There’s no question that big data has transformed our economy. Perhaps the best example of this is the disruption it’s had on the world’s finance sector. As one of the first industries to fully embrace big data, finance has used the digital revolution to go from strength to strength. They now offer everything from automated pricing to personalized online banking. And at the heart of all this change? Big data and data scientists. In tribute to these practical wonder wizards, let’s check out the top nine applications of data science in the finance industry.
Data’s role in the stock market has always been important, even before the digital age. Historically, keeping track of which shares to buy and sell meant analyzing past data by hand. This allowed investors to make the best possible decisions, but it was an imperfect approach. It didn’t take into account the volatility of the market, meaning traders could only use data that had been manually tracked and measured, combined with their personal intuition. Bad investment decisions using outdated data were, unsurprisingly, not uncommon.
Today, by leveraging technological advances, financial data scientists have (to all practical ends) eradicated this data latency, providing us with a constant stream of real-time insights. Using dynamic data pipelines, traders can now access stock market information as and when it happens. Tracking transactions in real-time, they can make much smarter decisions about which stocks to buy and sell, vastly reducing the margin of error. These real-time technologies have also had a knock-on effect across the financial sector, as we’ll see.
The goal of stock market trading is to buy shares at a low price, before selling them on at a profit. This involves using past and present market trends to understand which stocks are likely to increase or reduce in price. To maximize profit, stock market traders have to get in there quickly, buying and selling shares before their competitors. This used to be done manually. However, with the arrival of big data and real-time insights, the landscape has been transformed. A consequence of real-time insights is the ability (and requirement) to trade far more quickly. Eventually, the speed of trading overtook what humans could manage.
Enter algorithmic trading. With machine learning algorithms trained using existing data, financial data scientists have created an entirely new type of trading: high-frequency trading (HFQ). Because the process is now completely automated, buying and selling can happen at lightning speeds. Indeed, the algorithms used are so unbelievably fast that they’ve led to a new practice in the market. Known as ‘co-location,’ this involves placing computers in data centers as close as physically possible to the stock market exchange (often on the same premises). This shaves mere fractions of a second off the time it takes to carry out a trade, but those fractions of a second keep investors ahead of the competition. Pretty incredible stuff!
Financial risk management is all about protecting organizations from potential threats. The threats themselves can be wide-ranging and include things like credit risk (e.g. ‘is this customer going to default on their card payments?’) and market risk (e.g. ‘is the housing bubble going to burst?’). Other types include inflation risk, legal risk, and so on. Essentially, anything that might negatively impact a financial institution’s functioning or profit can be considered a risk.
In its base form, risk management involves three tasks: detecting risks, monitoring risks, and prioritizing which risks to deal with most urgently. This might sound straightforward, but once you consider all the risk factors and how they intersect, it quickly becomes highly complex. Getting it right can be the difference between success and financial ruin. Unsurprisingly, then, data scientists have a key role to play in solving these problems, and they have leveraged machine learning (ML) to do so.
By automating the identification, monitoring, and prioritization of risk, ML algorithms minimize the scope for human error. They also take into account a huge variety of different data sources (from financial data to market data and customer social media) measuring how these different sources impact one another. Getting this right has become an art form. To illustrate, credit card firms using automated risk management software can now accurately determine a potential customer’s trustworthiness, even if they lack the customer’s comprehensive financial background.
A benefit of these algorithms is that they improve as they grow. AI-based risk management and smart underwriting can make connections that human beings alone would never spot. This is the power of machine learning. While these approaches are relatively new in the financial industry, their potential for the future is huge.
Financial fraud comes in many forms: credit card fraud, inflated insurance claims, and organized crime, to name a few. Keeping on top of fraud is vital for any financial institution. This is not just about minimizing financial losses; it’s also about trust. Banks have a responsibility to ensure that their customers’ money is secure.
Once again, real-time analytics comes to the rescue. Using data mining and artificial intelligence (AI), data scientists can detect anomalies or unusual patterns as they occur. Specially-designed algorithms then alert the institution to the anomalous behavior and automatically block the suspicious activity. The most obvious example of this is credit card fraud. For instance, if your card gets used in an unusual location, or withdrawals are made in a pattern matching that commonly used by fraudsters, the credit card company can block the card and inform you that something is wrong before you even know it.
While detecting this type of outlier behavior is useful to individuals like you and me, fraud detection goes much further. Machine learning can also spot broader patterns of anomalous behavior, e.g. different organizations being hacked simultaneously. This can help banks identify cyber-attacks and organized crime, potentially saving them millions.
