AI vs. Traditional Finance: How AI is Disrupting the Traditional Banking Landscape

The financial sector is facing an increasingly complex and competitive landscape, and one way that banks and financial institutions are responding is through the use of artificial intelligence (AI).

With the ability to analyze large amounts of data, identify patterns, and automate processes, finance AI is proving to be a valuable tool for improving efficiency and effectiveness in the financial sector.

In this blog post, we will explore the changing financial landscape, the challenges faced by the finance industry, and how finance AI can help overcome these challenges.

Financial Landscape and Emerging Technologies 

The financial industry is rapidly changing with the emergence of new technologies, such as blockchain, digital currencies, and finance AI. According to a report by ResearchAndMarkets, the global AI in the banking market is expected to grow at a compound annual growth rate (CAGR) of 31.38% from 2020 to 2025. This growth is being driven by the increasing adoption of AI-powered solutions by banks and financial institutions to improve their customer experience and operational efficiency.

One of the key advantages of finance AI is its ability to automate processes and reduce the need for manual intervention. This can significantly reduce operational costs and improve the speed and accuracy of financial processes. For example, some of any company’s most data-intensive procedures are found in accounting, tax, and finance.

Every aspect of the organisation needs statistics for revenue, spending, forecasting, and reporting. It may be quite challenging to get the data, verify the figures, and then construct the reports that will convey the information. However, a lot of these jobs and procedures may be automated with AI, which can save you time and money.

Challenges in the Financial Sector and How AI Can Help 

Despite the benefits of finance AI, the finance industry faces several challenges that can hinder the adoption and implementation of AI solutions. These challenges include data privacy and security concerns, lack of skilled professionals, and regulatory compliance.

Let’s explore these challenges deeper and understand how AI helps overcome these challenges with AI-driven accuracy, automation, efficiency, and security.

AI-enabled ERP Systems 

One area where this evolution is particularly evident is in the office of finance operations. Finance teams are under pressure to do more with less, and they need tools that can help them to streamline processes, reduce costs, and provide better visibility into financial performance.

Modern ERP systems are stepping up to meet this need. They come with new features and functionalities that are designed specifically for finance operations. For example, modern ERP systems can automate routine tasks such as data entry and reconciliation, freeing up finance professionals to focus on higher-value activities.

By incorporating artificial intelligence (AI) and machine learning (ML) capabilities, ERP systems are better equipped to perform more traditional data-intensive tasks like predicting cash flow, identifying risks, and decision-making. More specifically, AI-powered ERP systems can automate financial reporting, identify fraud risks, and provide real-time insights into financial performance. For instance, a finance team would be able to get access to all relevant ERP systems within a single dashboard, simplifying the alignment between FP&A and business teams up to three times faster than the spreadsheet approach.

AI Data Analytics in Finance: Reconciling Data

The rise of big data in recent years has led to the increasing importance of big data analytics in finance. However, accessing and reconciling large amounts of data from disparate sources into usable financial models can be complicated and time-consuming. Manual processes, such as using spreadsheets and VBA scripts, often lead to errors and slow downs, making reporting processes inefficient and repeatable.

AI platforms enable data analytics that can help streamline financial reporting processes. By quickly connecting to data from multiple sources, automating data analysis and preparation, and providing transparency, reusability, and version control, AI platforms empower finance teams to deliver valuable business insights faster and more accurately.

Teams can easily collaborate on projects and incorporate machine learning elements to enhance their analysis with AI democratization for automated forecasting.

Retail banks and corporate/investment banks both face various challenges in their day-to-day operations. To address some of these challenges, banks are turning to AI technology to help them make more informed decisions and streamline their processes.

