Uses of Data Analytics in Accounting and Finance
- Customer analytics identifies consumer spending habits and other behaviors to spot market trends and anticipate new opportunities.
- Algorithmic trading automates the process of monitoring stock prices. Algorithms place buy and sell orders for when specific conditions are met without requiring direct human intervention.
- Unstructured data that previously wasn’t available for analysis, such as social media feeds, broadens the scope and improves the timeliness of business analytics.
The role of data analytics in accounting and finance
- A broader and deeper perspective on the business’s financial and other operations
- More accurate predictions of future market and industry trends
- Automation of routine tasks to improve accounting accuracy and reduce costs
- Computing power and cloud storage have grown tremendously. Datasets can be large and complex because services such as Amazon Web Services offer scalable processing and storage that expands automatically to meet demand.
- Data sources such as internet service providers, social media platforms, mobile apps, government and other open sources, and sensors and other embedded devices are widely available.
- A digital infrastructure now exists that is based primarily on open-source software. Open networks make it easy for data specialists who have expertise in leveraging data to communicate with domain specialists who are experts in specific fields, including accounting and finance.
How analytics is transforming the accounting and finance industries
- The skills and training required to qualify for accounting and finance positions
- Technology adoption and integration with existing processes and procedures
- Greater client expectations about the quality and type of services available
- Percentage of accounting firms recruiting candidates from nontraditional backgrounds: 82%
- Percentage of firms seeking candidates who have industry experience outside of accounting: 43%
- Percentage who believed the accounting industry needs to move faster to adopt new technologies or risk falling behind international competitors: 85%
- Percentage who stated that the primary benefit of technology adoption is increased productivity: 56%
- Percentage who cited time savings as the principal benefit of new technologies in the accounting field: 27%
Risks of analytics: ethics, privacy, and potential for errors and misuse
- Safeguard customers’ personally identifiable information (PII) rigorously.
- Don’t try to hide bad news in the analytics results. Explain the reasons for the bad news.
- Don’t misrepresent the data to emphasize something that isn’t important or de-emphasize something that is important.
- Don’t attempt to skew analytics results to favor a specific outcome.
- Always protect the trust clients place in analytics by being honest and correcting mistakes as quickly as possible.
- When the quality of data comes into question, let the client know that the results may be unreliable.
- Always add value to the client’s business and give clients full ownership of their own data.
- Follow all data governance rules to the letter and ensure that employees and clients understand the regulations and compliance matters that relate to their customers’ data.
- Data analytics is a process of testing and iteration to continually experiment with results and then apply the lessons of each test. The freedom to make mistakes early leads to fewer errors in the future.
- Data analytics is intended to have a positive impact on the profitability of business clients of accountants and finance professionals. The process converts data into knowledge that leads to more effective business decision-making.
- Be accountable for the data and transparent about any potential shortcomings or mistakes in the analytics process. Step back from the technical details of the work to view the analytics process and results from the client’s perspective.
Big data in accounting
- Their analyses can now include unstructured data, such as audio, video, and images, as well as email and text files, social media posts, website content, and information gleaned from mobile devices. In the past, analysts were limited to analyzing data that could be converted to a structured format, usually a spreadsheet or relational database.
- Data analysis is enhanced by using visualization software that offers accountants and their clients unique views of the data that supports their decisions.
- Auditors are now able to process larger amounts of accounting data in a variety of formats simultaneously, which means their work is done more quickly and is more accurate.
- Big data improves risk analysis by providing accountants with access to more timely data. Advanced analytics tools allow them to process the data quickly.
- Performance measurement (used by 100% of firms that have implemented big data)
- Strategy formulation (74%)
- Research and development (50%)
- Order fulfillment (25%)
- Rationalizing products and services (25%)
- Data governance and privacy: The extensive use of personal information requires that accounting firms monitor their compliance with regulations relating to the security and appropriate use of sensitive information in big data applications.
- Gaining business insights: Accountants and other finance professionals will need to work more closely with business managers to understand the processes and functions that they rely on and better support the business decisions that affect those processes.
- Risk management: Business managers need a better understanding of the external forces that impact their operations, including regulations, supply-chain disruptions, and threats to the company’s reputation and brand. They also must be made aware of obstacles to the company’s growth plans and product strategies.
Resources on big data in accounting
- Journal of Accountancy, “Resources for Teaching Data Analytics in Accounting” — Tools for studying the use of data analytics by accountants including software programs, case studies, white papers, and training videos
- Strategic Finance, “The Impact of Big Data on Finance” — An overview of the results from a survey of accountants that examines the most common sources of big data for accounting firms and their strategies for implementing the technology
Big data in finance
- Machine learning and other advanced analytics techniques are needed to account for variations within small data samples.
