Data Analytics in Financial Services

There has never been more data in the world than there is right now; 90 percent of the world’s data was created in the past two years alone. The exponential growth signals a major shift in how industries will be able to leverage data analytics to their advantage. 

Data analytics in financial services has proven to be extremely effective in solving industry problems with innovative solutions, and has transformed how people interact with money. The industry’s success with data analytics makes it one of the best ways to understand how other industries are likely to approach their own shifts toward more data driven business models in the imminent future. 

What Is Data Analytics?

At its most basic, data analytics is the process of analysing data to find trends and answer questions. In the finance industry, data analytics refers to the use of analytic techniques on financial data to gain insights about businesses and consumers.

How Are Data Analytics Used In Finance?

Data analytics are used to help financial institutions uncover business insights and inform business decisions. Using advanced analytics and predictive analytics software that are powered by AI and machine learning algorithms, deep insights can be extracted from the most complex of data sets, and can be used to gain accurate foresight into future trends and market activity. 

With the power to process growing amounts of data almost instantly, financial institutions can leverage the data to also improve their customer service, build products faster, or spot fraud earlier than ever before. 

Some data sources that fuel analytics in the financial services industry include transaction data from credit or debit card accounts, financial market data providers, lines of credit, investments, mortgages, and much more. 

How the data is used varies from financial institution and its goals for streamlining internal data processing methods or providing better services to customers. 

Applications of Data Analytics in the Financial Services Industry

The most common applications of financial data analytics in the financial services industry revolve around better business insights and ways to improve the customer experience with personalised product and service offerings. 

Discovering Opportunities for Growth

Financial institutions can use data analytics to identify new markets, expand their customer bases, and innovate new products and services based on real consumer data. With an accurate understanding of what customers are looking for in particular markets, financial institutions are better equipped to tailor their offerings for competitive advantage as they break into new markets. 

Managing Risk

Assessing risk of credit applicants is one of the most time-consuming tasks for lenders and financial institutions. Using financial data analytics, lenders can establish advanced warning systems to flag fraudulent account information early on, and assess risk with a comprehensive understanding of a credit applicant’s information at a glance.  

Making Predictions

Having the ability to accurately predict future events may sound out of this world, but is entirely possible with data analytics in financial services. Data can be leveraged to predict and anticipate impacts of economic activity and market changes for investors to use to make better decisions in real time. 

Enhancing the Customer Experience

Most consumers have likely interacted with a new product or service offering from their bank in recent years that are the results of banking analytics used to enhance customer experience with financial products. Consumer financial data fuels consumer research that reveal new ways for financial institutions to improve customer loyalty by providing more personalised experiences.

Improving Productivity & Decision Making

The impact of data analytics on improving productivity and decision-making for financial institutions is thanks to AI and machine learning software. Using natural language processing, software is capable of deciphering large amounts of various data types to produce near instant actionable insights for financial institutions to use. 

Challenges with Data Analytics in Financial Services

Data Quality

The large amount of data created every day does not come in a singular consistent quality. There are often gaps in data sets obtained by financial institutions, and those gaps prevent any real value being able to be extracted from it. Having access to high quality and comprehensive data from multiple sources helps ensure you’re only working with the most accurate data available. 

Siloed Data

Due to the large amount of data available, it's easy to have data from multiple sources in multiple places, which can make it more difficult to see insights and trends. Using data aggregation, multiple data sources can be fed into one place to create a holistic view so it’s easier and faster to learn from multiple sets of data. 

Difficult to Interpret

Data can be ambiguous and difficult to understand without the right data to help make it easy to understand for humans. Transaction data enrichment tools are used to clean and enrich data so its full value can be extracted from it with actionable insights for financial institutions. 

Shortage of Talent

The rapid growth of data analytics in financial services has driven increased demand for financial institutions to hire data scientists who can make use and extract the value of complex data sets. They can also keep finding new ways to innovate using new types of data as they’re discovered. However, there is a shortage of employees with these skills compared to the demand. 


The protection of consumer data and security risks are challenges for financial institutions to follow if they wish to leverage data analytics. Data sharing protections and policies are growing increasingly strict around the world as open banking initiatives are implemented to establish more transparent and consistent methods to ensure data security for consumers. Adhering to regulations is key for financial institutions to stay competitive in the future. 

The Rise of Analytics in Banking & Investing

The concept of big data, or the mass amounts of various types of data generated worldwide, has undoubtedly led to an increase in data analytics in the banking and financial sector. The shift to Open Data and Open Banking systems, such as Australia’s Consumer Data Right, are making data more accessible, and will continue to drive more innovation and personalisation in the industry than ever before. 

The banking and investing landscape does not look like how it did just a decade ago, and consumers can expect to see the full benefits of Open Banking and data analytics in the coming years across other industries who have yet to tap into the insights awaiting to be discovered.