item's target value (represented in the leaves). It is one of the predictive modeling approaches used in statistics, data mining, and machine learning. Tree models where the target variable can take a discrete set of values are called classification trees; in these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels. Decision trees where the target variable can take continuous values (typically real numbers) are called regression trees. In decision analysis, a decision tree can be used to visually and explicitly represent decisions and decision making. In data mining, a decision tree describes data, but the resulting classification tree can be an input for decision making. Support vector machines Main article: Support vector machines Support vector machines (SVMs), also known as support vector networks, are a set of related supervised learning methods used for classification and regression. Given a set of training examples, each marked as belonging to one of two categories, an SVM training algorithm builds a model that predicts whether a new example falls into one category or the other.[69] An SVM training algorithm is a non-probabilistic, binary, linear classifier, although methods such as Platt scaling exist to use SVM in a probabilistic classification setting. In addition to performing linear classification, SVMs can efficiently perform a non-linear classification using what is called the kernel trick, implicitly mapping their inputs into high-dimensional feature spaces. Illustration of linear regression on a data set. Regression analysis Main article: Regression analysis Regression analysis encompasses a large variety of statistical methods to estimate the relationship between input variables and their associated features. Its most common form is linear regression, where a single line is drawn to best fit the given data according to a mathematical criterion such as ordinary least squares. The latter is often extended by regularization (mathematics) methods to mitigate overfitting and bias, as in ridge regression. When dealing with non-linear problems, go-to models include polynomial regression (for example, used for trendline fitting in Microsoft Excel[70]), logistic regression (often used in statistical classification) or even kernel regression, which introduces non-linearity by taking advantage of the kernel trick to implicitly map input variables to higher-dimensional space. Bayesian networks Main article: Bayesian network A simple Bayesian network. Rain influences whether the sprinkler is activated, and both rain and the sprinkler influence whether the grass is wet. A Bayesian network, belief network, or directed acyclic graphical model is a probabilistic graphical model that represents a set of random variables and their conditional independence with a directed acyclic graph (DAG). For example, a Bayesian network could represent the probabilistic relationships between diseases and symptoms. Given symptoms, the network can be used to compute the probabilities of the presence of various diseases. Efficient algorithms exist that perform inference and learning. Bayesian networks that model sequences of variables, like speech signals or protein sequences, are called dynamic Bayesian networks. Generalizations of Bayesian networks that can represent and solve decision problems under uncertainty are called influence diagrams. Genetic algorithms Main article: Genetic algorithm A genetic algorithm (GA) is a search algorithm and heuristic technique that mimics the process of natural selection, using methods such as mutation and crossover to generate new genotypes in the hope of finding good solutions to a given problem. In machine learning, genetic algorithms were used in the 1980s and 1990s.[71][72] Conversely, machine learning techniques have been used to improve the performance of genetic and evolutionary algorithms.[73] Training models Usually, machine learning models require a lot of data in order for them to perform well. Usually, when training a machine learning model, one needs to collect a large, representative sample of data from a training set. Data from the training set can be as varied as a corpus of text, a collection of images, and data collected from individual users of a service. Overfitting is something to watch out for when training a machine learning model. Federated learning Main article: Federated learning Federated learning is an adapted form of distributed artificial intelligence to training machine learning models that decentralizes the training process, allowing for users' privacy to be maintained by not needing to send their data to a centralized server. This also increases efficiency by decentralizing the training process to many devices. For example, Gboard uses federated machine learning to train search query prediction models on users' mobile phones without having to send individual searches back to Google.[74] Applications There are many applications for machine learning, including: Agriculture Anatomy Adaptive websites Affective computing Banking Bioinformatics Brain–machine interfaces Cheminformatics Citizen science Computer networks Computer vision Credit-card fraud detection Data quality DNA sequence classification Economics Financial market analysis[75] General game playing Handwriting recognition Information retrieval Insurance Internet fraud detection Linguistics Machine learning control Machine perception Machine translation Marketing Medical diagnosis Natural language processing Natural language understanding Online advertising Optimization Recommender systems Robot locomotion Search engines Sentiment analysis Sequence mining Software engineering Speech recognition Structural health monitoring Syntactic pattern recognition Telecommunication Theorem proving Time series forecasting User behavior analytics In 2006, the media-services provider Netflix held the first "Netflix Prize" competition to find a program to better predict user preferences and improve the accuracy of its existing Cinematch movie recommendation algorithm by at least 10%. A joint team made up of researchers from AT&T Labs-Research in collaboration with the teams Big Chaos and Pragmatic Theory built an ensemble model to win the Grand Prize in 2009 for $1 million.[76] Shortly after the prize was awarded, Netflix realized that viewers' ratings were not the best indicators of their viewing patterns ("everything is a recommendation") and they changed their recommendation engine accordingly.[77] In 2010 The Wall Street Journal wrote about the firm Rebellion Research and their use of machine learning to predict the financial crisis.