Application of data science in finance

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Application of data science in finance - There is no doubt that Big Data has transformed our economy. Perhaps the best example of this is the global financial sector. As one of the first industries to fully embrace big data, finance has used digital transformation to turn power into power.

Everything from automated pricing to personal online banking is now on offer. At the heart of all this change are big data scientists. In honor of these amazing wizards, let’s look at the top 9 uses of data science in the financial industry.

1- Stock market insight in the present

The role of data in the stock market has always been important, even before the digital age. Historically, trading stocks has meant analyzing past data by hand. This method allowed investors to make the best possible decisions, but it was an incomplete approach.

This method did not take into account market fluctuations, meaning that traders could only use manually tracked and measured data with personal intuition. Not surprisingly, bad investment decisions using obsolete data were not uncommon.

Today, with advances in technology, financial data scientists (for all practical purposes) have eradicated these data delays and provided us with a steady stream of insight into the present. Using dynamic data transmission lines, traders can access stock market information at the time of occurrence.

By tracking current trades, they can make much smarter decisions about buying and selling stocks, which greatly reduces the margin of error.

2- Algorithmic transactions

The purpose of stock market trading is to buy low-priced stocks before selling at a profit. This includes using past and present market trends to find out which stocks are likely to rise or fall in price. To maximize profits, stockbrokers need to step in quickly and buy and sell stocks before competitors.

This was previously done manually; But with the advent of big data and current insights, this trend has changed. The result of instant insights is the ability (and need) to trade much faster. Eventually, the speed of the transaction exceeded what man could manage.

Algorithmic traders entered the game. Using data-learning algorithms and existing data, financial data scientists have created a whole new type of trading: High-Frequency Trading (HFQ). Because the process is completely automated, buying and selling can happen at lightning speed.

In fact, the algorithms used are so fast that they have led to a new approach to the market. This is known as a “co-location”; That is, placing computers in data centers as close as possible to the stock market (often in the same place). This only reduces the fraction of a second of trading time, but this fraction of a second pushes investors ahead of competitors. unbelievable!

3- Automated risk management - data science in finance

Financial risk management means protecting organizations from potential threats. Threats can be wide-ranging and include issues such as credit risk (for example, “Does this customer pay for their card by default?”) And market risk (for example, “Is the housing bubble bursting?”). Other types include inflation risk, legal risk, and so on. Basically, anything that may adversely affect a financial institution’s performance or profit can be considered a risk.

Risk management includes three tasks: identifying risks, monitoring them, and prioritizing which risks need to be dealt with more urgently. This may seem simple, but it can quickly become complicated when you consider all the risk factors and how they intersect. Doing it right can make the difference between success and financial ruin. So it is not surprising that data scientists have played a key role in solving these problems and have used machine learning to do so.

By automating risk identification, monitoring, and prioritization, machine learning algorithms minimize the range of human error. They also consider a wide range of different data sources (from financial data to market data and customer social media) and measure the impact of these different sources on each other.

Doing this process correctly has become an art. For example, credit card companies using automated risk management software can now accurately determine potential customer reliability, even if they do not have a comprehensive customer financial background.

One advantage of these algorithms is that as they grow, they improve. Risk management based on artificial intelligence and intelligent underwriting can create connections that humans alone will never find. This is the power of machine learning. While these approaches are relatively new in the financial industry, their potential for the future is enormous.

4- Detection of fraud and forgery

There are many types of financial fraud; These include credit card fraud, inflated insurance claims, and organized crime. Preventing fraud is vital for any financial institution. This is not just about minimizing financial losses, it is about trust. Banks are responsible for ensuring the safety of their customers.

Again, real-time analytics can help. Using data mining and artificial intelligence, data scientists can detect abnormalities or unusual patterns if they occur. Specially designed algorithms then alert the organization to abnormal behavior and automatically block suspicious activity.

The most obvious example is credit card fraud. For example, if your card is used in an unusual place or withdrawn in a way that is commonly done by fraudsters, the credit card company can block the card and let you know that something is wrong.

While recognizing this kind of external behavior is helpful to people like you and me, detecting forgery goes far beyond that. Machine learning can also detect broader patterns of abnormal behavior, such as multiple organizations being hacked simultaneously. This could help banks identify cyberattacks and organized crime and save millions.

