Data Science in Finance: The Top 9 Use Cases

What are the most prevalent data science applications in the financial industry? Learn more in this article.

There’s no denying that big data has changed the way we do business. The upheaval it has caused in the global financial sector is perhaps the finest illustration of this. Finance, as one of the first businesses to completely embrace big data, has benefited greatly from the digital revolution. From automated pricing to individualized online banking, they now have it all. And what’s at the centre of it all? Data scientists and big data Let’s take a look at the top nine data science applications in the banking business as a homage to these practical wonder magicians.

1. Real-time stock market insights

Even before the digital age, data played a significant role in the stock market. Previously, keeping track of which stocks to purchase and sell required manual analysis of historical data. This enabled investors to make the best selections possible, but it was a flawed method. It didn’t account for market volatility, thus traders could only rely on data that had been painstakingly collected and measured, as well as their own intuition. Unsurprisingly, bad investment decisions based on obsolete data were widespread.

Today, financial data scientists have (practically) eliminated data latency by utilizing technical advancements, giving us with a steady supply of realtime information. Traders may now get realtime stock market information by using dynamic data pipelines. They can make significantly better selections about which stocks to purchase and sell by tracking transactions in realtime, greatly lowering the margin of error. As we’ll see, realtime technology have had a ripple effect throughout the financial sector.

2. Algorithmic trading

The purpose of stock market trading is to purchase low-cost stocks and then sell them for a profit. This entails analyzing previous and current market patterns to determine which stocks are likely to rise or fall in value. Stock market traders must act swiftly to maximise profit, purchasing and selling shares before their competition. Previously, this was done by hand. The scene has changed, however, with the emergence of big data and realtime insights. The capacity (and necessity) to trade much more swiftly is a result of realtime insights. The speed of trade eventually outpaced what humans could handle.

This is where algorithmic trading comes in. Financial data scientists have built an altogether new sort of trading: high-frequency trading, employing machine learning algorithms trained on existing data (HFQ). Buying and selling may now take place at breakneck speeds, thanks to the automation of the process. Indeed, the algorithms used are so lightning fast that they’ve spawned a new market practise. This is known as ‘colocation,’ and it entails putting computers in data centres as close to the stock exchange as possible (often on the same premises). This reduces the time it takes to execute a trade by fractions of a second, but those fractions of a second keep investors ahead of the competition. It’s very great!

3. Automated risk management

The goal of financial risk management is to shield businesses from potential dangers. 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?’) are two examples of potential hazards. Inflation risk, legal risk, and so on are examples of other sorts of risk. Basically, anything that could have a negative impact on a financial institution’s ability to function or earn is a risk.

Risk management, in its most basic form, entails three tasks: recognizing risks, monitoring risks, and selecting which risks to address first. This may appear simple at first, but if you consider all of the risk factors and how they interact, it gets extremely complicated. It can mean the difference between financial success and failure. Data scientists, unsurprisingly, have a crucial role to play in resolving these issues, and they’ve done so using machine learning (ML).

ML algorithms reduce the risk of human mistake by automating the detection, monitoring, and prioritization of risk. They also take into account a wide range of data sources (ranging from financial data to market data and customer social media), assessing how these sources interact. It’s become an art form to get this correctly. Credit card companies, for example, may now reliably identify a potential customer’s trustworthiness using automated risk management tools, even if they don’t have the customer’s complete financial history.

One advantage of these algorithms is that they improve over time. Artificial intelligence-based risk management and smart underwriting can discover correlations that humans alone might miss.This is the power of machine learning. While these approaches are relatively new in the financial industry, their potential for the future is huge.

4. Fraud detection

Credit card fraud, falsified insurance claims, and organized crime are just a few examples of financial fraud. It is critical for any financial institution to stay on top of fraud. This is about more than just avoiding financial losses; it’s also about building trust. Banks are responsible for ensuring the safety of their clients’ funds.

Realtime analytics comes to the rescue once more. Data scientists can uncover anomalies or unexpected trends in real time using data mining and artificial intelligence (AI). The institution is then alerted to the unusual behaviour and the suspected conduct is automatically blocked by specially created algorithms. Credit card fraud is the most prominent example of this. For example, if your card is used in an unexpected area or withdrawals are made in a pattern similar to that employed by fraudsters, your credit card company may block your card and notify you that anything is wrong before you even realize it.