[78] In 2012, co-founder of Sun Microsystems, Vinod Khosla, predicted that 80% of medical doctors' jobs would be lost in the next two decades to automated machine learning medical diagnostic software.[79] In 2014, it was reported that a machine learning algorithm had been applied in the field of art history to study fine art paintings and that it may have revealed previously unrecognized influences among artists.[80] In 2019 Springer Nature published the first research book created using machine learning.[81] Limitations Although machine learning has been transformative in some fields, machine-learning programs often fail to deliver expected results.[82][83][84] Reasons for this are numerous: lack of (suitable) data, lack of access to the data, data bias, privacy problems, badly chosen tasks and algorithms, wrong tools and people, lack of resources, and evaluation problems.[85] In 2018, a self-driving car from Uber failed to detect a pedestrian, who was killed after a collision.[86] Attempts to use machine learning in healthcare with the IBM Watson system failed to deliver even after years of time and billions of dollars invested.[87][88] Bias Main article: Algorithmic bias Machine learning approaches in particular can suffer from different data biases. A machine learning system trained on current customers only may not be able to predict the needs of new customer groups that are not represented in the training data. When trained on man-made data, machine learning is likely to pick up the same constitutional and unconscious biases already present in society.[89] Language models learned from data have been shown to contain human-like biases.[90][91] Machine learning systems used for criminal risk assessment have been found to be biased against black people.[92][93] In 2015, Google photos would often tag black people as gorillas,[94] and in 2018 this still was not well resolved, but Google reportedly was still using the workaround to remove all gorillas from the training data, and thus was not able to recognize real gorillas at all.[95] Similar issues with recognizing non-white people have been found in many other systems.[96] In 2016, Microsoft tested a chatbot that learned from Twitter, and it quickly picked up racist and sexist language.[97] Because of such challenges, the effective use of machine learning may take longer to be adopted in other domains.[98] Concern for fairness in machine learning, that is, reducing bias in machine learning and propelling its use for human good is increasingly expressed by artificial intelligence scientists, including Fei-Fei Li, who reminds engineers that "There’s nothing artificial about AI...It’s inspired by people, it’s created by people, and—most importantly—it impacts people. It is a powerful tool we are only just beginning to understand, and that is a profound responsibility.”[99] Model assessments Classification of machine learning models can be validated by accuracy estimation techniques like the holdout method, which splits the data in a training and test set (conventionally 2/3 training set and 1/3 test set designation) and evaluates the performance of the training model on the test set. In comparison, the K-fold-cross-validation method randomly partitions the data into K subsets and then K experiments are performed each respectively considering 1 subset for evaluation and the remaining K-1 subsets for training the model. In addition to the holdout and cross-validation methods, bootstrap, which samples n instances with replacement from the dataset, can be used to assess model accuracy.[100] In addition to overall accuracy, investigators frequently report sensitivity and specificity meaning True Positive Rate (TPR) and True Negative Rate (TNR) respectively. Similarly, investigators sometimes report the false positive rate (FPR) as well as the false negative rate (FNR). However, these rates are ratios that fail to reveal their numerators and denominators. The total operating characteristic (TOC) is an effective method to express a model's diagnostic ability. TOC shows the numerators and denominators of the previously mentioned rates, thus TOC provides more information than the commonly used receiver operating characteristic (ROC) and ROC's associated area under the curve (AUC).[101] Ethics Machine learning poses a host of ethical questions. Systems which are trained on datasets collected with biases may exhibit these biases upon use (algorithmic bias), thus digitizing cultural prejudices.[102] For example, using job hiring data from a firm with racist hiring policies may lead to a machine learning system duplicating the bias by scoring job applicants against similarity to previous successful applicants.[103][104] Responsible collection of data and documentation of algorithmic rules used by a system thus is a critical part of machine learning. Because human languages contain biases, machines trained on language corpora will necessarily also learn these biases.[105][106] Other forms of ethical challenges, not related to personal biases, are more seen in health care. There are concerns among health care professionals that these systems might not be designed in the public's interest but as income-generating machines. This is especially true in the United States where there is a long-standing ethical dilemma of improving health care, but also increasing profits. For example, the algorithms could be designed to provide patients with unnecessary tests or medication in which the algorithm's proprietary owners hold stakes. There is huge potential for machine learning in health care to provide professionals a great tool to diagnose, medicate, and even plan recovery paths for patients, but this will not happen until the personal biases mentioned previously, and these "greed" biases are addressed.[107] Hardware Since the 2010s, advances in both machine learning algorithms and computer hardware have led to more efficient methods for training deep neural networks (a particular narrow subdomain of machine learning) that contain many layers of non-linear hidden units.[108] By 2019, graphic processing units (GPUs), often with AI-specific enhancements, had displaced CPUs as the dominant method of training large-scale commercial cloud AI.[109] OpenAI estimated the hardware compute used in the largest deep learning projects from AlexNet (2012) to AlphaZero (2017), and found a 300,000-fold increase in the amount of compute required, with a doubling-time trendline of 3.4 months.[110][111] Software Software suites containing a variety of machine learning algorithms include the following: Free and open-source so

