Financial modeling has become a cornerstone of effective business management, and those operating in the tech sector are no exception. From high-growth startups to well-established industry giants, tech companies rely on financial models to evaluate past performance, assess current operations, and project future economic outcomes. Beyond enabling informed budgeting, capital allocation, and fundraising decisions, robust financial modeling helps companies identify and mitigate financial risks, as well as prove business value to investors.
At the same time, conducting financial modeling for a tech company can be a challenging task, as it can face technological and organizational hurdles, ranging from data siloes to employee resistance. Whether you are planning to use financial risk modeling software, spreadsheet-based approaches, or specialized fintech tools, you should be prepared to navigate and mitigate these challenges to ensure the efficiency, reliability, and accuracy of your models.
In this article, experts from the fintech development company Itransition explore the common pitfalls that tech companies face when implementing financial modeling tools and building financial models, as well as share some practical tips to navigate them.
Maintaining source data integrity
To provide informative and reliable forecasts, financial modeling tools require data from a variety of sources, including both internal (such as ERP systems and accounting systems) and external (such as financial news aggregators and stock screeners). Simply aggregating this data with a financial modeling tool, however, does not guarantee accurate analytics. To minimize erroneous and misleading insights, which can lead to incorrect financial decisions, a company must ensure that the data used for analysis is accurate, consistent, and complete. Ensuring that the tool provides relevant output based on the ingested data and generates forecasts properly is also critical.
Developing a robust data management strategy is key to overcoming the aforementioned challenge. To ensure the quality and integrity of the gathered data, a tech company should first clearly define criteria for accuracy, completeness, and consistency, setting them as quality standards for its data management strategy.
After outlining a data pipeline (a sequence of processes for data collection, transformation, and delivery to the target financial modelling system), the company should also include data quality checks. To ensure data integrity, it is recommended to perform checks at various stages of the data pipeline.
A company can use the set of tools to automate and accelerate these checks, with MobyDQ, Soda Core, Informatica, and Talend among the examples. When deciding on which ones to use, it is advisable to prioritize AI-enabled tools with comprehensive data cleansing functionality. Not only can they detect issues in data as it flows, but also correct them automatically based on predefined rules before the data reaches the target system, which can help a company maximize data integrity.
Along with building a data strategy, the company should also clearly define the rules of the financial modeling process to ensure the provided output is accurate and relevant. For example, if a company uses an automated financial modeling tool, the strategy should determine which data variables to rely on for creating forecasts, which assumptions to consider (such as cost inflation and market disruptions), and which logic and rules to follow during forecast generation.
Providing efficient employee training
Effective financial modeling often requires domain expertise, encompassing the interpretation of financial statements and the application of financial mathematics, as well as solid technical skills, such as programming in Python or R and database management. However, the availability of qualified specialists with both financial and technological expertise has always been limited, and the situation is likely to worsen in the near future, given that the global shortage of financial and accounting talent is intensifying.
While large organizations can attract rare talent through brand recognition and offering higher salaries, small tech firms and startups must look to alternative approaches. One of the strategies is reskilling or upskilling existing employees, enabling them to handle finance modeling efficiently. While this is generally a sound option, training calls for additional resources. It also takes time, which can cause delays and operational disruptions.
Tech companies can accelerate employee training and make it more cost-effective by leveraging online learning platforms, such as Wall Street Prep, CFA Institute, and Wharton, reducing expenses associated with traditional in-person training. Introducing mentoring programs and encouraging peer learning among employees is also an effective way to promote knowledge sharing. Along with assigning the role of mentors to more experienced employees, companies can develop clear guidelines on how mentors should train their peers and set up a regular schedule of mentoring sessions to establish a sustainable internal knowledge-sharing ecosystem.
Overcoming employee resistance
Technology adoption inevitably encounters employee resistance, and the implementation of financial modeling is no exception. Without proactive onboarding management, tech businesses risk underutilizing new tools and failing to realize the intended business value.
Successful software adoption requires a combination of targeted training and clear and consistent communication. Company leaders should clearly articulate the reasons behind the financial modeling initiative, emphasizing how financial models can contribute to more confident decision-making and improve the company’s financial health. Identifying and encouraging early adopters as change champions can further accelerate adoption across teams, fostering a culture of advocacy.
Final thoughts
Financial modeling software is a valuable tool that allows technology companies to assess their current financial health, predict how it might change in the future, and inform budgeting, strategic planning, investment, and other financial decisions. However, implementing new financial modeling technology is challenging, and companies should be prepared to overcome numerous pitfalls associated with this process to ensure it brings meaningful and accurate results.
Whether you plan to build a solution on top of a market-leading fintech platform or create a financial modeling solution from scratch, partnering with an experienced technology provider is recommended. A professional software partner can help you make a sound technology choice, as well as accelerate and streamline its implementation, minimizing technology costs and ensuring financial models deliver reliable insights that support sustainable business growth.
