Uses of Data Analytics in Accounting and Finance

Tables of Contents

Since their inception, the accounting and finance industries have proven their worth to businesses by delivering new forms of value, whether through higher revenue or more efficient operations. No technology offers more promise for delivering innovative sources of value to businesses than advanced data analytics.

In particular, the use of data analytics in accounting and finance has been a major factor in boosting profitability and reducing the costs of doing business.

According to a 2020 survey of accounting professionals by software vendor Sage, 44% of accounting firms were using advanced and predictive analytics that leverage big data, or planned to do so in the next 12 months. Among emerging technologies, only 5G had a higher adoption rate among accountants (46%).

Advances in data analytics create opportunities for accountants and finance professionals to offer higher-quality services to their business clients in three areas:

Data analytics in accounting uses advanced techniques to help firms capitalize on the massive amounts of data they collect. The goal is to create value and growth by leveraging three emerging technologies:

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While many accounting and financial services companies are planning to use data analytics and other new technologies, the rate of implementation remains uneven, according to the Institute of Management Accountants. For self-service reporting, 48% of firms have completed implementation, while 31% plan to implement. In big data, 32% completed implementation and 35% plan to implement. For data visualization, 27% completed implementation and 37% plan to implement. In predictive modeling, 25% completed implementation and 33% plan to implement. For automation, 22% completed implementation and 42% plan to implement. In voice recognition, 5% completed implementation and 20% plan to implement.

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The accounting and finance fields are being reinvented by new technologies. An international survey of accountants conducted by Sage in 2019 found that 90% of respondents believed there has been a cultural shift in accountancy. The changes are apparent in hiring practices, business services, and the industry’s approach to analytics, artificial intelligence, and other emerging technologies.

This shift embraces the opportunity to expand the range of services accountants and finance professionals offer their clients and improve the quality of the services they currently offer. The changes will affect many areas of accounting and finance:

One effect of the cultural shift in accounting and finance is that companies are increasingly recruiting candidates from nontraditional backgrounds, according to the Sage survey. This change is an attempt by accountants to better represent their clients and for accounting firms to add a broader range of skills they can tap to serve their business customers.

The accountants surveyed emphasized the importance of preparing the industry for analytics, AI, and other technologies.

Any business process that collects customer data must ensure that any use of the data protects the privacy and other rights of those customers. One of the new ethical dilemmas related to AI-based algorithms in particular is the lack of consent when the systems create private data that didn’t previously exist. An example is an algorithm that automatically links a person’s bank account activity with the location tracking and call history collected from the individual’s cell phone.

The Digital Analytics Association has created a code of ethics for web analysts that emphasizes honesty and personal accountability to protect privacy, operate transparently, and educate web users about their work. The analytics service Blast Analytics offers a code of ethics for data analysts that describes eight ethical guidelines for the profession:

In addition to the eight guidelines for ethical analytics, the code lists three areas that accountants and finance professionals should focus on when applying data analytics in their work:

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The goal of big data in accounting is to collect, organize, and tap data from a variety of sources to gain fresh business insights in real time. For example, instead of relying on monthly financial reports for their analyses, accountants and financial analysts have access to up-to-the-minute information from any location with a network connection.

A survey conducted by the Institute of Management Accountants found that 67% of accounting firms have either implemented big data or plan to do so. Among the 32% of firms that have completed implementation, these are the most popular uses of big data:

Big data presents opportunities to improve the quality of accounting services in three primary areas:

To meet their business clients’ needs, accountants will need to learn new skills relating to how datasets are structured, organized, and applied, as well as tools for conducting strategic analyses and collaborating across functional teams in an organization.

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As accounting and finance firms develop their plans for using data analytics, implementation will focus on several specific areas, according to the Institute of Management Accountants. For performance measurement, 7% of firms will apply data analytics, 26% big data, and 18% both. For formulating strategy, 7% will apply data analytics, 16% big data, and 15% both. For research and development, 5% will apply data analytics, 11% big data, and 10% both. For product rationalization, 5% will apply data analytics, 10% big data, and 8% both. For order fulfillment, 5% will apply data analytics, 5% big data, and 2% both.

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The use of big data in finance combines tools that create, capture, manage, and process financial and other information with innovative approaches to convert the data into financial intelligence that guides business decisions. In addition to the size of the data pools that the tools work on, big data has two other characteristics that finance requires:

Big data helps financial institutions address heightened competition and regulation while meeting the rising expectations of their clients by taking advantage of the four V’s of big data:

The four V’s are sometimes referred to as the five V’s when value is added. Value in this context means that the data contributes in a meaningful way to the analysis rather than being extraneous.

The three primary areas where big data is applied in finance are algorithmic trading, compliance, and data quality:

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The many ways that firms are applying big data analytics in finance fall into three general categories:

Here’s a closer look at how three companies benefited from their application of big data analytics in their financial operations.

NASDAQ is one of the largest securities markets in the world, handling share volumes that average around 4 billion exchanges each day. In 2014, the company switched from an on-premises data warehouse to Amazon Web Services (AWS). By 2020, NASDAQ’s AWS data warehouse processed financial data from thousands of sources totaling up to 70 billion records in a single day.

The use of AWS Simple Storage Service (S3) cloud storage technology allows NASDAQ to meet customer demand for fast access to historic stock information for its Market Replay and Data on Demand services. Market Replay allows clients to validate best execution and regulatory compliance by reconstructing events relating to a specific trade. Data on Demand provides NASDAQ traders with ready access to historical tick data for financial analytics purposes.