No description available

Application to finance

The aim is to understand which are the key factors for a certain level of credit risk to occur. In addition, some ML models capable to predict the credit risk level for a company in an year - given past years data - have been built and compared.

Analysis in Niuwa Credit Risk

This credit risk modeling project for a modest-scale finance company encompasses the entire process, from data collection and exploratory data analysis (EDA) to deployment.

Credit Risk Analysis with Machine Learning

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Credit Risk Analysis using Machine Learning models

Predicting how much loan will be approved

A Data Science/Machine Learning Project. According to Bolster , Global Fraud Index (as at June 2022) is at 10,183 and growing. This is high risk to businesses and customers transacting online. This indicates that traditional rules-based methods of detecting and combating fraud are fast becoming less effective. It becomes imperative for stakeholders to develop innovative means to make transacting online as safe as possible. Artificial intelligence provides viable and efficient solutions via Machine Learning models/algorithms. In this project, I trained a fraud detection model to predict online payment fraud using Blossom Bank PLC as case study. Blosssom Bank ( BB PLC) is a multinational financial services group, that offers retail and investment banking, pension management, assets management and payment services, headquartered in London, UK. Blossom Bank wants to build a machine learning model to predict online payment fraud. Here is the dataset used for this task. With this model, BB PLC will: Keep up with fast evolving technological threats and better prevent the loss of funds (profit) to fraudsters. Accurately detect and identify anomalies in managing online transactions done on its platforms which may go undetected using traditional rules-based methods. 3.Improve quality assurance thus retaining old customers and acquire new ones. This will increase credit/profit base. Improve its policy and decision making. Steps: 1.Loading necessary python libraries. Loading Dataset. Exploratory Data Analysis. Higlighting Relationships and insights. Data Transformation; Using resampling techniques to address Class-imbalace.. Feature Engineering. Model Training. Model Evaluation. Challenges: I encountered a number of challenges during coding which made me run into error reports. these were due to improper documentations, syntax, especially during feature engineering (one-hot encoding: 'fit.transform'). This aspect consumed most of my time I was able to solve these challenges by making extensive research and paying close attention to syntax. I was able to selve the encoding by using 'pd.get_dummies() and making some specifications in the methods.

