Four advantages and five applications of machine learning in the financial field

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No one can live without finance. Although people are becoming smarter with the development of technology, finance is a basic necessity of life because everyone needs money to eat, travel and buy things. At present, a financial market has been formed where people and machines work together, and people are inventing more and more ways to default on loans, steal money from other accounts, create false credit ratings, etc. Today, machine learning plays an indispensable role in many stages of the financial ecosystem, from loan approval to asset management to risk assessment. However, only a few professionals who understand technology really understand how machine learning plays a role in people's daily financial lives.

What is machine learning?

Machine learning is the science of designing and applying algorithms to build algorithms that can learn and make predictions from data. Machine learning applications are so common today that you probably use them dozens of times a day without even realizing it. Machine learning also provides a plethora of use cases, such as self-driving cars, product recommendation engines, predictive analytics, speech recognition, and more. The main purpose of data scientists using machine learning is to reduce the human workload and reduce the time humans spend reading, understanding, and analyzing big data to just a few seconds.

The two most common methods for implementing machine learning are supervised learning and unsupervised learning. Supervised learning algorithms are trained using labeled examples, and the output corresponding to the input data is known in advance. In unsupervised learning, the learning algorithm does not have any labels to use and can only discover the structure in the input data on its own.

Features of Machine Learning in Finance?

Compared with machines, brain capacity has a certain limit on thinking. Humans can only focus on 3-4 things at the same time, while machines have thousands of times the processing power of humans. In addition to speed, machines will also perform better than humans in other aspects of the financial field.

Reliability : When dealing with financial matters, it is necessary to establish a credit rating system for individuals. Banks, investment firms, and stock markets conduct transactions worth billions of dollars every day. Therefore, we must trust the company or individual who handles this matter. Due to the bias and selfishness that may exist in human nature, some people tend to commit fraud during the process of money transactions. To solve such problems, machines embedded with machine learning can achieve zero corruption when processing requests.

Speed : We all know that stock trading in the stock market is difficult. People usually do a lot of analysis in historical data, charts, and formulas to predict the future of stocks, while some people just make random bets. All these behaviors sound very hectic and time-consuming. Machine learning algorithms can accurately analyze thousands of data sets and give concise and accurate predictions in a short time, which helps to alleviate people's troubles in big data collation and analysis.

Security : Previously, the ransomware WannaCry attacked computers around the world, which showed that we are still vulnerable to hackers and cybersecurity threats. Machine learning predicts fraud or anomalies by classifying data into more than three categories and building models. Manual review is costly, time-consuming, and has a high false positive rate, so it is not suitable for the financial industry.

Precision : People are not capable or willing to do repetitive and monotonous tasks, which often result in many errors, while machines can perform repetitive tasks indefinitely. Machine learning algorithms do the hard work of data analysis and recommend new strategies when needed by humans. They can also detect subtle or non-intuitive patterns more effectively than humans, thereby identifying fraudulent transactions. In addition, unsupervised machine learning models can continuously analyze and process new data, and then automatically update their models to reflect the latest trends.

How to apply machine learning in credit scoring?

Even though banks are extremely cautious and carefully verify the creditworthiness of companies, multinational companies defaulting on their bank debts seems to be a common phenomenon in the financial sector. Some financial institutions use scoring models to reduce credit risk in credit assessment, issuance and supervision. Credit scoring models based on classical statistical theory have been widely used. However, these models have poor elasticity when it comes to large amounts of data input. Therefore, some assumptions in classical statistical analysis cannot be established, which in turn affects the accuracy of predictions.

It is crucial for banks to determine the credit risk score of customers based on information such as their nationality, occupation, salary, experience, industry, credit history, etc., even before providing any services to the customers. This is an important key performance indicator (KPI) before banks provide credit or other financial products.