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5- Consumer analysis

For any bank or financial service provider, understanding customer behavior is critical to making the right decision. As you might have guessed, the best way is to understand the customer through their data. Financial data scientists are increasingly using market segmentation (customer breakdown to granular demographics) to create highly complex indexes.

By combining different data sources and using demographic information such as age and geographical location, banks, insurance companies, pension funds, and credit card companies can have very accurate insights.

Using these insights, they can adapt the direct marketing and customer relationship management approach accordingly. This may include using the data to sell specific products or improve customer service.

Customer analysis also allows organizations to determine what is known as “customer lifetime value,” the measure of a customer’s net profit in all past, present, and future interactions with the organization. If this amount is high, you can be sure that customers are well cared for!

6- Personalized services - data science in finance

Before the advent of the Internet, people had to do their banking physically. That seems to be quite inefficient by today’s standards, but it does mean that people knew their bank managers.

However, as the customer experience shifts online, the relationship becomes much more business-like. Personal contact is lost. How to personalize and stay connected in the digital age has long been difficult for banks. But once again, data analysis comes in handy!

A happy customer is good for business and that is why personalized services focus on customer care. As you know, if you have ever used online banking, there are many personal services available and these are data-driven. They can be divided into three types.

The first is personalization. It uses data and customer preferences in the past to predict their needs. It is generally driven by rule-based algorithms that respond to customer interactions.

The second type is personalization in real-time. It relies on past and present information to adjust the customer experience at the time of occurrence (for example, if you are offered a product or service during an online transaction).

The final type is the personalization of machine learning. Although this is a relatively new concept, it still has some potential. A great example is a wallet. AI software, which uses your financial information and trading history to act as a personal advisor in your daily expenses.

7- Pricing and revenue optimization

Pricing optimization is the ability to shape pricing based on the context in which the customer encounters. Most banks and insurance providers have large sales teams and offer complex networks of different products and services.

If they work separately, they can often be unaware of products available elsewhere in the business. Because they are usually led by the bottom line, it is easy for sales teams to focus on their personal experiences instead of insights.

Financial data scientists can help these sales teams be profitable and save time and effort by using data from various sources such as surveys, past product pricing, and sales history.

How does this happen in practice? Advanced machine learning analysis can perform various experiments (for example, packing services together or selling them separately) allowing teams to develop smarter strategies.

Financial data scientists also ensure that these algorithms integrate effectively with the organization’s systems and map the data as needed to further automate the process. This means that sellers can sell at their best.

Although price optimization may seem pessimistic, it ultimately gives the customer what he wants (good value) and maximizes profits for the company.

8- Product development - data science in finance

One of the fastest-growing uses of data science in the financial industry is through fintech providers. This fledgling area of ​​industry, which has only recently emerged in recent years, has been able to take advantage of the slow pace of growth in larger and stricter financial institutions (such as old banks).

Financial technology companies are delivering exciting innovations at a much faster pace than global organizations can manage.

While many financial technology providers have launched digital banks, others focus on specific areas of technology before selling them. Blockchain and cryptocurrency, mobile payment operating systems, analytics-based trading programs, lending software, and AI-based insurance products are just a few examples of data-driven financial technology.

9- General data management

As mentioned, financial institutions have access to vast amounts of data. This data comes from a wide range of sources: mobile interactions, social media data, cash transactions, market reports, and more. In addition to social media giants, the financial sector has more access to our data than any other industry. With the right use of these gold mines, data can provide you with unparalleled financial information. But properly controlling this data is half the challenge.

While most of this data is digital, most of it lacks any structure. With real-time data constantly flowing, it is very difficult to establish order in this chaos.

Data management in finance requires teams of data professionals who can build data warehouses, extract data, understand the intricacies of the industry, and do all of this while developing new approaches to working with it. Data engineers and data architects (who manage the data themselves) are critical to the effective management of financial data.

Conclusion of data science in finance

In this article, we look at the top 9 applications of data science in finance. As we have learned, accurate statistical techniques and modern technologies have increasingly transformed the financial industry; And they will continue to do so.

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