Explore integration between Watson Studio and Cognos Analytics

Reverse engineering of the FICO algorithm

The project involved developing a credit risk default model on Indian companies using the performance data of several companies to predict whether a company is going to default on upcoming loan payments.

Credito - Credit Risk Analysis using XGBoost Classifier with RandomizedSearchCV for loan approval decisions.

Predicting the ability of a borrower to pay back the loan through Traditional Machine Learning Models and comparing to Ensembling Methods

Predicting the ability of a borrower to pay back the loan through Machine Learning Models. Logistic regression, decision tree, XGBoost, and LightGBM models are employed to predict a loan default.

Análise de risco de crédito (Credit risk analysis project).

Project from TATA job simulation program from Forage "Exploratory Data Analysis (EDA) and risk factor identification for Geldium's credit delinquency prediction model."

Decentralized Finance & It's use cases- DeFi (Decentralized Finance) Another open-world approach to the current financial system. Products that allow you to borrow, save, invest, trade, and more. Based on open source technology anyone can plan with. DeFi is an open and global financial system that has been built for years - another way of being a sharp, tightly managed, and cohesive system of decades-old infrastructure and processes. It gives you more control and visibility than your money. It gives you exposure to global markets and other options for your local currency or banking options. DeFi products open financial services to anyone with an internet connection and are highly managed and maintained by their users. To date, tens of billions of dollars worth of crypto have gone through DeFi applications and is growing every day. What is DeFi? DeFi is an integrated name for financial products and services accessible to anyone who can use Ethereum - anyone with an Internet connection. With DeFi, markets remain open and no central authorities can block payments or deny you access to anything. Services that used to be slow and vulnerable to human error are now automated and secure as they are governed by a code that anyone can check and evaluate. There is a thriving crypto-economy out there, where you can borrow, borrow, length / short, earn interest, and more. Crypto-savvy Argentinians have used DeFi to escape inflation. Companies have begun distributing their pay to their employees in real-time. Some people even withdraw and repay loans worth millions of dollars without the need for personal information. DeFi vs Traditional Finance One of the best ways to see the power of DeFi is to understand the problems that exist today. Some people are not given access to setting up a bank account or using financial services. Lack of access to financial services can prevent people from being employed. Financial services can prevent you from paying. Hidden payment for financial services is your data. Governments and private institutions can close markets at will. Trading hours are usually limited to one-hour business hours. Transfers may take days due to personal processes. There is a premium for financial services because mediation institutions require their cutting. DeFi Use Cases DeFi has revolutionized the financial world over the past few years. This new approach to financial planning can transcend asset systems through efficiency and security. It is true that there are certain dangers in DeFi but those are within the concrete limits. Let's take a look at the most effective DeFi usage cases - Asset Management One of DeFi's biggest effects is that users can now enjoy more control over their assets. Many DeFi projects provide solutions that allow users to manage their assets, including - buying, selling, and transferring digital assets. Therefore, users can also earn interest on their digital assets. Contrary to the traditional financial system, DeFi allows users to maintain the privacy of their sensitive information. Think of the secret keys or passwords of your financial accounts - you should have shared that information with the appropriate organizations beforehand. Now, different DeFi projects, such as Metamask, Argent, or Gnosis Safe help users encrypt and store those pieces of information on their devices. This ensures that only users have access to their accounts and can manage their assets. Therefore, asset management is one of the most widely used financial services cases for users. Compliance with AML and CFT Rates through the KYT Mechanism Traditional financial systems focus heavily on Know-Your-Customer (KYC) agreements. KYC Guidelines are its major law enforcement tool for using Anti-Money Laundering (AML) and Countering-the-Financing-of-Terrorism (CFT) standards. However, KYC guidelines often conflict with DeFi's privacy efforts. DeFi responds to this problem with a new concept