The main challenge is to introduce a centrally integrated financial risk mechanism that can serve customers immediately. Even now, banks cannot approve loans immediately due to the inability to predict the risk score of customers. Machine learning can speed up the lending process and avoid time-consuming and necessary due diligence procedures. Regression algorithms can determine the credit score of customers. These algorithms use statistical processes to estimate the relationship between variables and have been widely used in prediction and forecasting. Their application in the field of machine learning has also been rapidly developed. The first step of this approach is to define the availability of customer historical credit records, then select the target population and determine the benchmark to define satisfactory/unsatisfactory performance. This part will serve as the basic data set for the regression algorithm to start the operation. The next step is to select the sample. The selection criteria are as follows:

1. Identify available variables in your company’s system

2. Define the interest period and sample size

3. Verify data consistency and integrity

The possible scattered information selected is also called demographic variables: gender, age, occupation, company, education, marital status, etc. It is generally recommended to register a customer sample for 12-18 months. This period of time is enough to check for delayed payments and defaults, and to consolidate the payment behavior model of high-quality customers.

By selecting variables, grouping variable attributes, and creating dummy variables, preliminary analysis can be performed. Use contingency tables to calculate the relative risk (RR) index associated with the level of independent variables, and finally calculate the ratio of high-quality customers to low-quality customers at each single variable level. The larger the ratio, the greater the predictive effect of the variable on future performance. RR is usually between 0 and 2, with 0 representing extremely poor and 2 representing extremely good. However, the analysis process will not use samples classified as neutral because the degree of goodness/badness is not much different.

The establishment of the model includes the selection of multivariate statistical techniques. After that, the software to be used is determined, the independent variables are selected, and the assumptions of the technique are tested. Once the data is reduced to the cluster level, discriminant analysis, logistic regression, and neural networks can be used. Discriminant analysis and logistic regression use statistical techniques of different methods. In addition, the selected software should be checked for implementation and ease of use.

Finally, to evaluate the performance, we need to find the KS test for two samples. We need to find the difference between the two clusters, such as the good/bad payers defined by their respective prediction results, determine the difference between the distribution of good/bad payers in each prediction, and the value of the KS test is the one with the largest difference in this module. Since the final result obtained from the model is usually between 0-1, when the result is less than 0.5, the customer will be defined as a poor payer; otherwise, it is a good payer.

Other benefits of machine learning

Fraud detection : When using machine learning for fraud detection, historical data is first collected and segmented into three different parts. The machine learning model is then trained with the training set to predict the probability of fraud. Finally, a model is built to predict fraud or anomalies in the data set. This method of fraud detection takes less time than traditional detection. Since the current application of machine learning is still small and still in its growth stage, it will develop further in a few years to detect complex frauds.

Stock Market Prediction : It is common to become a billionaire by buying and selling stocks, but it is very difficult to beat the market without understanding how stocks work and current trends. With the use of machine learning, stock predictions have become quite simple. These machine learning algorithms use the company's historical data such as balance sheets, profit and loss statements, etc., analyze them, and find meaningful signs related to the company's future development. In addition, the algorithm can also search for news about the company and understand the market's perception of the company through sources around the world. Moreover, through natural language processing technology, it can search for more data about the company by browsing news channels and social media video libraries. This technology is still developing, and although it is not accurate enough at present, it is certain that in the near future, it will be able to make very accurate stock market predictions.

Treasury – CRM, Spot Transactions : CRM plays a prominent role in retail banking, but it plays little role in the treasury space within banks. Treasury has its own product group, such as foreign exchange, options, swaps, forwards and more importantly, spots. Online transactions require a combination of the complexity of these products, customer risk, market and economic behavior, and credit record information, which is almost a distant dream for banks.

Chatbots - Personal Financial Assistants : Chatbots can act as financial advisors, personal financial guides, track expenses, and provide advice on everything from property investments to new car purchases. Financial robots can also translate complex financial terms into easy-to-understand language, making communication easier. A chatbot from a company called Kasisto can handle a variety of customer requests, such as customer notifications, transfers, check deposits, inquiries, FAQs and searches, content distribution channels, customer support, and discount reminders. By recording users' deductible expenses over a long period of time, it can also provide potential savings on bills.

Machine learning is a relatively new technology. Due to reasons such as data sensitivity, infrastructure requirements, and business model flexibility, the application of machine learning has its own shortcomings. However, it helps solve many problems, and its advantages outweigh its disadvantages. Therefore, it has been analyzed by many scholars and industry experts. It is certain that more innovative applications will appear in this field in the future.

Finance is important to all countries in the world. Machine learning technology is safer than human operations and can protect it from threats and improve its operations. It is the best choice for the financial industry and will also help countries achieve development and prosperity faster.