Lending Club Loan data analysis

Prediction of loan defaulter based on more than 5L records using Python, Numpy, Pandas and XGBoost

This is the Python Code for the submission to Kaggle's Loan Default Prediction by the ID "HelloWorld"

Lending Club Data Loan Default Prediction

零基础入门金融风控-贷款违约预测 TOP11

Code for Kaggle's Default Loan Prediction - Imperial College London challenge.

Loan Default Prediction at Kaggle

R code for Kaggle's Loan Default Prediction - Imperial College London challenge

Loan Default Prediction using PySpark, with jobs scheduled by Apache Airflow and Integration with Spark using Apache Livy

Loan default predictions

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Kaggle code for Loan Default Prediction competition

Artificial Intelligence and Machine Learning have empowered our lives to a large extent. The number of advancements made in this space has revolutionized our society and continue making society a better place to live in. In terms of perception, both Artificial Intelligence and Machine Learning are often used in the same context which leads to confusion. AI is the concept in which machine makes smart decisions whereas Machine Learning is a sub-field of AI which makes decisions while learning patterns from the input data. In this blog, we would dissect each term and understand how Artificial Intelligence and Machine Learning are related to each other. What is Artificial Intelligence? The term Artificial Intelligence was recognized first in the year 1956 by John Mccarthy in an AI conference. In layman terms, Artificial Intelligence is about creating intelligent machines which could perform human-like actions. AI is not a modern-day phenomenon. In fact, it has been around since the advent of computers. The only thing that has changed is how we perceive AI and define its applications in the present world. The exponential growth of AI in the last decade or so has affected every sphere of our lives. Starting from a simple google search which gives the best results of a query to the creation of Siri or Alexa, one of the significant breakthroughs of the 21st century is Artificial Intelligence. The Four types of Artificial Intelligence are:- Reactive AI – This type of AI lacks historical data to perform actions, and completely reacts to a certain action taken at the moment. It works on the principle of Deep Reinforcement learning where a prize is awarded for any successful action and penalized vice versa. Google’s AlphaGo defeated experts in Go using this approach. Limited Memory – In the case of the limited memory, the past data is kept on adding to the memory. For example, in the case of selecting the best restaurant, the past locations would be taken into account and would be suggested accordingly. Theory of Mind – Such type of AI is yet to be built as it involves dealing with human emotions, and psychology. Face and gesture detection comes close but nothing advanced enough to understand human emotions. Self-Aware – This is the future advancement of AI which could configure self-representations. The machines could be conscious, and super-intelligent. Two of the most common usage of AI is in the field of Computer Vision, and Natural Language Processing. Computer Vision is the study of identifying objects such as Face Recognition, Real-time object detection, and so on. Detection of such movements could go a long way in analyzing the sentiments conveyed by a human being. Natural Language Processing, on the other hand, deals with textual data to extract insights or sentiments from it. From ChatBot Development to Speech Recognition like Amazon’s Alexa or Apple’s Siri all uses Natural Language to extract relevant meaning from the data. It is one of the widely popular fields of AI which has found its usefulness in every organization. One other application of AI which has gained popularity in recent times is the self-driving cars. It uses reinforcement learning technique to learn its best moves and identify the restrictions or blockage in front of the road. Many automobile companies are gradually adopting the concept of self-driving cars. What is Machine Learning? Machine Learning is a state-of-the-art subset of Artificial Intelligence which let machines learn from past data, and make accurate predictions. Machine Learning has been around for decades, and the first ML application that got popular was the Email Spam Filter Classification. The system is trained with a set of emails labeled as ‘spam’ and ‘not spam’ known as the training instance. Then a new set of unknown emails is fed to the trained system which then categorizes it as ‘spam’ or ‘not spam.’ All these predictions are made by a certain group of Regression, and Classification algorithms like – Linear Regression, Logistic Regression, Decision Tree, Random Forest, XGBoost, and so on. The usability of these algorithms varies based on the problem statement and the data set in operation. Along with these basic algorithms, a sub-field of Machine Learning which has gained immense popularity in recent times is Deep Learning. However, Deep Learning requires enormous computational power and works best with a massive amount of data. It uses neural networks whose architecture is similar to the human brain. Machine Learning could be subdivided into three categories – Supervised Learning – In supervised learning problems, both the input feature and the corresponding target variable is present in the dataset. Unsupervised Learning – The dataset is not labeled in an unsupervised learning problem i.e., only the input features are present, but not the target variable. The algorithms need to find out the separate clusters in the dataset based on certain patterns. Reinforcement Learning – In this type of problems, the learner is rewarded with a prize for every correct move, and penalized for every incorrect move. The application of Machine Learning is diversified in various domains like Banking, Healthcare, Retail, etc. One of the use cases in the banking industry is predicting the probability of credit loan default by a borrower given its past transactions, credit history, debt ratio, annual income, and so on. In Healthcare, Machine Learning is often been used to predict patient’s stay in the hospital, the likelihood of occurrence of a disease, identifying abnormal patterns in the cell, etc. Many software companies have incorporated Machine Learning in their workflow to steadfast the process of testing. Various manual, repetitive tasks are being replaced by machine learning models. Comparison Between AI and Machine Learning Machine Learning is the subset of Artificial Intelligence which has taken the advancement in AI to a whole new level. The thought behind letting the computer learn from themselves and voluminous data that are getting generated from various sources in the present world has led to the emergence of Machine Learning. In Machine Learning, the concept of neural networks plays a significant role in allowing the system to learn from themselves as well as maintaining its speed, and accuracy. The group of neural nets lets a model rectifying its prior decision and make a more accurate prediction next time. Artificial Intelligence is about acquiring knowledge and applying them to ensure success instead of accuracy. It makes the computer intelligent to make smart decisions on its own akin to the decisions made by a human being. The more complex the problem is, the better it is for AI to solve the complexity. On the other hand, Machine Learning is mostly about acquiring knowledge and maintaining better accuracy instead of success. The primary aim is to learn from the data to automate specific tasks. The possibilities around Machine Learning and Neural Networks are endless. A set of sentiments could be understood from raw text. A machine learning application could also listen to music, and even play a piece of appropriate music based on a person’s mood. NLP, a field of AI which has made some ground-breaking innovations in recent years uses Machine Learning to understand the nuances in natural language and learn to respond accordingly. Different sectors like banking, healthcare, manufacturing, etc., are reaping the benefits of Artificial Intelligence, particularly Machine Learning. Several tedious tasks are getting automated through ML which saves both time and money. Machine Learning has been sold these days consistently by marketers even before it has reached its full potential. AI could be seen as something of the old by the marketers who believe Machine Learning is the Holy Grail in the field of analytics. The future is not far when we would see human-like AI. The rapid advancement in technology has taken us closer than ever before to inevitability. The recent progress in the working AI is much down to how Machine Learning operates. Both Artificial Intelligence and Machine Learning has its own business applications and its usage is completely dependent on the requirements of an organization. AI is an age-old concept with Machine Learning picking up the pace in recent times. Companies like TCS, Infosys are yet to unleash the full potential of Machine Learning and trying to incorporate ML in their applications to keep pace with the rapidly growing Analytics space. Conclusion The hype around Artificial Intelligence and Machine Learning are such that various companies and even individuals want to master the skills without even knowing the difference between the two. Often both the terms are misused in the same context. To master Machine Learning, one needs to have a natural intuition about the data, ask the right questions, and find out the correct algorithms to use to build a model. It often doesn’t requiem how computational capacity. On the other hand, AI is about building intelligent systems which require advanced tools and techniques and often used in big companies like Google, Facebook, etc. There is a whole host of resources to master Machine Learning and AI. The Data Science blogs of Dimensionless is a good place to start with. Also, There are Online Data Science Courses which cover the various nitty gritty of Machine Learning.