Applications of AI in Finance:

1. Sales Tax Reporting: The complexity of tax codes and varying rates across different jurisdictions can make sales tax reporting a daunting task for businesses. Errors in reporting can result in financial penalties, legal disputes, and damage to reputation. AI can help automate sales tax reporting by leveraging machine learning algorithms to track sales and tax rates across different regions, reducing the risk of errors. AI can also help identify patterns in sales data to uncover potential areas of risk and opportunities for optimization.
2. Income Tax Provision Compliance: Income tax provision compliance requires businesses to accurately calculate and report their tax liabilities and ensure compliance with tax laws and regulations. This task can be challenging due to the complexity of tax codes and frequent changes to tax laws. AI can help streamline the income tax provision process by automating data collection, analysis, and reporting. It can also help identify potential tax savings opportunities and optimize tax planning strategies.
3. Fixed Asset Depreciation: Fixed asset depreciation is a crucial accounting process that determines the value of an asset over its useful life. Calculating depreciation can be time-consuming and prone to errors, particularly when dealing with a large number of assets. AI can help automate fixed asset depreciation calculations by leveraging machine learning algorithms to analyze asset data and calculate depreciation schedules accurately. It can also help identify opportunities to optimize asset utilization and improve depreciation strategies.
4. R&D Calculations: Research and development (R&D) expenses are often a significant cost for businesses in innovative industries. Calculating R&D expenses can be complex and require the tracking of numerous variables. AI can help automate R&D expense calculations by analyzing data and identifying R&D expenses eligible for tax credits and deductions. It can also help optimize R&D investment by identifying promising areas of research and development.
5. Sales Apportionment: Sales apportionment is the process of allocating a company’s income across different jurisdictions based on its sales activity. This process can be complicated due to varying tax laws across different regions and the need to track sales data accurately. AI can help automate sales apportionment calculations by analyzing sales data and identifying the appropriate allocation of income across different jurisdictions. It can also help identify areas of risk and opportunities for tax optimization.
6. Demand forecasting: Forecasting demand is a crucial task for businesses to optimize inventory levels, production schedules, and supply chain management. It can be challenging due to numerous variables that can impact consumer behavior and demand. AI can help forecast demand by leveraging machine learning algorithms to analyze historical sales data and identify patterns that can inform future sales predictions. It can also help businesses optimize production and inventory levels to match predicted demand.
7. Management reporting: Management reporting involves gathering, analyzing, and presenting data to inform business decision-making. This task can be time-consuming and require the tracking of numerous data points across different departments. AI can help automate management reporting by collecting and analyzing data from various sources and presenting it in a clear, actionable format. It can also help identify areas of risk and opportunities for optimization across different business functions.
8. Identify accounts of interest: Identifying accounts of interest involves analyzing financial data to identify accounts that require further investigation or monitoring. This task can be challenging due to the volume of financial data businesses generate and the need to identify potential areas of risk. AI can help identify accounts of interest by analyzing financial data and identifying anomalies or patterns that require further investigation. It can also help businesses optimize their auditing and monitoring strategies to improve financial transparency and reduce the risk of fraud.
9. Predictive models to assess creditworthiness: Assessing creditworthiness involves evaluating a borrower’s ability to repay a loan based on their credit history and financial information. This task can be time-consuming and require the analysis of a large volume of data. AI can help assess creditworthiness by leveraging machine learning algorithms to analyze credit data and identify patterns that indicate a borrower’s ability to repay a loan. It can also help businesses optimize their credit underwriting process by identifying potential areas of risk and improving the accuracy of credit risk models. AI can help lenders make informed lending decisions and reduce the risk of default.

Conclusion

Overall, AI can help businesses tackle a range of financial and accounting challenges by leveraging machine learning algorithms to analyze and automate complex tasks. By reducing the risk of errors, optimizing financial processes, and identifying areas of risk and opportunity, AI can help businesses improve financial transparency, reduce costs, and make more informed decisions.

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Payal Paranjape

Payal is a Product Marketing Specialist at Subex, who covers Artificial Intelligence and its application around Generative AI. In her current role, she focuses on Telecom challenges with AI and its potential solutions to these challenges. She is a postgraduate in management from Symbiosis Institute of Digital and Telecom Management, with analytics as her majors, and has prior engineering experience in the Telecom industry. She enjoys reading and authoring content at the intersection of analytics and technology.