8 Best Applications of Data Science in Finance

Applying data science in finance

Data science has become integral to the financial services industry. Data is regarded as an essential commodity and the fuel for future innovation. But how does this closely entwined relationship affect business value?

By 2025, global data creation is projected to grow to more than 180 zettabytes. In 2020, the demand shock of the COVID-19 pandemic saw the amount of data created and replicated reaching a new high, peaking at 64.2 zettabytes. But can this growth be solely attributed to the pandemic? The answer is partly yes. But let’s dig a little deeper.

Data is seen as one of the most valuable commodities in the age of technological innovation and progress in information technology. It’s often called the “new oil.” Data science is therefore deployed to extract insights from structured and unstructured data.

The finance sector, thanks to its longstanding relationship with data science, sees in its mountain of data an opportunity to take measured risks and profit. And in this article, we’re going to find out how.

What are the benefits of introducing data science in financial services?

Data science in finance benefits infographic

Financial organizations were among the first to pioneer and employ data science in banking and financial services. The legacy of big data in finance continues through machine learning, predictive and prescriptive analytics that provide potent options for analyzing financial data and solving related difficulties.

Here is precisely what data science brings to finance:

  • Greater efficiency

The availability of new datasets has provided a powerful means of understanding behavior, and offers new predictive directions for the financial industry. As a result, companies can reduce operating expenses while improving overall performance. In addition, they can leverage data from their consumers to reduce operational risks and lower business processing costs.

  • Timely risk analysis and management

By gathering and optimizing client data, financial services can make more informed decisions. For example, significant progress has been made in applying artificial intelligence to forecast financial fraud. Financial data science projects can also expand credit decisions, improve collection strategies, forecast liquidity needs, mitigate risks, and (again) reduce costs.

  • Improved customer service

Understanding users’ spending patterns results in more personalized products and recommendations. These will enhance consumer loyalty and provide financial services companies with more opportunities for cross-selling.

The financial sector, which includes both traditional financial institutions and fintech companies, deals with large volumes of data types and, by its nature, is constrained by certain factors that don’t affect other industries. This significantly impacts the various applications of data science for finance and the work that data science experts can undertake. However, this situation has not lessened the importance of integrating data science in finance — quite the opposite, in fact.

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8 best examples of implementing data science in finance

Eight best data science in finance use cases infographic

Navigating a constantly shifting landscape, replete with big data, requires first-rate skills. Financial data scientists are exactly the experts with the skills that the industry demands. They have specialist, in-depth domain knowledge which goes beyond simply collecting, mining, and making sense of data.

Here are the fields in which financial data scientists are actively working:

Risk analytics

Risk analytics is one of the critical areas of data science and business intelligence in finance. Via risk analytics and management, a company can make strategic decisions and boost security.

There are various forms of risk, originating from competitors, credits, and markets. Therefore, the primary management step is to identify, monitor and prioritize the risks.

Initially, data was processed in batches and not in real-time, causing problems for those industries that needed real-time data to understand current conditions. However, thanks to technological developments and the growth of dynamic data pipelines, companies can now monitor purchases, credit scores, and other financial parameters without any latency issues.

Customer analytics

Following the COVID-19 pandemic, personalization and customer experience have grown in importance. By leveraging advanced analytics, financial services firms can know more about customer preferences, multichannel touchpoints, and buyer behavior factors.

Insurers, for example, are utilizing supervised machine learning to analyze consumer behavior drivers, as well as to reduce losses by eliminating low-value clients, improve cross-sale opportunities, and calculate customers’ lifetime value.

Banks and financial institutions use unsupervised machine learning to understand their consumers better, via clustering techniques which identify groups of similarly behaving customers.

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Fraud detection and prevention

Traditional fraud detection uses rule-based models to identify unusual transactions. These models often flag legal transactions as fraudulent activities, in a scenario where millions of transactions are coinciding.

Machine learning, though, creates algorithms that process large datasets with many variables to find hidden correlations between user behavior and the possibility of fraudulent acts.

With the help of machine learning techniques and big data analytics, banks and other financial services firms can detect and prevent fraudulent activities, including speculatory trading, rouge trading, and regulatory violations.

Algorithmic trading

Algorithmic trading is one of the most important applications of data science in finance. Through complex mathematical formulas and high-speed computations, financial firms can map out new trading techniques and develop strategies accordingly. This sophisticated use of data science in finance leads to more accurate forecasts for a company’s future based on a better understanding of massive datasets.

Pricing automation

The financial and banking sectors are constantly under pressure to maintain handy profit margins and to improve operations. They can use predictive analytics, visualization, and AI to achieve both. Replacing paper-based forms with digital apps and leveraging natural language processing (NLP) technology will lead to greater accuracy, fewer errors, and faster work.

Service personalization and customization

When engaging with customers, personalization has long been a priority.

Financial institutions are responsible for providing personalized services to their customers, and to that end they use a range of techniques to evaluate customer information and generate insights from their interactions. For example, speech recognition and NLP software are used to improve user involvement.

With the data that users provide, financial institutions can gain meaningful insights into their customers’ needs, resulting in increased profits. In addition, the data will assist the institutions in optimizing their plans and providing better services to their clients.

Decision-making

Finance executives and stakeholders are required to judge market conditions daily and make strategic decisions accordingly. Propelled by past and present data, these decisions are becoming ever more timely and smart. Some analytics platforms will go as far as to suggest when and how to go to the next phase, when to withdraw your funds, and when to put money in.

Data management

Today’s financial data structure and volume range widely: from social media behavior and smartphone interactions to corporate statistics and transaction information.

Finance professionals must also deal with semi-structured or unstructured data that is notably difficult to retrieve manually.

However, it has been apparent to most organizations that incorporating data science and machine learning methods for process management are what is required when extracting real data intelligence.

NLP, data mining, and text analytics are AI tools that help turn data into information, leading to better data governance and more successful business solutions.

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Should you apply data science in your business?

Data science for finance organizations infographic

The answer is yes — and here’s why. As one of the core components of the global economy, the finance sector has for some years now recognized the value of big data in making lucrative decisions and taking calculated risks. As discussed in this article, the industry already has a history of embracing the use of data science to revolutionize everything from how the stock market works to identifying fraud and enhancing customer experience.

As the tech landscape expands, financial services — traditionally, a product-centric industry — have become customer-centric. And for a good reason.

According to the Salesforce survey, which included 8,000 company buyers and consumers worldwide, 84% of customers believe that a customer’s experience is as important as the products and services offered by a company. Customers’ standards for modern engagement are far removed from the transactional, one-size-fits-all experiences that were once the norm.

This means that if you want your business to differentiate itself and create more value in today’s digital environment, you must adapt to today’s data-driven business models.

Final thoughts

Data science can help businesses in the financial industry overcome the primary difficulties that arise daily. Machine learning algorithms, real-time data analytics, and data mining are just a few examples of how you can improve your firm’s financial plans and strategies.

For analysis-based strategy, you can call on PixelPlex big data consulting team. From data strategy customization to intelligent techniques integration and cloud deployment, we offer analytics-savvy consulting and development professionals who will ensure operational risks are mitigated, client needs are factored in, decisions enhanced, and revenue boosted.

Get in touch now, and let us structure your multi-source data lake into a neatly organized data warehouse that drives business value across your entire enterprise.

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author

Anastasiya Haritonova

Copywriter

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