Application of data analysis in banking industry and specific cases
1. Fraud detection
Fraud detection is to detect possible fraud by analyzing transaction patterns, which mainly include the following aspects
1. Cross-institutional account number verification mechanism and risk information sharing mechanism: establish these The mechanism can increase the sharing and use of risk labels in more dimensions and improve the effect of joint prevention and control.
2. Big data risk control models for abnormal accounts, suspicious transactions, etc.: Use external shared data to further improve these risk control models and continue to improve detection effects.
3. Police-bank linkage: Cooperate with the public security department to establish and improve systems, procedures and relief measures for instant inquiry, emergency payment stop, quick freezing, timely unfreezing and fund return of funds involved in telecommunications network fraud cases.
4. Knowledge graph: Taking users of the whole bank (debit card, credit card, credit) as the customer group, using more than 20 kinds of relational data such as transfers, employment, IP, and equipment in the entire history or within a certain time range to build a full graph. Identify the risk of fraud among all customer groups.
5. Anti-fraud system: The anti-fraud system mainly detects and blocks real-time fraudulent transactions. The customer submits a transaction request on the APP or online banking. The request will be supplemented by a series of data fields to form a complete transaction message. The anti-fraud system takes out the transaction message in real time and performs risk assessment, and returns the risk assessment and corresponding control measures to the online bank. System, online banking system for actual control.
Case
Credit card fraud is a category of the traditional financial industry. Credit card corporate debt behavior includes using the characteristics of credit card overdraft consumption for the purpose of illegal possession and still not returning it after being called by the issuing bank. Overdraft or absconding to conceal identity after a large overdraft to avoid repayment responsibilities. There are usually several situations when a credit card is used fraudulently: the card is not present: the fraudster steals card and person-related information (card number, validity period, name) to conduct transactions; the card is forged: the real magnetic field is read through a certain device. card information and forge the credit card; the card is lost or stolen: the cardholder is used fraudulently before reporting the loss; identity information is stolen: fraudsters steal information such as phone bills, utility bills, bank statements, etc. Apply for a credit card in your name; card stolen in the mail: The credit card is stolen in the mail.
There are many algorithms that can be used in credit card fraud detection. Here are some common algorithms:
②Support Vector Machine (SVM): The collection of SVM classifiers provides a high detection rate.
③Random Forest: Random Forest has the lowest false positive rate.
Specific cases such as:DF, CCF big data competition case
Data set:Credit card fraud detection data Set - DF, CCF big data competition data; The data set contains transactions conducted by European cardholders through credit cards in September 2013, including the amount, time, amount and other information of credit card transactions;< /span> Field description: 31 fields in total, among which V1-V28 are data (numeric variables) after PCA conversion, Time The transaction time is in seconds, Amount is the transaction amount, and Class is the transaction type (1 in case of fraud, 0 otherwise)
Data size: 284807 rows*31 columns
2. Customer segmentation
By analyzing customer behavior, income, credit rating and other factors, customers are divided into different groups in order to better understand their needs and behaviors. There are mainly the following types of algorithms.
①K-Means clustering algorithm:K-Means clustering algorithm is a commonly used unsupervised learning algorithm used to divide customers into different groups. This method has a relatively small amount of calculation and is suitable for big data.
②Hierarchical clustering method:Hierarchical clustering method can also be used for customer segmentation, but it is more suitable for small data.
③ Analysis of relevant variables based on demographic characteristics and behavioral characteristics: Select relevant variables of demographic characteristics and behavioral characteristics for data mining, and obtain the clustering results of individual cases and the clustering of variables. class result.
④Machine learning algorithm:In recent years, machine learning algorithms have become more and more widely used in banks. Classification, clustering, association, etc. may be used and will also be used. to neural networks, deep learning, graph algorithms, etc.
Among them, cluster analysis is the mainstream application algorithm. For specific cases, see the hyperlink above.
3. Risk modeling
The identification and assessment of risks is a concern for investment banks. In order to regulate different financial activities and determine appropriate prices for various financial instruments, it helps banks to analyze historical data and predict risks such as loan defaults and fraud. Make better decisions.
Data analysis algorithms in risk management mainly include the following:
Based on the customer data of historical insurance purchases, conduct supervised machine learning, build an insurance recommendation model, and issue application strategies, and use the marketing model to push marketing plans to the business department. A case study on PD modeling conducted by Deloitte France found that Multiple model performance indicators showed that using random forest, gradient boosting and stacking methods in building The PD model is better than the logistic regression model. Under the right conditions, employing machine learning methods in model estimation has the potential to improve model accuracy. However, while machine learning improves model accuracy, it often also makes the model difficult to interpret.
A case example is SAS risk management tool, through regulatory risk, capital planning, credit risk management, risk monitoring and other services , establish risk awareness, optimize capital and liquidity, and meet regulatory requirements.
Project data: By substituting historical loss data and financial statement data into the formula of the new standard measurement method, financial institutions can complete the calculation of their minimum capital requirements for operational risk.
