As AI Transforms Financial Management, Data Bottlenecks Remain

Artificial intelligence (AI) is becoming more integrated into financial management, with a recent article from KPMG’s Global Financial Intelligence Research Report revealing that 71 percent of enterprises worldwide have integrated AI into their financial management practices to some degree. However, issues with data quality frequently present bottlenecks, preventing executives from taking full advantage of this new technology to generate the AI-driven financial intelligence needed to make informed decisions in an increasingly crowded marketplace.

That’s where fintech expert Xie Ning has proven especially forward-thinking. Drawing on her expertise in financial management from her work at internationally known accounting firms and large enterprises, Xie Ning has been planning for the shift from data-driven to intelligence-driven financial management since AI’s earliest integration into this sector. Recently, as part of her efforts to address the bottleneck created by inconsistent data quality, she has developed AI-powered software designed to improve real-time data processing, financial planning, and dynamic budgeting. She expects that in the future, financial management will become more focused on predictive analysis, real-time decision-making support, and dynamic risk management, and she is committed to helping develop the intelligent technologies that can make this happen.

Addressing the Bottleneck

Xie Ning realized that one major factor holding back the successful integration of AI into budget planning is that many enterprises lack standardized processes for collecting and organizing their financial data. Due to human error during data entry, some data may be skewed or missing, and existing data is often stored in multiple, inconsistent formats. This makes it difficult for AI models to access and analyze accurate information, which can then lead to inaccuracies in predictive analytics.

Suppose that an AI model is tasked with suggesting cost control strategies, but the cost data on which these strategies are based is skewed. In such cases, the report delivered to executives may be incomplete or even erroneous. Either outcome could easily result in ill-informed decisions and wasted resources.

Xie Ning’s Approach to Fintech

To address this, Xie Ning first led a research project titled Intelligent Transformation Pathways and Practical Research of Financial Analysis Processes in the Context of FinTech. Based on the findings, she developed new fintech software to tackle these bottlenecks. Her Automated Financial Budget Planning and Dynamic Adjustment Software (V1.0) uses natural language processing (NLP) technologies and machine learning algorithms to clean and integrate large and varied corporate data sets.

While collecting data, Xie Ning’s software uses an intelligent recognition module to match various data formats automatically, whether the data comes from Excel worksheets or more complex database files. The software also uses real-time validation to immediately check each data entry. When the program detects data that violates predefined format requirements or logical parameters, the system flags an error, prompting users to make corrections on the spot. This prevents “bad data” from entering the system.

Once the financial data is processed, Xie Ning’s AI-driven software performs dynamic budget adjustments, analyzing market trends and business operations to generate budget modification plans. Because this analysis and adjustment occur in real time, executives can transition from conventional static budgets to dynamic budgets that adapt to rapid market changes while allocating resources more efficiently and effectively.

Looking Ahead

Asked about her platform, Xie Ning is not content to rest on her laurels. “Every technological breakthrough is a new starting point, and there is a vast space for financial intelligence waiting for us to explore,” Xie Ning says. Currently, she is leading her team to investigate how AI can be applied more effectively to enterprise financial risk management and cost forecasting. She plans to continue developing more intelligent solutions to challenges in corporate financial management.