In the current financial context, the application of data analysis in financial technology mainly focuses on risk assessment and management, customer behavior analysis and marketing optimization, anti-fraud, etc. Our company is a leading auto parts e-commerce platform. Many customers are enterprises. Because the capital amount is relatively high, it involves all aspects such as loans, credit granting, fraud prevention, etc. Today, we will disassemble the application of data analysis in financial risk control in detail.

I. Risk assessment and management
This is one of the most critical applications of data analysis in financial technology. It not only involves pre-loan decision-making but also post-loan continuous monitoring. Through quantitative analysis, we can identify credit risk, market risk and operational risk. This process uses the prediction model to assess the probability of violation and market fluctuations, so as to make wiser choices in loan decision-making.
For example, our company is an auto parts Internet platform. Some car maintenance companies will buy parts from our platform in bulk, because our platform also provides financing services. Should we approve these users who apply for loans? At this time, we need to deeply analyze customer data, establish a credit score model based on historical data, and prevent loan default.
How long is the so-called historical data generally? When we do a lot of analysis, we usually use the data of the first 12 months for analysis. Then use the data of the month for testing. Conduct effective identity verification and risk assessment before the loan is issued, decide whether to lend to him, and then how much is appropriate to borrow? As shown in the picture, our background will have some options such as credit limit, shutdown, etc., which will be adjusted according to the user's situation.

II. Customer behavior analysis and marketing optimization
When the loan is issued, we need to verify in time whether the customer will return the money to us in time? If he doesn't return it to us, what risks will he have? When the customer fails to repay on time, identify potential risks and take corresponding measures.
We need to evaluate customers' repayment ability in real time, that is, by analyzing customer transaction behavior, preferences, feedback and other data, we can segment customers and formulate personalized marketing strategies.
So which model will we adopt for user segmentation? Of course, the RFM model is more suitable. We divide the customer level according to the user's recent consumption, consumption frequency, consumption amount, etc., and implement differentiated marketing.

1. RFM model user classification

Back to our company's case, we divide the company's customers into important value customers, important recall customers, important deep digging customers, retention, potential customers, new customers, general maintenance customers, lost customers, etc. according to the RFM model.

For high-value users, we will design an annual feedback plan to enhance customer loyalty and maintain long-term relationships.
For important customers, we have to find a suitable strategy to follow up. If their trading interval becomes longer, we can call them back through regular reminders. For example, if a customer buys goods a month ago, we use robots or automatic reminders to tell them that they may need to replenish the goods.
For deep-digging customers, we need to examine their purchasing patterns. For example, if you buy oil today and tires tomorrow, we need to think about whether they have other potential needs to explore. This requires us to formulate different strategies based on the RFM model.

The RFM model may be a little rough in practical application, because the customer classification it gives may not be detailed enough for platform operation. Therefore, many enterprises and data analysts will make adjustments based on the RFM model, such as considering the frequency of consumption, the amount, and even the breadth of consumption. In this way, we can analyze customers more accurately to adapt to different business scenarios.

2. Sandbox analysis
In addition to strategies for specific customer groups, we also need to conduct an overall market analysis, which is called sandbox analysis. Sandbox analysis helps us understand how much capacity we can participate in the whole market, how much has been occupied, and how much space is left for development. For the senior management of enterprises, they are concerned about the penetration rate - that is, the current market share, and how much room is left to improve. They may consider expanding their market share through different strategies.

This sandbox analysis method is very practical in various industries, which can help us understand the market situation more comprehensively and formulate more effective business expansion plans.

III. Fraud detection
Fraud detection is a key link in identifying potential problems, but it is not enough to find problems. We need to understand the root cause of the problem in depth. Although we may have identified multiple problems, such as customers may not repay, we still need to specifically analyze the reasons why each customer does not repay.
In fraud detection, we must first combine business knowledge to identify abnormal behavior. By enumering possible problem points, such as abnormal behavior, preferential treatment, overduring financing or after-sales problems, we can locate the problem more accurately. This kind of analysis helps us associate data with specific cities or stores, so that local leaders can take action on specific problems.
For example, if a store has an after-sales problem, we should not only pay attention to the problem itself, but also predict the repayment risks that this situation may cause, and formulate corresponding strategies. The purpose of fraud detection is to identify abnormal transaction patterns by building models and prevent financial fraud and other illegal activities. This includes timely detection of suspicious transactions and taking measures to stop them. This method is not only applicable to credit card transactions, but also to other financial transactions.

IV. Operational efficiency and cost control
Everyone is talking about reducing costs and increasing efficiency, but the key is to find out where the cost and benefit points are. Data analysis can provide suggestions and indicators. We use process mining technology to analyze the loan approval process and find ways to shorten time and improve customer satisfaction.
One of the ways to save costs and manpower is to use the UE model (minimum economic unit model). This model can help us calculate the cost, rate and final profit of each person. In this way, we can manage costs more finely and improve the overall operational efficiency.
Simply put, we need to pay attention to the three key factors of income, cost and profit. Revenue reduction is equal to profit, which is the most basic financial equation. By analyzing the income and cost of each business path, we can better understand the economic benefits of each link, so as to make wiser decisions.

V. Regulatory compliance and reporting
The so-called regulatory compliance is to analyze internal process data, identify efficiency bottlenecks, optimize resource allocation, and reduce costs. Specifically, what we do is to use process mining technology to analyze the loan approval process, shorten the approval time, and improve customer satisfaction.