AI is evolving from data analysis (traditional AI) to new content creation and intelligent data modeling (Generative AI). ChatGPT, Gemini, Claude, Midjourney are popular examples.
AI in finance opens up the possibility of automated reporting, risk forecasting, and financial decision support. However, to operate effectively, businesses need a strong data and governance system (such as EPM) – this is often the “bottleneck” that many organizations encounter.
Let's find out with Bizzi in this article what Generative AI is!
What is Generative AI and how does it work?
Before understanding how Generative AI is changing the way businesses operate, we need to understand What is Generative AI and how does it work?.
This section will help you visualize “self-learning – self-creation” mechanism by Generative AI – the technology platform behind ChatGPT, Gemini or Midjourney that you hear every day.
What is Generative AI?
Generative AI is a type of artificial intelligence that has the ability to create new content – text, images, audio, or data – rather than just analyzing or predicting based on existing data. When you ask “what is generative AI”, it is the ability to “create new data” from a machine learning model.
Common models in generative AI include LLM (Large Language Models), GAN (Generative Adversarial Networks), Diffusion Models and architecture Transformer.
How Generative AI Works
- Learn from large amounts of input data via Deep Neural Networks, to understand semantic structures or image patterns.
- Given input (prompt, context), the model predicts the probability of the next part and generates new content from it.
- For example, GPT uses a model to predict the next word in a sentence, thereby generating complete, logical, and coherent text.
A simple way to visualize it: if data is the “seed”, generative AI is the “tree” that grows new knowledge from that seed.
The Difference Between Generative AI and Traditional AI
To choose the right application, businesses need to clearly understand difference between generative ai and traditional ai in many aspects.
| Criteria | Traditional AI | Generative AI |
| Main function | Historical analysis & prediction | Create new content or simulate data |
| Input data | Structured (tables, quantitative data) | Unstructured (text, images, audio) |
| Target | Data-driven decision support | Generate reports, simulate, and create content automatically |
| Background technology | Machine Learning, rule-based, regression, tree-based | LLM, GAN, Diffusion, Transformer |
| Main applications | Revenue forecasting, fraud detection, classification | Generate financial reports, create investment scenarios, and automatically generate content |
Economic example:
- Traditional AI in banking uses credit profile data to predict loan repayment risk.
- Generative AI can simulate hundreds of economic scenarios (e.g. interest rate fluctuations, exchange rates, supply and demand) to suggest investment strategies.
So when you look for “difference between generative ai and traditional ai”, note: Traditional AI helps understand the past & present; GenAI helps expand creativity, simulation and create new insights.
Practical applications of Generative AI in finance
Generative AI isn't just a new technology – it's already starting to be applied in many areas of corporate finance, from automating reporting to supporting strategic change.
Automate reporting and data aggregation
GenAI can automatic generation of accounting / FP&A reports from ERP/EPM input data, without the need for time-consuming staff compilation.
For example, GenAI can generate balance sheets, profit & loss (P&L) statements, or cash flow reports in real time from multiple source systems.
Financial forecasting & scenario simulation
Generative models help businesses perform cash flow forecasting, sensitivity analysis, and simulate business scenarios under various conditions (growth, crisis, market volatility).
As a result, CFOs can predict and react faster, using “dynamic” data rather than relying solely on history.
Fraud Detection & Smart Auditing
An interesting application: GenAI can generate simulated data to detect anomalies. If the real data deviates from the generated model, the system alerts the suspicious transaction.
In practice, this solution helps audit teams detect unusual transactions approximately 50% faster than manual methods.
Virtual Financial Assistant (AI Copilot)
GenAI can also play a role AI Copilot for FP&A / accounting:
- Support data entry, check invoices for errors, basic budgeting.
- Analyze KPI, make recommendations for plan revisions.
- Interact in natural language – employees can simply ask “how is July revenue compared to plan?” and get a report right away.
These applications demonstrate GenAI's ability to turn data into action in finance.
What are the challenges when businesses deploy Generative AI?
Despite its strong potential, AI Gen Deployment There are many obstacles in corporate finance. This section identifies four major challenges and approaches to their mitigation:
Data Governance
If the input data is not clean (missing, wrong, inconsistent), the GenAI model can generate incorrect results, called hallucination.
Therefore, businesses need a strict data management system, input data verification, and output verification before putting it into use.
Security and accounting standards
Financial data is extremely sensitive. When using Gen AI in finance, the risk of information leakage, hacker attacks or misuse increases.
Therefore, organizations must comply with security standards (GDPR, ISO 27001) and accounting standards (IFRS, GAAP) during the process of creating AI content.
Cost and ROI
Investment in GPU/TPU infrastructure, API copyright, and system integration is not small.
The ROI of GenAI depends on businesses having good data, standard processes, and the ability to apply insights. Otherwise, the costs can outweigh the benefits.
Lack of data skills & culture
If the finance/accounting team is not familiar with working with AI, giving good prompts, checking results, then the ability to exploit Gen AI in finance is very limited.
Need training, culture building data-driven decisions bottom up so AI can be effective.
