Application of Data Science in Finance Domain

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Data science covers a vast array of knowledge and skills. The importance and application of this collection of skills has grown rapidly in recent years to become of crucial importance in many industries, including marketing, production and finance, to name but a few.

This article aims to answer the question of how data science is applied in the financial domain. First, we will give a very brief overview of what data science is. This is covered in depth in another article. Then, we will unpack specific applications of data science in the finance domain with examples of each.

What is Data Science?

Data science is the processing of data collected from structured and unstructured sources in order to extract useful information. Sources of data could include online or manual surveys, retail client information (including, but not limited to, purchases, demographic and location) and user information and behavior on social media, among others. This data is used to model a system or client base’s behavior in order to predict future behavior and trends. This field could be applied to a human study group, such as retail clients or social media users, weather forecasting, machine learning and a vast host of other disciplines. These disciplines include machine learning, artificial intelligence, statistics, mathematics, computer science and analytics, among others.



When used correctly, data science will lead to actionable insights extracted from data that could propel an organization to new heights. It takes the guess work out of decision making, instead replacing it with solid, data-based insights.

Why use Data Science in Finance?

As was mentioned earlier, data science takes the guess work out of decision making. In the financial field, many decisions must be made in a short period of time. These decisions range from minor, short term decisions to major decisions having long-term ramifications that could pose a significant threat to an organization. It is clear that there are enormous risks involved in these decisions. Basing them on sound data and scientific principles would give all role players peace of mind and would mitigate the risks involved.

Here are some examples of applications where one would use data science in the finance domain:

Risk Management Automation

The face of the financial industry has been completely changed over the past few years, in large part directly as a results of risk management strategies. In order to attract and maintain clients, a financial institution must ensure customer security, display trustworthiness and guarantee sophisticated strategic decision making. Here, machine learning algorithms, which is a subsection of data science, are used with tremendous success.

The origins of risks in financial institutions are legion. These include competitors, clients, investors, legislation or regulators. Some risks are large and carry an enormous potential loss, while others are smaller or even insignificant. Machine learning algorithms are used to identify, prioritize and monitor risks in an automated fashion. Data from insurance results, customers and financial lending and the like are used to train these algorithms to mitigate these risks and score them according to importance and potential impact. This takes human bias and error out of the equation, giving peace of mind to all relevant parties. It also enhances cost efficiency and sustainability, since fewer staff members are needed to monitor this system and actively mitigate risks.

Another task that carries potential risks for a financial institution is financial lending. The creditworthiness of a potential customer should be determined conclusively and reliable before entering in to a lending agreement. Here, artificial intelligence or machine learning algorithms are used to analyze a customer’s financial history. This approach could even be applied to new customers or ones with a very brief credit history, since these algorithms are highly sophisticated.

Although this technology is still relatively new, the positive impact is already being felt. To align themselves with the possibilities and benefits that this could bring, companies are training financial teams in advances analytics and other related fields. This allows for the automation of core financial processes, allowing staff to spend more time on more profitable tasks.

Managing Customer Data

Currently, vast amounts of customer data are generated daily. This data is mostly in an unstructured format. Sifting through this data manually in order to extract useful information is simply unpractical. Data science, specifically machine learning and artificial intelligence (AI) can be utilized in order to automate this task. Reporting on trends gathered from this data could also be automated, taking a massive load off the shoulders of data analysts. Smarted data governance translates into smarted decision making, which, in turn, translates into increased profits. It makes business sense to employ data science to your benefit.

Predictive Analytics

Data science and machine learning algorithms can be employed to “understand” and predict system and client behavior. When used correctly, it is possible to accurately predict customer lifetime value, significant life events and even stock market behavior. Armed with this crucial and valuable information, companies can decide how to intervene or pre-empt these events in order to turn the largest possible profit. This is potentially an extremely complicated question and even more complicated answer, but the correct and effective utilization of data science can simplify the process. This would, in turn, enable to formulation of clear goals and logical, practical actions for the financial institution and other role players.

Real-Time Analytics



Data is only useful if it is accurate, current and accessible. Real-time analytics allow financial institutions to take timely action in order to prevent losses, protect the business and enhance profits. There are three main areas in which real-time analytics are especially crucial: fraud detection, consumer analytics and algorithmic trading.

Fraud Detection

Security is one of the biggest and most pressing concerns for any financial institution. Fraudsters are constantly finding new ways to steal or embezzle funds and other products. Data scientists are constantly working to stay ahead in this race, programming algorithms to detect any unusual user behavior and anomalies in system behavior and function. The diversity of fraud that we currently face renders this a mammoth task usually undertaken by only the most highly qualified data scientists.

Examples of fraud detected by data science and machine learning algorithms include a large and unusual cash amount withdrawn from a client’s account. This would usually lead to freezing that account until the client confirms that it was indeed a valid transaction. Another example comes from the stock market: unusual data trading patterns detected by machine learning algorithms may indicate manipulation of stock. In this instance, staff would be alerted and they would investigate this case.

It is imperative that these machine learning algorithms continue to self-teach in order to stay up to date and relevant in this ever-evolving field of technology.

Consumer Analytics

When an institution understands their customers, informed decisions and greater personalization is possible. This leads to greater customer satisfaction and engagement, which would translate into increased profits. Real-time analytics makes this a very powerful reality. Here, sophisticated algorithms are used to analyze customer interactions and sentiment in-store and on social media in order to better predict customer behavior and suggest options for personalization.

Algorithmic Trading

Trade is highly volatile. Every second counts and making a trading decision a moment too late could have dire consequences. Here, it is of the utmost importance to have the fastest and most accurate decision-making tools at your fingertips.

When employing highly sophisticated and advanced machine learning and predictive algorithms, a decision maker can have peace of mind that they are at the forefront of their trade, making the correct split-second decisions with confidence, every time.

Deep Personalization and Customization

Customers have come to expect personalized services and sophisticated customer interaction. In order to manage this effectively on large scale, data science techniques are employed to analyze customer interaction and sentiment on social media and other electronic platforms. Artificial intelligence (AI) algorithms are increasingly able to decipher and understand human speech and emotion, turning this into tangible, useful data for a company to use. When employed efficiently, this information would lead to actionable insights of customer needs, which, when met, would result in an increase in profit. This could be used to predict when a customer is most likely to benefit from advice or other input from a financial institution, optimizing marketing strategies and timelines in order to benefit both the customer and the institution.

In Closing



Data science offers a tremendous opportunity for companies to increase their customer engagement, protect their profits through risk management and stay ahead in the fast- paced, ever-evolving world of finance and artificial intelligence.

When employed efficiently, data science would allow companies in the finance domain to effectively mitigate risks and ensure customer security, make real-time decisions regarding trade and customer sentiment, manage consumer data to their advantage and accurately predict future trends in their specific sector. This would allow them to pre-empt changes and be prepared for any eventuality.

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