2021 Year-End Tax Planning for Businesses
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Rose Report: Issue 48
By Ulker Dagdelen, Director, Tax, Rose Financial Solutions
Tax planning should be addressed throughout the year and be an integral part of financial planning. As 2021 wraps up, we suggest year-end tax planning to take a close look at your current tax strategies to make sure they still meet your needs and take any last-minute steps that could save you money.
Generally deferring income and accelerating deductible expenses in the current year is a good idea. However when expecting tax rates to increase or when you expect to be in a higher tax bracket next year, the opposite may be beneficial. In the current environment it makes sense to consider accelerating income such as bonuses, capital payouts, stock sales or property sales into 2021. Additionally deferring expenses into the following year will make the deductions more valuable.
Expected Changes:
- Top corporate tax rate increase: rate would jump to 26.5% from 21% for taxable income over $5 million; a lower 18% rate would apply to taxable income of $400,000 and under, while the 21% rate would continue to apply to taxable income above $400,000 but not greater than $5 million; once a corporation reaches $10 million in income, the graduated rates disappear, and all income would be taxed at 26.5%
- Qualified business income deduction limitation: the 20% qualified business income deduction under IRC section 199A would be limited to annual deductions of $500,000 for married filing joint returns and $400,000 for single and head-of-household filers
- Top ordinary income rate increase: rate would bump up to 39.6% from 37%
- Top capital gains rate increase: long-term capital gains and qualified dividends would be taxed at 25%, up from the current 20%; this would generally be effective for gains recognized after Sept. 13, 2021 (an exception applies to transactions that are under a binding contract but have yet to be executed)
Additional items to note:
- Form 1099s due January 31, 2022
- Partnership and S-Corporation return due date March 15, 2022
- Corporation and individual return due date April 15, 2022
- Pass-through entity-level tax election (PTE): for the state PTE, pass-through entity taxpayers, such as partnerships and S-Corporations can elect to pay and deduct state income taxes at the entity level return rather than on the personal income tax returns of the individual partners and owners
- Net Operation Loss incurred in tax years 2018 or after may only offset 80% of taxable income & all NOLs generated can only be carried forward
- For 2021 and 2022, 100% deduction for business meals provided by a restaurant
- Research & Development costs for years after December 31, 2021 must be capitalized and amortized for 5 years with amounts incurred outside the U.S. amortized for 15 years
- Business interest expense limitation is 30% (was 50% for 2020) of the adjusted taxable income adding back for depreciation, depletion, and amortization for December 31, 2021; after this date the add back will not be computed and therefore more disallowed interest expense
- Jurisdictional evaluation should be performed at least annually to ensure adequate tax compliance.
- State and local sales tax nexus needs to be reviewed, as many states have updated rules related to remote employees.
- 2021 401(k)s contribution limit is $19,500 and 2022 401(k)s contribution limit is $20,500 – with $6,500 catch-up for both years
- 2021 SIMPLESs contribution limit is $13,500 and 2022 SIMPLEs contribution limit is $14,000 – with $3,000 catch-up for both years
- 2021 and 2022 Traditional and Roth IRAs regular contribution limit is $6,000, with $1,000 catch-up
- 2021 HSA contribution limits: self-only $3,600 and family coverage $7,200 and 2022 HSA contribution limits: self-only $3,650 and family coverage $7,300
- 2021 Annual gift tax exclusion is $15,000 and 2022 Annual gift tax exclusion is $16,000
- 2021 Above-the-line charitable deduction: non-itemizer individuals can deduct $300, joint filers $600, for cash contributions to qualifying public charities
Rose Financial Solutions is here to help you take a fresh look at your tax and financial well-being. We also offer jurisdictional monitoring to track your activities nationwide and make registration recommendations when nexus is met for sales tax, annual reporting, and business license renewals.
Please schedule an introductory call if you would like to discuss your tax situation so we can develop a customized plan to minimize your tax liability.
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.
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Issue 72 - Data Driven and AI Enablement Strategies for 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
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