called the Know-Your-Transaction (KYT) mechanism. This approach suggests that low-level infrastructure will focus on ethical behavior for digital addresses rather than user considerations. Therefore, KYT solves two issues simultaneously - monitoring real-time operations and ensuring user privacy. This makes KYT one of the biggest gaps in low-cost cases. Non-Governmental Organizations or DAOs The DAOs are partners of the central financial institutions of DeFi - making it one of the pillars of low-income finance cases. In the traditional system, central financial institutions play a major role. These organizations operate as administrative institutions that regulate basic financial operations, such as monetization, asset management, administrative utilization, etc. The Ethereum blockchain echerestem has introduced empowered organizations to achieve the same goals. However, DAOs are naturally empowered and do not conform to the limits set by central governments or authorities. Analysis and Risk Tools Transparency and redistribution of world power have opened the way for the discovery and analysis of unprecedented user data. With access to this information, users can make informed business decisions, discover new financial opportunities, and implement better risk management strategies. A new type of data analytics with useful blockchain tools and dashboards has emerged in this industry trend. DeFi projects such as DeFi Pulse or CoDeFi Data bring an impressive amount of analytics and risk management tool. Now, businesses are moving faster as they enjoy unpredictable competitive advantages. This is certainly one of the most widely used financial cases. Receivables and Manufacturing Goods Smart contracts allow for the receipt of token receipts and have become one of the most distinctive scenarios for DeFi use. Making a token further means setting a contract value based on the underlying financial asset or set of assets. This underlying financial asset acts as a security measure, which means it can include - bonds, fiat currencies, commodities, market indicators, interest rates, or stock prices. Now, the issuance of outgoing tokens is a secondary security and their value varies with the number of key securities (bonds or fiat money). Thus, the output actually creates artificial goods. Synthetix and dYdX are some of the leading DeFi projects focused on token acquisitions. Network Infrastructure Effect In a DeFi ecosystem, objects within the system can connect and interact. This design feature is known as integration and serves as a protocol for infrastructure development. As a result, DeFi projects are continuously integrated with the network result. Infrastructure tools for use of DeFi applications are remarkable. Various DeFi projects, such as TruffleSuite or InfuraAPI, are good examples in this case. Enhanced Digital ID Blockchain-based identity system systems are already gaining a lot of attention in recent times. Pairing DeFi programs with these patent systems can help people access the global economic system. The traditional method rewards personal income or assets collected as credit providers. With digital identity paired with DeFi, you may be looking for other practical attributes, such as - financial services or professional ability. This new type of digital ID can help the poor to access DeFi apps from any internet connection. It can certainly be one of the cases of possible use. Insurance Insurance is one of the largest financial institutions and has already been proven to be one of the biggest charges for using DeFi. The current insurance system is crowded with paperwork, old audit plans, and bureaucratic insurance claim processes. With the successful implementation of smart contracts, all these problems with the current system can be solved. Many DeFi projects (Nexus Mutual, Opyn, and VouchForMe) provide blockchain access to insurance against DeFi or contract risk. P2P borrowing and borrowing As DeFi bids farewell to traditional banking systems, a space for the lending and lending market has emerged. Therefore, borrowing and lending is one of the most important aspects of using DeFi. However, the DeFi ecosystem is well suited for peer-to-peer (P2P) borrowing and lending efforts. Many DeFi projects have already entered the market focusing on this particular application case. Among these programs, Compound and PoolTogether are two well-known names. These projects have independent policies for lending and lending. Payment Solutions One of DeFi's top drivers was serving non-bankers or understated banks from the get-go. DeFi's natural features make it ideal for solving the problems of current global payment systems. DeFi provides fast, secure, and transparent solutions compared to asset systems. As DeFi lowers the demand for intermediaries, making payments easier and more transparent, DeFi-based blockchain-based payment solutions can appeal to non-bankers.