Individuals like you and me can benefit from recognizing this type of aberrant behaviour, but fraud detection goes far beyond. Machine learning can help detect larger patterns of unusual behaviour, such as multiple firms being hacked at the same time. This could aid banks in detecting cyberattacks and organized crime, perhaps saving millions of dollars.

5. Consumer analytics

Understanding consumer behaviour is critical for any bank or financial services provider to make the best decisions. And what’s the greatest approach to get to know your customers? You guessed it: through their information. To develop very sophisticated profiles, financial data scientists are increasingly using market segmentation (breaking down clients into precise demographics). Banks, insurance companies, pension funds, and credit card businesses can acquire very precise insights by combining numerous data sources and employing demographics such as age and geographic area.

They can modify their direct marketing and customer relationship management strategies based on these information. This could entail utilizing data to upsell specific products or improve customer service.

Customer analytics also enables businesses to calculate what’s known as the ‘customer lifetime value,’ a number that forecasts a customer’s net profit over all past, present, and future interactions with the company. Customers will be well taken care of if this value is high! This serves as a nice reminder that, while the customer may always be right, data insights are frequently leveraged to benefit the company as well!

6. Personalized services

People had to do all of their banking in a real bank before the internet. By today’s standards, this seemed inefficient, but it did mean that people got to know their bank manager. This relationship became much more transactional when the customer experience shifted online. That personal touch was vanished. Banks have long struggled with how to remain personal and relevant in the digital age. However, data analytics comes to the rescue once more!

A satisfied consumer is excellent for business, which is why individualized services emphasize customer care. If you’ve ever used internet banking, you’ll know that there are a plethora of specialized options accessible. And all of this is based on statistics. They can be classified into three categories.

Prescriptive personalization is the first. This method anticipates what customers require based on previous data and preferences. It’s usually controlled by rule-based algorithms that react to customer interactions.

Real-time customization is the second type. This uses both historical and current data to modify the client experience as it occurs (for example, if you’re recommended a product or service while making an online purchase).

7. Pricing and revenue optimization

The capacity to shape pricing based on the context in which customers encounter it is known as pricing optimization. Most banks and insurance companies use big sales teams that sell a complicated web of products and services. They may be uninformed of products available elsewhere in the company if they work in isolation. Because sales teams are frequently motivated by profits, it’s easier for them to rely on personal experience rather than data-driven ideas.

Financial data scientists may help generate profit and minimise difficulties for these sales teams by using a variety of data from sources like as surveys, prior product prices, and sales histories.

In practise, how does this work? Advanced machine learning analytics, on the other hand, may run tests on numerous situations (for example, whether to bundle services together or offer them separately), helping teams to develop more intelligent plans. Financial data scientists will also make sure that these algorithms work well with a company’s systems, pulling data as needed to automate much of the process. As a result, salespeople can focus on what they do best: selling! While it may appear cynical, pricing optimization ultimately provides customers with what they desire (excellent value) while increasing profit for the organisation. Everyone comes out on top.

8. Product development

Fintech (financial technology) companies are one of the fastest-growing users of data science in the finance business. This embryonic sector of the economy has just recently evolved, but it has quickly capitalized on the slower pace of change prevalent in larger, more inflexible financial institutions (such as older banks). Fintech companies are sweeping in with a disruptive start-up mindset, providing fascinating breakthroughs at a much faster rate than global enterprises can handle.

While several fintech companies have developed digital banks, others concentrate on certain technological areas before selling them. Fintech driven by data science includes blockchain and cryptocurrencies, mobile payment platforms, analytics-driven trading apps, lending tools, and AI-based insurance solutions, to name a few examples.

9. General data management

Financial firms, as previously stated, have access to enormous volumes of data. Mobile conversations, social media data, financial transactions, marketplace reports…the possibilities are endless. It’s not something that many people consider, yet the banking business, aside from the social media behemoths, has access to more of our data than almost any other company. When correctly harnessed, these data goldmines can give important financial business knowledge. However, appropriately utilizing these data is only half of the battle.

While the majority of the data has been digitized, the majority of it is unstructured. Bringing order to this chaos is a hassle, especially with real-time data continually coming in. While the first eight items on our list focused on the eye-catching outcomes of this data science journey, data management in finance is a massive undertaking in and of itself. It will take a team of data professionals that can design data warehouses, mine data, comprehend the complexities of the sector, and come up with new ways to deal with it. Effective financial data management necessitates the use of data engineers and data architects (who handle the data itself).