- Natural language processing, speech recognition, and image processing allow the systems to work with unstructured data beyond the capabilities of analyses that are limited to data housed in spreadsheets, databases, and other structured sources.
- Volume refers to the increasing size of the datasets that the financial industry must process and analyze, which now measure in the petabytes (one petabyte equals 1 million gigabytes).
- Variety relates to the many different data sources that big data applications tap to create analyses that more accurately represent a business’s financial operations today and in the future.
- Velocity refers to the high speed at which data is created, which requires distributed processing techniques to collect and curate information in many different formats and contexts.
- Veracity describes the quality of the data being analyzed, especially whether the data is consistent and certain. It also relates to the data’s ready availability and controllability.
- Big data is a key to algorithmic trading, which uses a computer program to execute financial trades much faster than human traders can. The algorithm uses mathematical models to define the parameters and instructions that control the trade decision, including timing, price, and quantity.
- Big data helps financial firms confirm they comply with all government regulations, particularly those relating to data security and privacy.
- Big data techniques help financial firms improve the quality of the data they use in their analyses, which leads to more accurate results and enhanced business decisions.
Resources on big data in finance
- Mathematics, “Identifying Big Data’s Opportunities, Challenges, and Implications in Finance” — An examination of how big data has reshaped the finance industry and altered existing business models
- National Bureau of Economic Research, “Big Data in Finance” — A discussion of the impact of big data on corporate finance, market microstructure, asset pricing, and other areas of finance
Applications of big data analytics in finance
- Techniques for improving the customer experience by using purchase histories, demographic data, and behavior tracking to offer personalized financial services, such as making product recommendations
- Methods for automating and otherwise enhancing the efficiency and quality of business processes, such as algorithmic trading process automation and credit risk determination
- Approaches to identifying and mitigating potential risks quickly to minimize financial exposure and capitalize on financial opportunities
NASDAQ’s use of Amazon Web Services Simple Storage Service
JP Morgan Chase’s use of Apache Hadoop
Acorns’ use of big data to revolutionize micro-investing
Resources on big data analytics in finance
- SMB Compass, “Emerging Accounting Trends & Statistics for 2021” — An explanation of the impact of the COVID-19 pandemic on the adoption of big data and other technologies in accounting and finance
- Emerj, “Predictive Analytics in Finance — Current Applications and Trends” — A description of AI-based financial analysis applications from Teradata, Dataiku, DataRobot, and RapidMiner
Data mining in accounting
- Identify potential fraud
- Better organize accounting data
- Predict audit opinions on financial statements
Detecting fraud patterns in accounting databases
Enhanced categorization, clustering, and association of accounting data
- Categorization, also called classification, places each item contained in a dataset in a specific category, class, or group that shares characteristics and attributes.
- Clustering automatically creates a meaningful or useful cluster of data objects based on identifiable patterns.
- Association discovers patterns in the data based on relationships between the items in a single transaction, such as identifying products that consumers frequently purchase together.
Data mining techniques that predict audit opinions on financial statements
- A qualified opinion indicates that the auditor found some material issues in the financial report in terms of the firm’s accounting policies but no misrepresentations of the company’s financial position.
- An unqualified opinion indicates that the company’s financial reports are fair and appropriate, in compliance, and without any exceptions.
Resources on data mining in accounting
- Syntelli Solutions, “Using Big Data for Financial Fraud Prevention” — An examination of the relationship between data mining and financial fraud detection from the perspective of forensic accountants
- Financial Innovation, “Comprehensive Review of Text-Mining Applications in Finance” — Techniques for mining financial data in the form of unstructured text, such as press releases, news reports, and web pages
Accounting and data science
Helping an airline improve safety, reduce costs, and better serve customers
Sharing GPS Data with Banks to Prevent Fraud
Detecting fraud in credit card and banking payments
- Data credibility assessments are improved by automating gap analytics, which identifies missing values in sequences of transactions and automatically searches public data sources to fill in the gaps.
- Duplicate transactions are a common fraud method that charges the bank twice for a single transaction. AI-based systems are more accurate than rules-based approaches at discerning true accidental duplicate transactions from fraud attempts.
- Account theft and unusual transactions are typically thwarted by using behavior analysis: Consumers tend to follow similar patterns over time, so departures from those patterns are flagged as potential fraud. Machine learning algorithms are adept at analyzing behavior and deviations from that behavior to determine the likelihood of fraud.
Resources on accounting and data science
- DataToBiz, “Data Analytics Helping Accountants Excel! Role of Data Science in Accounting” — The outlook for data science in accounting from the perspective of a corporate chief financial officer
- Towards Data Science, “How AI Is Changing Financial Planning and Analysis” — A description of robotic process automation (RPA) as it applies to business processes, and financial analytics as an offshoot of predictive analytics and prescriptive analytics