AI Financial Modeling: A Paradigm Shift in Finance

The financial sector today has shown to be undergoing major changes, with large amounts of new data being generated. Through the use of financial modeling, enterprises are capable of leveraging this data in a variety of ways to maintain their progress. 

Traditional financial modeling, however, cannot keep pace with the dynamic nature of the industry’s evolution. Therefore, the rise of AI financial modeling comes as a welcome relief to companies looking to supercharge their financial journeys.

This article aims to guide firms through the intricacies of financial modeling, explain how financial modeling prep can be automated, and elucidate the advantages it brings to adopters.

Understanding Financial Modeling

Financial modeling can be understood as the process of creating a roadmap for a company’s financial future using elements such as past data, assumptions about the state of the market, and the company’s own financial projections. 

All of these elements are combined to derive insights for decision making, financial forecasting, and to plan for the future growth of the enterprise. In the process of financial modeling, simulated scenarios are created to judge the performance of the company, evaluate investments, and also assess decision risks.

Many firms continue to use tools such as Microsoft Excel to create spreadsheets and generate financial models based on them. These tools offer several options to analyze statements of income, balance sheets, and cash flow statements, which have made them a mainstay in the industry. 

However, as complexity increases, traditional financial modeling software can no longer effectively deliver results in a timely manner. It is here that automation can make the difference.

The Rise of AI Financial Modeling

The use of AI for financial modeling is the best way to combat the drawbacks of traditional financial modeling tools. It brings many innovations that provide a boost to individual components of the financial modeling process. There are several ways to take advantage of AI financial modeling:

1. Data collation and analysis 

One of the most time-consuming parts of the modeling process is the collection of data from different sources. AI has the ability to combine and categorize large sets of data quickly and accurately. 

Advanced financial modeling software can also utilize AI to help analyze the collected data and deliver a more in-depth, nuanced financial analysis.

2. Task automation 

Another area where time loss occurs is in resource allocation for repetitive routine tasks such as data entry and generating reports. 

AI takes over from the humans performing such tasks and frees up these resources to be allocated elsewhere. This also leads to fewer errors overall, bettering the quality of the final model.

3. Predictive analytics

Large sets of data, especially historical data collected across long time periods, may be beyond the ability of human analysts to deal with. AI addresses this issue by bringing greater computing power as well as a deeper understanding of the data. 

This can help deliver better predictions related to stock prices, market movements, potential future financial risks, and trends in the economy.

4. Natural Language Processing (NLP) 

AI also has the capacity to read and comprehend textual data through the use of NLP technology, which accounts for 26% of the overall use of AI in the industry. This means that manual transfer of data and insights from financial reports, news articles, and earnings calls are no longer necessary. 

Financial modeling software can automatically read and assess this data to deliver deep financial insights on market sentiment and potential impact.

5. Risk assessment and management 

AI can examine various market factors to perform scenario analyses. Within these analyses, the financial modeling software can detect risks in terms of credit, the market, and operations, and assess their impact. Using this data, preventive measures can be thought of in a timely manner.

Benefits of AI Financial Modeling

Given the different ways in which AI can be incorporated into financial modeling, companies can rely on automation for a host of benefits:

1. Streamlined workflows 

Financial modeling software uses AI to save time by automating time-intensive and complicated parts of the existing process, such as data entry and model generation. It also improves the organization of processes by creating a traceable outline of the workflow to be followed, which links every single process with the next in a coherent, streamlined fashion.

Most of these solutions are easily integrable into the regular functioning of the firm, thereby preventing workflow disruptions.

2. Error reduction

One of the fundamental advantages of AI financial modeling solutions is the prevention of errors at every stage of the financial modeling process. 

Fewer errors committed during data entry and analysis ensure greater financial forecast accuracy. With more reliable financial projections, the business decisions made can be more impactful and ensure greater financial success.

3. Lower technical proficiency requirements

Machine Learning and NLP technology in AI financial modeling software helps companies resolve the proficiency barrier. 

With ease of use being a key element of these solutions, more members of an organization’s financial team can build and run financial models — it doesn’t take an expert. This makes the entire process more egalitarian and greatly enhances efficiency by eliminating the need for operational experts for oversight.

4. Better data volume management 

Using AI for financial modeling allows enterprises to organize and analyze larger and larger data sets in fewer cycles, no matter the disparity of data sources. 

This means that geographical limitations related to data sourcing from across operational locations are removed, and newer technology developed across locations can be directly incorporated into the modeling process. 

5. Increased adaptability

With the help of AI tools for financial modeling, companies can improve their agility in dealing with the changing financial environment. 

As market trends and conditions evolve, AI financial modeling allows for real-time business decisions to be made. This ensures that all processes remain flexible and reduces financial risks.

6. Greater security

Human analysts can be hoodwinked and may commit errors, leading to security breaches in financial information. There is also the possibility of fraud, which would cause significant harm to the firm. 

AI-powered fraud detection systems are designed to combat this by regularly analyzing transactions for discrepancies. They also provide information that can be used to design tighter security measures to safeguard financial data.

7. Better compliance and regulation

One of the tertiary areas of manual reporting is the generation of reports that showcase compliance. Additionally, oversight must be maintained to ensure financial regulation continues to be adhered to. 

AI can help automate these tasks to reduce the risk of regulation violations and streamline reporting timelines.

Implementing AI in Financial Modeling

To avail the above benefits, enterprises must strategically implement AI across their financial processes. There are certain considerations, however, that must be made to ensure proper implementation. Below are some of the steps to consider:

1. Define objectives:

The objectives of the model must be clearly defined to help guide the implementation process.‍

2. Data collection:

Relevant data sources must be identified to ensure the right data is being collated to avoid misrepresentations and inconsistencies. 

3. Model selection and training:

An appropriate AI model that meets the requirements of the firm and the data being analyzed must be selected. The selected AI model must then be trained using historical data to help create future patterns to be identified or followed.

4. Workflow integration:

Once the AI model is trained, it can be integrated into the existing financial modeling workflow in a seamless manner.

5. Maintenance:

The model must be regularly monitored to evaluate its performance and to make adjustments as needed.

6. Compliance testing:

While the AI model performs its tasks, checks for regulatory compliance must also be conducted. This is especially required to ensure data privacy.

7. Feedback and iteration:

Finally, once the financial modeling solution is implemented, feedback must be sought from users and stakeholders to iterate on these models for the future.

Security Concerns With AI for Financial Modeling

Despite the many advantages it presents, AI financial modeling still comes with certain risks, mainly in terms of AI-related security. 

Firstly, all financial data that is gathered and analyzed by AI during the modeling process is vulnerable to misuse in case of a breach. 

Secondly, many AI-powered financial solutions may use third-party elements within their software, creating a potential risk of accountability during a software malfunction. This can be addressed by improving cybersecurity measures to protect data both within and outside the organization. Additionally, all confidential data can be encrypted such that only the company’s AI financial modeling software can decrypt it, ensuring data safety at every point.

Automate Financial Modeling With Savant

The ever-evolving financial sector requires technology that can keep pace with its changes at all times. Enterprises have begun turning to AI-based solutions for cutting-edge financial models to help them in their financial growth journeys. 

Savant’s intuitive cloud-based analytics automation solution presents organizations with the required tools to optimize their AI financial modeling process. It eliminates the need to struggle with traditional reporting methods and allows for error-free financial modeling and incisive cash flow forecasting.

To begin your financial automation journey and benefit from industry-leading financial modeling software, contact our team today!