In 2019, more than 19 million Americans had at least one unsecured personal loan. Personal lending is growing at an extremely fast rate, and FinTech firms need to go through an organize large amounts of data in order to optimize lending. Python will be used to evaluate several machine learning models to predict credit risk. Algorithms such as RandomOverSampler, SMOTE, and RandomForest will be used to analyze credit card datasets from a company (LendingClub) and use linear regression to both sample and predict data. This data can be used to determine the number of people who are predicted to be at high/low risk for credit risk.

Credit risk analysis in financial institutions

The objective of this project is to determine the risk of default that a client presents and assign a risk rating to each client. The risk rating will determine if the company will approve (or reject) the loan application

We'll use Python to build and evaluate several machine learning models to predict credit risk. Being able to predict credit risk with machine learning algorithms can help banks and financial institutions predict anomalies, reduce risk cases, monitor portfolios, and provide recommendations on what to do in cases of fraud.

Providing a quantitative assessment of the likely effects of the private market for student loans on college enrollment in order to better assess the effectiveness of higher education policies.

1 Introduction LendingClub is a peer to peer lending company in which their product allows consumers to both invest and borrow loans. They offer multiple kinds of loans like student loans, personal loans, auto refinancing loans and even business loans. The borrowers who are interested in obtaining loans will get a loan grade assigned to them which affect their interest rates and the amount of money they can borrow. A lot of the LendingClub data leads to insightful conclusions about the borrowing and investing patterns of all kinds of individuals. Through our investigation, we will explain patterns and similarities of the behaviors of borrowers and investors. 1.1 Questions of Interest We intend to start off with exploratory data analysis of all the factors involved to find patterns and relationships. We will look at the data from multiple angles to get a sense of the intricacies that lie within the data. We will additionally match the trend we see in the data to external events to try to explain why such is happening. We will also conduct time series decomposition in regards to the average loan amounts being requested. We will take a look at the trend and the seasonality so that we can better forecast spikes in demand. After, we will try to fit prediction models in order to answer a couple questions: namely whether a loan request from a client should be funded or not from the perspective of the bank, and what interest rate a borrower would get for a loan from the perspective of a client. After finding good models, we will deconstruct them in order to get a deeper sense of the important aspects in such decisions. 1.2 Dataset The dataset we are using is a compilation of data on loans issued by LendingClub from the period 2007 to 2015. The data includes information on the current loan status (how much has been funded so far, how much has been paid off, etc) as well as information about the borrower (occupation, income, credit score, etc). This data lends itself to a variety of interesting financial analysis, notably time series analysis since the data is date stamped. We will touch on a number of variables present in the dataset throughout the course of this analysis. We will consolidate the meanings of all these variables here for future reference. • loan_amnt: listed amount of the loan applied for by the borrower • funded_amnt: total amount committed to that loan at that point in time • funded_amnt_inv: total amount committed by investors for that loan at that point in time • term: number of payments on the loan. Values are in months and can be either 36 or 60 • int_rate: interest rate on the loan University of California, Davis • installment: monthly payment owed by the borrower • grade: loan grade that corresponds to the risk of the loan • loan_status: current status of the loan • total_bal_il: total current balance of all installment accounts • emp_title: job title of the borrower • next_pymnt_d: next scheduled payment date • sec_app_mort_acc: number of mortgage accounts at time of application for the secondary applicant More information about the dataset can be found here: https://www.kaggle.com/wendykan/lending-club-loan-data

Build and evaluate several machine learning algorithms to predict credit risk.