Data science works to extract actionable insights from raw data using tools, and methodologies drawn from different disciplines including mathematics, statistics, computer science, and information science.
HBS makes a significant distinction between “Data science” and “Data analytics”, describing Data Science as the process of “…building, cleaning, and structuring datasets to analyze and extract meaning”. While Data Analytics refers to the analysis and interpretation of data. The role of a data scientist covers the following:
- Forming hypotheses
- Running experiments to collect data
- Assessing data quality
- Cleaning and streamlining datasets
- Organizing and structuring data for analysis
Why is this so critical?
Digitalization is transforming every aspect of our lives and the amount of data being generated is exploding. The best estimates suggest that at least 2.5 quintillion bytes of data are being generated daily. Statista estimates that 79 zettabytes of data were created in 2021, an increase on 64 zettabytes in 2020, and 41 zettabytes in 2019. (A zettabyte is a trillion gigabytes!) By 2025 global data creation is forecast to increase to 181 zettabytes.
We need data science to make sense of the huge amounts of unstructured and structured data that are being generated. Data scientists work with different tools to uncover unseen patterns, extract information and support business decisions. This increasingly involves data mining, machine learning and big data. There is a huge demand for data scientists.
Data science applications in corporate finance
Data science is useful in every industry but especially so in finance. Financial institutions were among the earliest adopters of data analytics. “Finance has always been about data”. In any business, the finance department is the data hub.
Finance gathers, analyses and reports on the company’s financial position and consolidates plans and creates budgets. But one of the central challenges faced by finance is producing real-time data. Excel and SaaS-based reporting and budgeting can snap-shot the present and reveal historical trends but not at the speed and level of granularity required by business today.
“The new world of finance is all about data driven decisions. But it’s not just about financial data. Finance needs to leverage all data: financial, operational, and external.” – oracle.com
This is why companies around the world are recruiting data scientists to be part of their finance team.
Why is data science the future of corporate finance?
Financial data science is changing how finance operates. According to McKinsey, the finance department of the future will have to change to adapt to the demands of a fast-changing environment. A unifying thread in the evolution of finance is the use of data technology.
- Expanding focus beyond transactional activities or traditional accounting
- Automating routine and repetitive accounting and budgeting activities
- Allowing finance staff to concentrate on more value-added activities such as finding strategic opportunities
- Implementing a master data strategy for the entire organization including responsibility for consolidating, simplifying, and controlling company-wide data, including:
- Prioritizing data quality and consistency
- Leading data-standard alignment across departments
- Investing in technology enabled data backbone
- Allocating finance-staff to data cleaning and data processing
- Deploying technological solutions to improve data quality and data usage – including machine learning and other data science technologies
- Improving decision-making process at all levels by providing faster, better, and clearer insights
- Reimagining the finance operating model
- Implementing a more agile operating model allowing staff to adjust focus dynamically to address business challenges
- Connecting and aligning data analytics and decisions across finance and operations
Data science in finance: use cases
How is data impacting finance today? Here are some of the most common use cases:
This is one of the most critical use cases of data science. Every business is exposed to a variety of risks arising from external factors such as competitors, markets, macro-economic issues, and internal considerations such as debt exposure. Managing risks involves identifying, monitoring, and planning risk mitigation strategies. Different ML tools can be used to analyze risk drivers, develop, and implement risk scoring models.
Fraud detection and prevention:
Fraud detection is another critical area in finance that is benefitting from data science applications. ML can help in quickly extracting incomplete or incorrect data, increasing productivity by automating routine tasks. Examples include:
- Detecting fake insurance claims, based on historical patterns found in genuine claims
- Identifying suspiciously high-value or high-volume transactions
- Exposing identity theft
- Recognizing money laundering activities
Gartner describes real-time analytics as the application of logic and math to enable more rapid decision making. Gartner gives, as an example, a bank scoring system that can help banking employees with the immediate information needed to decide on a loan extension. The benefits of access to real-time data include:
- Reduced reaction time to risk
- Real-time testing of marketing or pricing schemes
Data science extracts insights from consumer behavior through mining huge amounts of customer data. This information can be used to make better business decisions.
Customer data management and analytics:
Using such ML and AI tools as natural language processing, data mining, text analytics, data science helps businesses understand their customers at a deeper level. Customer segmentation can be dramatically improved using clustering and segmentation algorithms.
The information and detailed customer behavior metrics that data science reveals can help businesses to offer personalized and tailored services to customers.
One common but controversial use of data science in finance is algorithmic driven trading. Many large financial institutions are using algorithms to place market orders, to predict and capitalize on small market movements increasing profits by leveraging volume, price, and time variations at much faster speeds than humans are capable of.
Benefitting from financial data science
Financial data science is the application of data science techniques to finance issues. It covers such critical areas as fraud detection, risk management, and customer analysis.The integration of data science into finance is changing the way business is done. Finance is being transformed from a mainly support function to a more strategic role. Data science applications are allowing finance departments to streamline and automate accounting processes. Finance professionals now have access to powerful data-based insights that can drive business decisions. Now, it is not enough for corporate finance to collect and monitor financial KPIs. Finance managers must be able to develop and execute data-driven financial strategies.
Many finance departments have recognized the need to include data scientists in their finance teams. A data scientist combines skills drawn in computer science, mathematics, statistics, information visualization, graphic design, complex systems, communication, and business. The role of data scientists is to reveal insights from structured and unstructured data. They use tools such as predictive modeling, data wrangling, and data visualization.
With the complexity of work involved in financial data science, hiring freelancers or remote data scientists is an efficient and reliable way to secure the support you need. On Pangaea X, you can hire the best data freelancers from around the world without breaking the bank.
It’s time to start making better use of your data today.