Top AI/ML use cases in corporate finance

Top AI/ML use cases in corporate finance

The finance sector has shown a steep rise in the application of AI and machine learning (ML) to improve outcomes for both businesses and customers. Some use cases include financial analysis, reporting, forecasting, process automation, security, customer satisfaction, and many more.

Corporate finance teams are  adopting machine algorithms to automate mundane, time-consuming processes while offering a more streamlined and personalized customer experience.

Even the role of the finance office has shifted from traditional reporting and analysis to becoming a position that drives strategy and forecasting, enhancing performance across all business operations.

In a nutshell, the AI/ML technologies for finance can minimize the number of potential risks, automate processes, and enhance revenues owing to better productivity and customer experience.

Here, we shortly describe some AI and ML use cases for corporate finance:

1. Data governance

Poor data governance can affect the regulatory compliance initiatives of an organization, causing data privacy, usability, and integrity issues. The right data governance framework can help organizations maximize benefits and mitigate risks using AI to provide actionable insights that help streamline processes and improve decision-making.

2. Reprioritize employee focus

Modern financial reporting allows employees from all departments and knowledge levels to empower themselves using financial data. Using modern dashboard technologies, employees can visualize critical financial information to measure the performance of their activities and rapidly generate ad hoc financial forecasts. With this, employees can focus on other value-added tasks rather than spending time generating reports manually.

3. Machine learning-based forecasting

Through ML-based forecasting, organizations can more accurately predict future revenues, expenses, and cash flows by using ML-based forecasting. Machine learning allows businesses to conduct more complex and sophisticated querying of data extracted from different sources and produce accurate forecasts by reducing the forecasting cycles from weeks to months to minutes.

4. Leveraging all data, not just financial data (in forecasting)

Organizations can also go beyond simple historical and financial data when creating forecasts. For example, a team could fine-tune their financial forecasts based on other factors such as the company’s ML-based demand forecasts. By monitoring external factors that impact businesses, an organization can enable rapid adjustments to forecasts which allows them to react to changing market trends with greater confidence.

5. Gaining a holistic view of all financial data

Centralizing financial data on one platform using cloud-based services can promote operational planning through greater collaboration across the organization. By centralizing the data, it becomes accessible to the management from other business areas, and they can incorporate financial information into their planning.

6. Integrated dashboards

A financial dashboard can help track all relevant finance KPIs, expenses, sales, and profits. Tracking these details can help enable effective cash management to meet the organization’s financial objectives. Using financial dashboards, you can unify all the financial data and generate actionable insights in real-time.

7. Near real-time reporting

Real-time financial reporting allows organizations to issue a wide array of reports faster and monitor and react to issues in near real-time. Organizations can pull data from different entities and sources using cloud-based solutions, helping to eliminate manual data entry, handoffs, and potential input errors. It also allows flexibility in how data is leveraged to facilitate organizational decision-making, fraud detection & prevention, and audit efficiency in real-time.

8. Automated document verification using Natural Language Processing (NLP)

Leveraging technologies like NLP, Optical Character Recognition (OCR) and ML algorithms can help transcribe conversations in real time, which can then be used to extract data, allowing for AI to make decisions based on this information.

This helps an organization access documents for any discrepancies and even identify potential risks. It can also help ensure the documents comply with corporate policies and regulatory standards.

9. Compliance automation

Compliance automation uses the power of AI to simplify the compliance procedures for an organization. Once the compliance is automated, organizations can access the compliance status and audit information using a single dashboard. It can help mitigate risks, catch potential weaknesses, and minimize inadequate and inaccurate reporting mistakes. As laws and regulatory requirements update continuously, compliance automation processes become more productive, simplified, and accurate for internal auditors and senior management.

10. Repository for tax code & legal documentation

AI can help accelerate different processes associated with documentation like classification, defining taxonomy, and extracting required data. OCR running in conjunction with ML allows scanning and converting a large number of paper-based documents into machine-readable text, that could be stored and accessed by anyone with a single click. ML algorithms can detect a company’s capital gains and charitable contributions, manage assessment notices, and extract information like account numbers, payments to tax bills, and other key information. The organization can then leverage the data to pay taxes and file returns.

11. Access customer behavior to better understand risk

In 2020, Gartner analyzed 796 financial statements and revealed that provisions and write-offs of bad debts increased by 25.8%, resulting in negative cash flows, missed investment opportunities, and a heightened risk of bankruptcy.

AI can help predict which customers are likely to pay, late in paying, or will not pay the debts at all. Once the customer data is analyzed based on different parameters such as industry type, credit rating, product purchase, and sales, a forecast can predict how likely a customer can pay debts or allow extended credit if required. Alternatively, identifying likely non-payers can help with customer qualification and credit approvals.

Next steps

Since finance is one of the most critical functions of an organization, the right solution can help improve analysis, forecasting, reporting, regulatory compliance, and employee efficiency. Neal’s modern finance solutions are designed to operate within finance-related compliance frameworks and can be tailored and customized to meet an organization’s unique needs and requirements.

Learn how we helped a major global organization produce thousands of forecasts over two years with machine learning. Interested in knowing more about this solution, contact us to get started!