Top Finance and Accounting Industry Trends for 2022

Share this article:

Rose Report: Issue 48

By Ted Rose, CEO, Rose Financial Solutions

For the past two years, COVID-19 has presented many challenges to the business world, driving companies to implement transformative changes to the way they operate, including how they manage their finance and accounting functions. The most impactful changes have occurred in the area of technology, a trend that will continue in 2022 and beyond. To quote author Stewart Brand, “Once a new technology rolls over you, if you’re not part of the steamroller, you’re part of the road.” Indeed, companies that haven’t made an investment in finance and accounting technology are far behind the competition in having a clear understanding of their financial position today and into the future — a competitive edge that is essential as we move past the health crisis and back to ‘business as usual.’ That’s why technology is the common thread in the following finance and accounting industry trends for 2022.  

Remote Workforce

The pandemic forced us to reimagine how and where we work in order to keep employees safe and healthy. With that, many knowledge employees have settled into working remotely and post pandemic want to split their time between home and the office. In fact, according to the April 2021 report, What employees are saying about the future of remote work , 56% of employees want their companies to implement flexible hybrid virtual-working models. In order to meet employees’ demands, it looks like remote work is here to stay. With this changing workplace landscape, businesses are moving towards finance and accounting systems that can be securely accessed by employees whether they are in the office or working remotely and are configured to provide real-time data that is timely and accurate.  

Risk of Fraud

The Association of Certified Fraud Examiners (ACFE) reports that fraud risks are evolving in the wake of the pandemic. And while businesses have become more acutely aware of the risk of fraud that occurs from outside forces, it is often hard to detect the threat from within. That’s why it is important to utilize accounting best practices that not only deter employees or others from committing fraud, but also provide a clear view of the business’ finances.

The risk of fraud grows when appropriate control structures are not developed. With that in mind, many companies are implementing finance and accounting system that automate procedures and enforce protocols that only allow individuals with authorization to access, review, and approve payments.  

Data Security

Cyber attackers view the disruption brought on by the pandemic as an opportunity to step up their game and prey on of the vulnerability of a company’s remote workforce. Employees who work from home are at a significantly higher risk than those who work in offices because residential connections are less secure. This results in cyber criminals being able to gain access to a company’s network—and their financial information—more easily. That’s why there is movement towards hardening access to corporate data through security measures such as multi-factor identification, encryption, and security awareness training.  

Optical Character Recognition (ORC)

The necessity to retain data in digital form is becoming more crucial as companies increasingly rely on data-driven information such as financial forecasting, key performance indicators (KPIs), and cashflow analysis to make better business decisions. Not all accounting activities, however, are paperless, and manually converting printed invoices to digital data takes time and is at risk of human error. By utilizing ORC, data entry is automated, more accurate, and a less time-consuming process.  

Artificial Intelligence and Machine Learning

Artificial intelligence (AI) and machine learning (ML) are having a major impact on how businesses plan for the future, and the finance and accounting profession is already embracing these technologies. While the full benefit of AI and ML may take years to come to fruition, companies that want to head down a path of becoming an industry leader will need to build an infrastructure that is able to benefit from the promise of these technologies. With automated processes in place, AI and ML will code, route, and analyze information, and provide additional insight based on future financial results based on market data trends.

Financial Guidance

Today’s business leaders expect finance and accounting services that go beyond ensuring compliance and maintaining records. They also want proactive strategic assistance and direction to help them in making better business decisions. This paradigm shift necessitates companies to invest not only in, cutting-edge technology that allows for greater visibility into financial performance, but also highly experienced finance and accounting personnel.  

As these trends show, the finance and accounting industry will continue to focus on technology advancements in 2022. However, technology will not replace accounting and finance professionals, but rather provide them with the opportunity to focus on data analysis, process development, and strategic initiatives.  

If you would like to learn more about these trends and how they can help your company succeed in 2022, please schedule an introductory meeting.

This content is for information purposes only and should not be considered legal, accounting or tax advice, or a substitute for obtaining such advice specific to your business.

Visit Us On:

By Matthew Scroggs January 10, 2024
Issue 72 - Data Driven and AI Enablement Strategies for 2024
By Matthew Scroggs January 10, 2024
Recent findings from Pigment’s Office of the CFO 2024 survey highlight a critical issue for business leaders – the prevalent use of inaccurate data in their decision-making processes. The survey reveals that a staggering 89% of finance leaders are basing their decisions on incomplete or faulty data. The foundation of successful business strategies depends on the quality and accuracy of the decisions made. As businesses navigate expansion and heightened competition, the reliance on data-driven insights has become critical. Harnessing the transformative power of accurate, reliable data enables informed and effective decision-making. Businesses with financial clarity will outpace companies that struggle with flawed data. Financial visibility will help businesses avoid common pitfalls while shaping a future oriented strategic vision. Why Is Most Financial Data Flawed? Financial Data often ends up flawed due to several factors. Disparate systems and fragmented processes within an organization can cause increased inaccuracies over time. The lack of standardization of data within an organization introduces complexities and leads to inconsistencies in data handling. Nomenclature and connectivity issues further compound the problem, making it challenging to establish a framework for data organization. When these issues persist, they pave the way for flawed data, hindering accurate analysis and decision-making. Improving Financial Data with a “Single Source of Truth” Addressing the complexity of inaccurate financial data requires a strategic approach. Streamlining systems and processes and implementing standardized, data-oriented procedures across departments can mitigate inaccuracies stemming from disparate systems and fragmented processes. Moreover, establishing a unified nomenclature and resolving connectivity issues are pivotal to ensuring data integrity. By instituting a cohesive framework for data organization and management, businesses can tackle the root causes of flawed financial data. Establishing a single source of truth consolidates data into a single data structure. This allows for the streamlining of processes, reduction of complexity, standardization of nomenclature and improved connectivity. In essence, a single source of truth reduces errors by ensuring everyone in an organization refers to the same accurate information. This unified data hub speeds up decision making and lays the groundwork for integrating AI into future financial operations. Enter Easby, a system of engagement that standardizes financial activities and data while improving data integrity. As a CFO Co-Pilot, Easby streamlines data handling and reporting, allowing leaders to make better decision based on better information. Easby reduces administrative activity and promotes data-accuracy, improving decision-making and driving companies toward success in our competitive business environment. Easby connects with your accounting system of record to become a “single source of truth”, centralizing data and refining processes. By streamlining data collection and reporting, Easby empowers leaders to refocus their efforts on strategic growth initiatives. To discover how Easby can become your CFO Co-Pilot while fortifying the future of your organization, we invite you to schedule an introductory call with Rose Financial Solutions (ROSE). Schedule an Introductory Call
By Matthew Scroggs January 10, 2024
Technology, Data and Automation are transforming decision-making, especially with the democratization of Artificial Intelligence (AI). This transformation is especially pronounced within finance, where AI's emergence is influencing financial system strategies, placing a premium on structured data for AI-driven initiatives. However, the ability to utilize AI effectively heavily relies on data organization and security. Organizing data includes data consolidation, categorization, and tokenization. This organization can help establish the groundwork for your company to benefit from the full potential of a wide-range of AI-driven use-cases. Consolidating Diverse Data for Unified Insights Data consolidation includes merging and unifying diverse data sets from multiple sources into a single source of truth. Let’s consider a corporation that operates across various states. Each division might maintain financial and operational records, such as sales figures, payroll, operational expenses, and inventory in disparate systems. Data consolidation in this scenario involves merging these diverse datasets from different divisions into a singular, centralized system. For instance, combining sales data from different regions, integrating it with payroll and inventory records, and aligning financial reports across divisions creates a comprehensive overview of the company's overall performance. This consolidated data allows for better analysis of revenue streams, cost optimization strategies, and more accurate forecasting across the entire organization, aiding in strategic decision-making for the whole company. Enhancing AI Precision through Categorization Categorization involves sorting data into specific items or categories based on various parameters or attributes. It's about organizing and labeling data in a structured manner. For example, in accounting, data categorization refers to sorting expenses into a variety of dimensions, such as general ledger codes, department codes, project codes, etc. These codes are normally broken down into logical categories that help users and AI understand that certain vendors are related to travel and others are related to office supplies, or utilities. In AI-driven strategies, categorization is paramount for contextualizing and organizing information effectively. By classifying data into relevant categories or items, AI systems can understand the nuances of different data sets. This categorization allows for more precise analysis, facilitating the extraction of actionable insights and comparisons that are crucial for decision-making. Tokenization for Advanced Data Efficiency and Security Tokenization is the segmentation of complex data into smaller, more manageable units known as tokens, each representing individual pieces of data or information. This process primarily focuses on maintaining confidentiality when inputting data into AI systems. Its core objective is safeguarding sensitive data by substituting identifying information with distinct tokens or representations. By implementing tokenization, organizations create a protective barrier around sensitive information, like personal or financial data, thwarting AI from associating the data from a specific entity. Tokenization ensures that AI algorithms work with transformed data. For instance, tokenization involves converting sensitive data, like vendor names, into random tokens in financial transactions. This not only enhances security by safeguarding sensitive information but also streamlines data analysis by reducing the complexity of the dataset. In AI strategies, tokenization is a critical step. By segmenting data into tokens, AI algorithms can more effectively identify patterns, trends, and correlations within the information, ultimately enabling more accurate predictions and insights, all without providing the AI with sensitive information. Leveraging Integration Opportunities with AI Consider a company working to streamline its accounting processes. The organization creates a unified database through data consolidation and tokenization. The integration of AI technology allows for the use of machine learning to automate transaction coding, a move that significantly reduces manual workload while improving processing accuracy. Other examples of AI integration include automating graphic analysis and categorization creation. For instance, AI-driven tools can autonomously generate visual representations of complex datasets. Moreover, within categorization, AI systems excel at continuously refining and automating the sorting of diverse data sets into specific categories or segments, ensuring accuracy and efficiency in data handling. Finally, AI-driven tools leverage historical patterns to track and analyze financial behaviors. For instance, by examining past expenditures, these systems identify trends, anomalies, and potential cost-saving opportunities. This level of insight allows businesses to make more informed decisions regarding budget allocation, identifying areas for optimization and possible financial risks. Scaling Efficiently Through AI-Driven Strategies By merging AI-driven strategies with data management, businesses gain adaptability. This agility powers informed decisions, intelligent resource allocation, and proactive risk management. This approach isn't just about navigating competition; it's about efficient scaling and strategic growth, representing a shift towards growth while benefiting from financial clarity. This strategic combination empowers businesses to thrive, evolve, and seize opportunities in a constantly changing business environment. Schedule an introductory call with us today to explore how optimizing your data strategy can enhance your adaptability, drive informed decisions, and propel your business towards scalable growth. Schedule an Introductory Call
More Posts