Capstone Project: Predicting default in P2P lending

Repository with code and data associated with the paper: P2P Loan acceptance and default prediction with Artificial Intelligence

This project automates bank credit risk assessment using AI and machine learning models to predict loan defaults. It streamlines the credit process with predictive analytics, model evaluation, explainability (SHAP), and deployment readiness.

Classification problem to predict loan defaulters using Lending Club Dataset

PaiPaiDai(拍拍贷) Loan Default Prediction Contest Solution

L&T Financial Services & Analytics Vidhya presents ‘DataScience FinHack’. where I have predicted whether the customer will be defaulter in the first EMI payment using different algorithms from machine learning

Predicting the likelihood of a borrower defaulting on a loan using a machine learning model based on variety of factors given on an imbalanced dataset.

Loan Default Prediction using various boosting algorithm and ensemble method

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In this FinHack, you will develop a model for our most common but real challenge ‘Loan Default Prediction’

End to end Machine Learning process of loan default prediction, Final Project for Machine Learning I Spring 2018@GWU

Loan Default Prediction Machine Learning Project

Building a machine learning model to predict loan payment status (paid or default), helping financial institutions better manage risks and minimize financial losses.

LTFS loan default predictions competition hosted by AV

Numerous companies from financial indutry often invest considerable resources to improve their predictive models with the aim of having better insights into their customers. Such an interest in model improvement has intensified in recent years mostly because of fast development of machine learning and artificial intelligence. For standard lending institution default predictive model with high performance helps to considerably minimize Credit Loss, resulting in higher revenue and profits. Usually the better predictive model the more efficient is the underwriting policy and collection process. A well-functioning model should distinguish creditworthy customers from those that are credit risks. Often, more-predictive credit-decisioning model can identify a greater number of customers within an institution’s specified risk tolerance, which should expand revenues as well. In this project the goal is to increase detection of defaulted loans before the loan is issued/offered by P2P lending company - Lending Club. Peer-to-peer lending differs from traditional financial institutions like banks or commercial lending companies. So, Lending Club is a mediator between investors and borrowers, earning money by charging both. The main Lending Club interest is to attract more clients and maintain protfolio size. The motivation of borrowers is clear, they want to find as cheap capital as possible, so they're seeking for the best offer at the market, which is available for them. In case of investors the motivation is obvious as well. Investors look for high ROI (return of investments), but remembering that returns are proportional to risks, we may formalize saying, that investors look for appropriate returns/risks ratio. If investors experience losses it may cause churn rate growth. The underwriting process for Lending Club looks like this. Borrower applies for the loan, then if he/she meets all the basic requirements - Lending Club using their scoring model assigns client to respective grade. There are 7 grades and 35 sub-grades. Interest rate is dependent on sub-grade. After that, Lending Club gives access to the loan for investors with information about the loan and the borrower (incl. grade and sub-grade) and investors decide whether or not to invest money in this loan. The lower the grade the higher the interest rate, which means, that investors may take higher risks to gain potentially higher returns. Seeking for default rate reduction we can end up with too restrictive underwriting policy which does not neccessary correlate with higher ROI for investors, because we'll not let investors choose risky loans, which means lower interests. For Lending Club it probably means the loss of investors with high risk appetite and borrowers with weak credit history, or in case of Lending Club those who need higher loan amount.

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