JP Morgan Chase is one of the largest firms in the financial industry, supporting approximately 3.5 billion user accounts and 30,000 separate databases. The company relies on the open-source Apache Hadoop big data framework to operate more efficiently by distributing processors and storage among low-cost hardware. The system collects and processes data from emails, social media posts, telephone calls, and other unstructured data sources.

Many of these data sources were unavailable to JP Morgan Chase prior to adopting the Hadoop framework, which limited its banking products’ effectiveness. Now the company’s data analytics operations more accurately reflect the attributes and tendencies of its millions of banking customers. As a result, its sales of foreclosed properties generate more revenue, and the bank is better able to assess credit to manage risk.

Acorns is one of the leading practitioners of automated micro-investing that combines automatic savings with portfolio management. The company uses machine learning techniques to identify customers’ spending patterns and automatically categorize their transactions. Clients are automatically notified when their spending increases, and the system can even recommend a budget.

The Acorns system works by collecting the excess “change” from customers’ credit card and online transactions and automatically depositing them in their investment portfolio. Clients have the option of applying the change automatically with each transaction or doing so manually on a per-transaction basis. Acorns’ robo-adviser applies algorithms to manage customers’ investment portfolios, which is much less expensive than relying on a human investment adviser.

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Data mining is the process of using software to identify patterns in large data repositories to learn more about a business’s customers, devise more effective marketing strategies, and operate more efficiently. Data mining in accounting extracts knowledge from huge stores of financial and other data to improve accounting practices’ effectiveness.

Three common applications of data mining in finance and accounting are to:

An important component of accounting data analytics is identifying potential fraud in financial records. Data mining tools spot outliers in massive pools of data that include atypical values and unusual behaviors. Among their applications are to detect symptoms of fraud in financial statements and to discover credit card fraud, securities fraud, corporate fraud, and other financial crimes.

Data mining in accounting has been shown to be more effective at detecting potential financial fraud than statistical methods because it applies machine learning to improve classification accuracy, especially when working with low sample data.

Data mining tools use three different techniques to identify similarities within large data repositories:

An accounting audit concludes with the auditor expressing one of two opinions:

Data mining tools can forecast the likelihood that an audit would result in one or the other opinion. Research published in the journal Mathematics showed that a model created using data mining techniques was able to predict the audit opinion of individual and consolidated financial statements with an accuracy of about 82.5%.

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The union of accounting and data science has led to many of the principles of data analytics being applied to enhance accounting practices. Among the many ways that accountants apply data science techniques are to monitor and enhance accounting and financial processes, calculate the risk related to strategic decisions, and anticipate and meet their customers’ expectations.

Here’s a closer look at three examples of the use of data science to improve accounting and finance operations.

Competition in the airline industry is fierce, and airlines are among the most complex businesses to manage due to the many market variables and government regulations that can influence their profitability, as well as the industry’s high degree of unpredictability. Big data analytics and other data science concepts can increase airline revenue by providing companies with a greater understanding of customer behavior, more efficient maintenance schedules, and better fuel efficiency.

Bank of America is one of several banks that are doing away with the traditional fraud alerts that notify customers when transactions occur far from the customer’s home. Instead, the bank uses the location services that accompany its mobile banking app, whose default settings include a daily location check, to verify that customers and their cards are in the same place. At present, the service is available only for the bank’s Visa card holders, but other banks are adopting the automated fraud detection technology as well.

Data analytics, machine learning, and AI techniques are replacing the rules-based approach used previously by banks and credit card companies to detect payment fraud.

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Accountants welcome opportunities to automate many of their work processes and are learning new data analytics skills to improve the services they offer their clients.
According to Sage Group, 66% plan to invest in AI-based automation; 55% plan to use AI to increase business; 56% report increased business in the past year; 67% state that cloud technologies improve client interactions; 42% see their role with clients as increasingly strategic; and 39% consider themselves early adopters of technology.

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Data analytics presents accountants and finance professionals with an opportunity to regain some of the decision-making authority the professions had prior to the advent of automated decision-support systems over the past two decades. Accounting data has become one of several sources of information that contribute to a business’s analytics operations, and accountants have been relegated to providing only “historic” data while the analytics department provides insights and outlooks.

Among the analytics skills that accountants and finance professionals need to develop to have a prominent role in a business’s strategic planning and forecasting are advanced revenue analytics, which focuses on pricing and sales channel optimization, and analytical segmentation, which helps companies align their marketing and sales strategies with their profit goals.

Companies are embedding predictive models in their business processes that can be expanded as new data sources become available. Embedded models can be updated more frequently based on the season, the accuracy of the existing models, and behavioral or other activity changes. As the models learn, they are better able to adapt automatically to unpredictable changes in markets, customer behavior, and other activities.

Automation continues to be applied to a growing number of business areas, including all aspects of accounting. For example, payroll automation is faster and more accurate than traditional payroll modules due to automated data input, net pay calculations, and data sharing. Similarly, by automating a business’s accounts receivable processes, accountants can include these records in their analytics operations more easily.

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The success of accountants and finance professionals depends increasingly on understanding the opportunities that data analytics creates for their clients and their industry. Accountants with a background in data analytics qualify for a far greater range of positions in accounting and finance. The growing adoption of data analytics in accounting and finance firms broadens the responsibilities of the professions while making their roles more important for supporting business decisions.

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Infographic Sources

Institute of Management Accountants, “The Impact of Big Data on Finance Now and in the Future”

Sage Group, “Accountants Adoption of Artificial Intelligence Expected to Increase as Clients’ Expectations Shift”