For any bank or financial services provider, understanding customer behavior is vital for making the right decisions. And the best way to understand customers? You got it: through their data. Financial data scientists increasingly use market segmentation (breaking down customers into granular demographics) to create highly sophisticated profiles. Combining various data sources and using demographics like age and geographic location, banks, insurance companies, pension funds, and credit card firms can gain very precise insights.
Using these insights, they can tailor their direct marketing and customer relationship management approach accordingly. This might involve using data to upsell particular products or to improve customer service.
Customer analytics also allows organizations to determine what’s known as the ‘customer lifetime value,’ a metric that predicts the net profit a customer will provide across all past, present, and future interactions with the organization. If this value is high, you can bet customers will be well cared for! This is a good reminder that, while the customer may always be right, insights gleaned from their data are regularly used to benefit the business, too!
Before the internet, people had to do all their banking in a physical bank. This seems completely inefficient by today’s standards, but it did mean that people got to know their bank manager. However, as the customer experience moved online, this relationship became much more transactional. That personal touch got lost. How to remain personal and relevant in the digital age has been a longstanding problem for banks. But once again, data analytics comes to the rescue!
A happy client is good for business, and that’s why personalized services focus on customer care. As you’ll know if you’ve ever used online banking, there are tonnes of personalized services available. And these are driven by data. They can be divided into three types.
The first is prescriptive personalization. This uses past customer data and preferences to anticipate what they need. It’s generally driven by rule-based algorithms that respond to customer interactions.
The second type is real-time personalization. This relies on both past and present data to tailor the customer experience as it’s happening (for example, if you’re recommended a product or service as you’re carrying out an online transaction).
The final type is machine learning personalization. Although this is a relatively new concept, it already has cool potential. A great example is the fintech software, wallet.AI, which uses your financial profile and transaction history to act as a personal advisor on your daily spending. Great if you’re not so good with money. What might the future hold?
Pricing optimization is the ability to shape pricing based on the context in which customers encounter it. Most banks and insurance providers have large sales teams, offering complex webs of different products and services. If they work in isolation, they can often be unaware of products available elsewhere in the business. And because they’re usually driven by the bottom line, it can be easy for sales teams to fall back on personal experience rather than data-driven insights.
Using a variety of data from sources such as surveys, past product pricing, and sales histories, financial data scientists can help drive profit and save headaches for these sales teams.
How does this work in practice? Well, advanced machine learning analytics can carry out tests on various scenarios (e.g. whether to bundle services together or to sell them individually) allowing teams to produce smarter strategies. Financial data scientists will also ensure these algorithms integrate effectively with an organization’s systems, drawing data as necessary to automate much of the process. This means salespeople can do what they do best: sell! While pricing optimization may sound cynical, it ultimately gives customers what they want (good value) while maximizing profit for the company. Everybody wins.
One of the fastest-growing uses of data science in the finance industry comes from fintech (financial technology) providers. This nascent area of the industry has only emerged in recent years, but has been quick to take advantage of the sluggish pace of change prevalent in larger, more rigid financial organizations (such as older banks). Sweeping in with a disruptive start-up mentality, fintech companies are offering exciting innovations at a much faster pace than global organizations can manage.
While many fintech providers have launched digital banks, others focus on specific areas of technology, before selling these on. Blockchain and cryptocurrency, mobile payment platforms, analytics-driven trading apps, lending software, and AI-based insurance products are just a few examples of fintech that is driven by data science.
As mentioned, financial institutions have access to huge amounts of data. The potential sources are vast: mobile interactions, social media data, cash transactions, marketplace reports…you get the idea. It’s not something many people think about, but besides the social media giants, the finance sector has access to more of our data than probably any other industry. Harnessed properly, these goldmines of data can provide invaluable financial business intelligence. But harnessing these data properly is half of the challenge.
While the majority of these data are digitized, most lack any structure at all. And with real-time data constantly streaming in, bringing order to this chaos is a headache. While numbers one to eight on our list explored the flashy results of this data science journey, data management in finance is a huge task in itself. It requires teams of data experts who can build data warehouses, mine data, understand the complexities of the industry, and do all this while developing novel approaches to working with it. Data engineers and data architects (who manage data itself) are vital to effective financial data management.
In this post, we explored the nine top uses of data science in the finance sector. As we’ve learned, increasingly precise statistical techniques and modern technologies have transformed the way the finance industry works. And they will continue to do so.
If you’re interested in a career in data analytics, this offers a small taste of the huge array of areas you could work in, even within a single industry. To discover more about data analytics, try a free, 5-day data analytics short course. You can also read the following posts for more industry insights:
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