4. Marketing Optimization
Marketing optimization means optimizing marketing strategies and improving marketing effects by analyzing customers' purchase history, response behaviors, etc., helping banks better understand customer needs, predict market trends, and formulate and implement effective marketing strategies. The following are some commonly used data analysis algorithms in marketing optimization in the banking industry:
① Classification algorithms: Such as decision trees, random forests, and support vector machines. These algorithms can help banks group customers and formulate appropriate marketing strategies for different types of customers.
②Clustering algorithm: such asK-means and hierarchical clustering< a i=4>, etc., these algorithms can help banks segment customers and identify similar customer groups for more refined marketing.
③ Association rule learning: Association rule learning such as Apriori, FP-Growth Algorithms such as can help banks discover the correlation between customer purchasing behaviors, thereby designing marketing strategies such as cross-selling and portfolio recommendations.
④Regression analysis algorithms: such as linear regression, logistic regression andsupport vector regression, etc. These algorithms can help banks predict customers’ purchase intention and purchasing power, thereby adjusting product pricing and discount strategies.
⑤Time series analysis algorithm: such asARIMA and exponential smoothing etc., these algorithms can help banks predict sales volume and market demand to manage inventory and supply chains more effectively.
⑥Collaborative filtering algorithm: This algorithm predicts products or services that target customers may be interested in by analyzing the customer's historical behavior and the behavior patterns of other customers.
5. Credit score
Credit scoring is to score customers by analyzing their credit history, financial status, etc. to decide whether to grant a loan and the interest rate of the loan. There are mainly the following types of algorithms:
① Logistic regression: This is a binary classification algorithm widely used in credit scoring. It predicts a customer's probability of default by analyzing their historical behavior and other relevant attributes.
② Decision trees and random forests: These algorithms can be used to deal with missing values and can group customers to formulate appropriate credit scoring strategies for different types of customers.
⑤Feature selection and modeling analysis: This process includesscreening of IV values, correlation coefficients and significance, and the use of logistic regression The algorithm solves a binary classification problem (determining whether a loan applicant defaults) and ultimately calculates a credit score for each sample.
6. Customer churn prediction
That is, by analyzing customer behavior patterns, we can predict which customers are likely to lose so that we can take measures to retain them. The main ones are as follows
①Logistic Regression:Logistic regression is a commonly used classification algorithm that can be used to predict the probability of an event, such as Predict whether customers will churn. . This is a binary classification algorithm widely used in credit scoring and customer churn prediction. It predicts the probability of customer churn by analyzing the customer's historical behavior and other related attributes.
②Decision tree and random forest:These algorithms can handle missing values and can group customers to formulate appropriate retention strategies for different types of customers.
③Support Vector Machine (SVM):SVM is a supervised learning model mainly used for classification and regression analysis.
④Neural Networks:Neural network is a model that imitates the work of human brain neurons and can be used for pattern recognition, time series prediction, etc.
⑤K-Means clustering algorithm:K-Means clustering algorithm is a commonly used unsupervised learning algorithm used to divide customers into different groups. This method has a relatively small amount of calculation and is suitable for big data.
⑦Bagging algorithm: Improve the accuracy and stability of the model by combining the prediction results of multiple decision trees, and effectively predict customer churn.
7. Recommendation engine
The key to success in any industry is to provide these selected goods and services to the users they actually want. By analyzing client activity, different data analytics and machine learning tools can help the industry identify the best projects for clients.
①Collaborative filtering recommendation algorithm: This is a commonly used recommendation algorithm that collects and analyzes a large number of users' historical behavior information to find the similarity or correlation between users and items, thereby predicting users' ratings or preferences for items.
②Content-based recommendation algorithm: This algorithm mainly calculates the similarity or correlation between items based on the attributes and characteristics of the items, as well as the user's historical behavior and other information, and then recommends items to the user that are similar to their historical preferences.
③Hybrid recommendation algorithm: The hybrid recommendation algorithm is a way of combining multiple recommendation algorithms for prediction. By combining different recommendation algorithms, the overall recommendation effect is improved.
④ Rule-based recommendation algorithm: This algorithm mainly predicts products or services that the user may be interested in through some pre-set rules, such as the user's historical behavior, the user's basic information, etc.
⑤Matrix decomposition: Matrix decomposition techniques such asSingular Value Decomposition (SVD) can be used to predict users’ ratings of unrated items, thus Implement recommendations.
⑥ Association rule learning: Association rule learning such as Apriori, FP-Growth Algorithms such as can discover association rules between items and then make recommendations based on these rules.
8. Customer Lifetime Value Forecast
Customer lifetime value forecast (CLV) value refers to the forecast value of net profit. This is the value that banks will get from their customers throughout the customer relationship.
①Classification and Regression Tree (CART):CART is a decision tree learning method designed to build a prediction model that can predict a or Multiple target variables.
②Stepwise regression: Stepwise regression is an improved regression analysis method that selects the best prediction model by gradually adding or removing variables. First, select the characteristics that have an impact on predicting customer lifetime value, such as the customer's consumption behavior, credit score, income level, etc.
After that, it will continue to iterate. At each step, it will select the best feature to add or eliminate the model, and continuously optimize the prediction ability of the model
③Generalized linear model (GLM):GLM is a flexible statistical model that includes multiple types of regression analysis, such as linear regression, logistic regression, etc.