How Can Data Science Help in Finance Predictive Models?
What is Predictive Analysis?
Predictive analysis is the process of analyzing the current and historical data to make a perfect prediction for future outcomes. By using historical data, one can make a predictive mathematical model, which will help you to think about what will happen next and what step you should take to overcome that situation. For creating a predictive model, you need data that you can collect from customer’s details, company’s purchasing histories, revenue & from profit. For building a predictive model, some techniques are needed, like machine learning, statistics, data mining & artificial intelligence.
How Predictive Analysis Works?
Predictive analysis is used to understand the customer’s choice. This helps the companies in improving their position in the market. This further helps in identifying the upcoming problems and finding their solutions. For example, many companies carry out employer credit check to determine their credit condition and how they can offer their finance services to them. In addition to this, predictive analysis also helps in finding new opportunities to expand the business. By using the customer’s personal information and history of the company’s data, scientists can predict whether the company is running in a risky position, or there is an opportunity to earn profit from that business.
How can Data Science help in Finance Predictive Model?
Data science plays an important role in the successful running of any financial institution. By using data, data scientists have created predictive models that help them to stay up-to-date about the market situation. Generally, more data helps you get a more accurate solution.
Here are some applications of data science in finance industry:
Providing Personalized Service: Providing a satisfying personalized service is more important for financial sectors where a clear understanding client’s requirement is very important. By using customer’s personal data, they can understand their clients & build a strong bond with them. This data can be collected from customers or via studying and analyzing their behavior, comments, transactions & feedback. This would help to lift the range of profit of that institution. Nowadays, financial institutions depend a lot on speech recognition & language processing based software where a customer can directly interact with service providers.
Credit Decision: Using data science, the financial industry can investigate a particular employee’s credit potential by using his history of spending. With this model, financial data scientists can decide “whether the employee would be able to repay the loan or not and the rate of interest”.
Risk Analytics: Risk is a common problem for all businesses. Identifying that risk and managing it properly is the key point of a successful business. For evaluating the level of risk, it is necessary to have the knowledge of mathematics, statistics & be good at solving problems. The main task of a risk management team is to help companies by analyzing their rate of risk and come with plans to give relief from that risk. There are three types of risk 1. Credit Risk, 2.Market Risk, 3.Operational Risk. A lot of data is required to calculate the rate of risk.
Algorithmic Trading: In algorithmic trading, the model uses some program that consists of mathematical models & formulas that help to trade at high speed. A trader uses this technique at the time of trading to execute his order in no-time, such as when the market stock price reaches up or falls below a specific level, then this program tells him how many shares he has to buy or sell at that time. The goal of algorithmic trading is to help investors to make quick decisions to earn higher profits.
Consumer Analytics: Consumer analytics is the process of collecting & analyzing customers’ information for making the right decision so that the institution gains more profit. The data scientists analyze customers’ buying behavior their lifestyle, etc. so that they can make accurate business decisions with that targeted customer. This includes increasing response rates, showing loyalty towards the customer, and a better understanding of their customers.
Real-time Analytics: Real-time analysis is the process of analyzing large amounts of data at the moment of it captured. In the real-time analysis, big data is processed by batch processing, which sometimes causes the problem, but now with new technology and help of data pipelines, this problem has been erased. Big data helps to solve many problems like real-time credit scoring, fraud detection at the point of sale, track transactions without any delay.
Fraud Prevention: Fraud is a common subject in financial institutions that often occurs. But using advanced data tools & improving algorithms, banks can easily detect that fraud and send a fraud detecting message to the customer and block that account to minimize the loss.
What More to Expect in The Future
As financial models are built from collected data, which helps a company to make accurate decisions at the time of financial crisis.
The other future benefits are:
- It shows the company’s financial performance.
- Raise the amount of capital or funding & provide account security.
- Create financial statements yearly based on the company’s assumption & forecast.
- Create effective plans which resulted in maximum return at the end of the plan.
Conclusion
For doing successful business, predictive models in finance are now accepted by almost all the companies. They use it for the security of the account, to improve work efficiency, productivity & to make valuable decisions in a time of risk.
Focused on providing information for anyone in need of debt relief, Jackson writes a blog on debt settlement, debt consolidation, tax debt relief and student loan debt which helps to find the debt solution that fits their unique needs no matter the amount of debt they are in.