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2019-xiamenbank-data-modeling-competition

2019厦门国际银行“数创金融杯”数据建模大赛-top2

artificial-intelligence-for-banking

This project contains codes and notebooks related to various use cases for banking.

artifitial-neural-network-churn-modeling

Business Problem: Dataset of a bank with 10,000 customers measured lots of attributes of the customer and is seeing unusual churn rates at a high rate. Want to understand what the problem is, address the problem, and give them insights. 10,000 is a sample, millions of customer across Europe. Took a sample of 10,000 measured six months ago lots of factors (name, credit score, grography, age, tenure, balance, numOfProducts, credit card, active member, estimated salary, exited, etc.). For these 10,000 randomly selected customers and track which stayed or left. Goal: create a geographic segmentation model to tell which of the customers are at highest risk of leaving. Valuable to any customer-oriented organisations. Geographic Segmentation Modeling can be applied to millions of scenarios, very valuable. (doesn't have to be for banks, churn rate, etc.). Same scenario works for (e.g. should this person get a loan or not? Should this be approved for credit => binary outcome, model, more likely to be reliable). Fradulant transactions (which is more likely to be fradulant) Binary outcome with lots of independent variables you can build a proper robust model to tell you which factors influence the outcome. alt text Problem: Classification problem with lots of independent variables (credit score, balance, number of products) and based on these variables we're predicting which of these customers will leave the bank. Artificial Neural Networks can do a terrific job with Classification problems and making those kind of predictions. Libraries used: Theano numerical computation library, very efficient for fast numerical computations based on Numpy syntax GPU is much more powerful than CPU, as there are many more cores and run more floating points calculations per second GPU is much more specialized for highly intensive computing tasks and parallel computations, exactly for the case for neural networks When we're forward propogating the activations of the different neurons in the neural network thanks to the activation function well that involves parallel computations When errors are backpropagated to the neural networks that again involves parallel computation GPU is a much better choice for deep neural network than CPU - simple neural networks, CPU is sufficient Created by Machine Learning group at the Univeristy of Montreal Tensorflow Another numerical computation library that runs very fast computations that can run on your CPU or GPU Google Brain, Apache 2.0 license Theano & Tensorflow are used primarily for research and development in the deep learning field Deep Learning neural network from scratch, use the above Great for inventing new deep learning neural networks, deep learning models, lots of line of code Keras Wrapper for Theano + Tensorflow Amazing library to build deep neural networks in a few lines of code Very powerful deep neural networks in few lines of code based on Theano and Tensorflow Sci-kit Learn (Machine Learning models), Keras (Deep Learning models) Installing Theano, Tensorflow in three steps with Anaconda installed: $ pip install theano $ pip install tensorflow $ pip install keras $ conda update --all

automatic-scorecard-model

评分卡建模自动化流程

bank-churn-model

银行客户流失预警模型

bank-marketing-data-set-classification

python

bank-product-recommendation

Recommendation system project applied to banking.

collaborative-deep-learning-for-recommender-systems

The hybrid model combining stacked denoising autoencoder with matrix factorization is applied, to predict the customer purchase behavior in the future month according to the purchase history and user information in the Santander dataset.

corporate-credit-rating-prediction-

Predicting the credit ratings of US based companies using machine learning models

credit-card-fraud-detection

This project is an implementation of credit card fraud detection using Hidden Markov Model (HMM)

credit-rating-prediction

Builds models to predict the credit ratings of companies as part of the S&P Data Challenge

credit-risk-modeling-with-ldps

Predicting credit default risk on highly imbalanced data using machine learning. Implemented state-of-the-art models and evaluated results on real-world data.

credit_rating_portfolio

Create and backtest long-short portfolios of US companies based on credit ratings

cure_models

datacastle_shixin

识别失信企业大赛

datascience

debtdefaultresearch

对截止至2017年7月17日的债券违约事件进行梳理归因，并寻找宏观流动性影响因素，组成数据集。运用Lasso回归进行特征提取后，输入带L2惩罚项LR、SVM、NN、GBDT、RF等机器学习模型进行违约预测，得出GBDT预测效果最好以及特征工程对线性模型预测效果具有重要性的结论。

deep-recommender-system

深度学习在推荐系统中的应用及论文小结。

deeplearning_baseontensorflow

基于TensorFlow的深度学习、深度增强学习代码：NN(传统神经网络)、CNN(卷积神经网络)、RNN(递归神经网络)、LSTM(长短期记忆网络)、GAN(生成对抗网络)、DRL(深度增强学习)

financial-knowledge-graphs

小型金融知识图谱构建流程

fujifusion

A data-driven application for predicting corporate credit ratings. Won 1st in a category at Hack MIT 2018.

introduction-to-artificial-intelligence

银行精准营销解决方案+青蛙叫声聚类分析

knowledgegraphofa-sharecompanys

金融知识图谱构建

lhy_dl_hw

machine_learning_tutorials

Code, exercises and tutorials of my personal blog ! 📝

mirroring-funding-circle-s-risk-based-pricing-models

Machine and deep learning based default prediction models for SMEs.

ml-fraud-detection

Credit card fraud detection through logistic regression, k-means, and deep learning.

multicure

R Package for Fitting Multistate Cure Models

multivariate-financial-time-series-analysis

UCLA Master of Applied Economics Capstone Research

nb