Barriers businesses need to overcome to effectively apply GenAI
Generative AI is ushering in a new era of financial automation and analytics. But in reality, More than 651 TP3T Vietnamese enterprises have not been able to effectively deploy GenAI., the causes come from internal barriers: data, infrastructure, human resources, compliance and strategy.
| Problem group | Reason | Consequences | Solution |
| Data | Most of the enterprise data today is scattered in ERP, Excel, CRM or internal accounting systems. Data is not standardization, lack of governance and lack of unified flow. | GenAI cannot understand the context properly, leading to misleading content or reporting (phenomenon) AI hallucination). This is especially dangerous in finance, where just one wrong number can affect investment decisions or budget management. | Standardize financial data into the same structure.
Apply EPM to consolidate and control all data before entering GenAI.
Set up the process Data Governance and Data Validation. |
| Infrastructure | Vietnamese businesses still rely heavily on old on-premise ERP systems that lack open API connectivity.
At that time, integrating Generative AI or intelligent analytics platforms was almost impossible. |
Real-time data extraction is not possible.
Unable to connect to new AI or BI tools.
AI projects stop at “demo” level, cannot be expanded. |
Upgrade your ERP or add an EPM middleware layer with 2-way API connectivity.
Platform priority EPM Cloud – can be easily integrated with popular ERP systems (SAP, Oracle, Bravo…). |
| Human Resources | Traditional accounting and finance departments are often unfamiliar with working with AI models or unstructured data. | Don't know how to "train" GenAI with the right data.
It is easy to misunderstand or misuse the results AI generates.
Unable to exploit insights from predictive models. |
Skills training AI Literacy for financial staff.
Use a system with a familiar interface like EPM (Excel-based) makes it easy for the FP&A team to get started.
Build a “hybrid” FP&A team (finance + data) to bridge the gap between humans and AI. |
| Follow | Generative AI can access sensitive data such as profit and loss statements, cash flow forecasts, HR or supplier data. Without a control policy, businesses can violate privacy regulations (GDPR, ISO 27001). | Financial data leak.
Risk of being fined or losing brand reputation. |
Deploy internal AI (Private GenAI) combined with EPM with access control.
Set up user permissions by role (CFO, FP&A, Controller…).
Maintain regular audits of data systems. |
| Strategy | Many businesses invest in AI as a fad without a clear financial goal: reducing costs, speeding up reporting, or improving ROI. | AI projects are deployed sporadically and cannot measure effectiveness.
Cannot scale or sustain long term. |
Building an AI strategy that is aligned with Financial KPI specifically.
Connect GenAI to the FP&A (Financial Planning & Analysis) process.
Use EPM as a central platform to measure the performance of each AI model. |
EPM – The foundational piece that helps businesses exploit GenAI safely and effectively
Most businesses are experimenting Generative AI all face a major obstacle: fragmented and uncontrolled dataEven the most powerful AI models become useless if their “feed” is unstandardized or biased data.
For GenAI to truly be powerful, businesses need a data-secure platform standardization, hierarchy and tight control – and that is the role of EPM (Enterprise Performance Management).
Why does GenAI need EPM as a data platform?
GenAI cannot generate accurate content or forecasts if the input data is fragmented, biased, or without governance standards.
EPM solve this problem completely by:
- Standardize financial & operational data from various systems.
- Data stratification at strategic - operational - reporting levels.
- Setting up a governance model Keep data transparent, control access and changes.
As a result, EPM becomes trusted data platform that every AI application can “learn” and “understand” the business accurately.
EPM creates “Single Source of Truth” – the only data source for GenAI
One of the core benefits of EPM is the ability to Consolidate data from ERP, accounting, CRM, FP&A, and other operational systems.
Instead of having to extract and process discrete data from multiple sources, EPM helps businesses:
- Focus data on a single standardized data warehouse.
- Eliminate discrepancies, duplication, and manual errors.
- Increase accuracy when GenAI generates reports, forecasts, or strategic recommendations.
Thanks to that, GenAI can “learn” faster, respond more intelligently, and minimize the risk of generating misleading content (AI hallucination)..
EPM helps businesses deploy GenAI securely and in a controlled manner
Unlike spontaneous GenAI adoption, EPM helps businesses Build a data governance framework and controlled decision-making model.
Specifically:
- Track data flow before AI accesses and processes.
- Log recording & audit trail, ensure compliance with privacy policies and legal regulations.
- Role-based access to limit the risk of leaking sensitive financial data.
This is especially important with CFO and FP&A team, when they need to both leverage the power of AI and ensure compliance with financial and accounting regulations.
Generative AI doesn't replace humans – it accelerates business decision-making capabilities.
Hopefully, through this article you have clearly understood what Generative AI is. Generative AI does not come to replace people, but to expand analytical and decision-making capabilities of financial leaders. When applied properly, GenAI can automate repetitive tasks, Simulate hundreds of business scenarios and provide strategic recommendations based on real data – something that previously took CFOs and FP&A weeks to do.
However, for Generative AI to truly create value, businesses need a trusted data platform. And that is the role of EPM (Enterprise Performance Management). EPM helps standardize, consolidate, and control all financial data – from ERP, accounting to CRM – creating “single source of truth” for all analysis and decisions.
