AI Is Changing Financial Modelling: What to Learn Before Choosing an Investment Banking Course in Be

Author : Raman Pandit | Published On : 26 Oct 2026

Artificial intelligence is changing how financial professionals research companies, analyse financial statements and prepare reports. Tasks that once required hours of manual work can now be supported by AI-powered tools that organise information, summarise documents and assist with data analysis.

But there is an important distinction between producing a financial model and understanding what the model actually means.

An AI tool might help an analyst prepare a forecast, but the analyst still needs to assess whether the assumptions are realistic, whether the numbers are consistent and whether the conclusions make sense for the business. A small error in revenue growth, operating costs or interest expenses can significantly change a company's projected valuation.

This is why financial modelling remains an important area of investment banking education, even as new technologies become part of the profession.

For students and graduates exploring an investment banking course in Bengaluru, understanding the relationship between finance and AI can offer a practical starting point for preparing for an evolving industry.

How Is AI Changing Investment Banking in 2026?

Investment banking involves several activities, including company valuation, mergers and acquisitions, capital raising, financial research and transaction analysis. Many of these activities require professionals to review large amounts of financial and business information.

AI can assist with parts of this process. Depending on the tools and controls available, it can help summarise company filings, organise information from documents, identify patterns in financial data and support the preparation of initial research drafts.

For example, an analyst researching a company before a potential acquisition may need to examine its annual reports, financial statements, market position and industry developments. AI-assisted tools can help organise this information and identify areas that deserve closer examination.

However, the initial analysis is only one part of the work. The analyst must verify the information, compare it with reliable sources and determine which findings are relevant to the transaction.

The CFA Institute highlighted this changing skill requirement in its March 2026 article on what employers want from finance professionals. Its research indicates that employers increasingly value AI and data-related capabilities alongside financial modelling, analytical judgement and communication. <Link url="https://www.cfainstitute.org/insights/articles/employers-new-skills-blueprint" title="Read the CFA Institute's analysis"/>.

The takeaway is not that traditional financial knowledge is becoming less important. Rather, professionals need to understand finance well enough to use new tools effectively and recognise when their outputs need to be challenged.

Financial Modelling Still Starts With Business Fundamentals

Financial modelling involves creating a structured representation of a company's financial performance. Analysts use historical data and assumptions about future business activity to estimate revenue, expenses, profitability, cash flow and financial position.

These projections can support decisions about company valuation, investment opportunities, financing requirements and business strategy.

Consider a fictional Bengaluru-based technology company planning to expand into two new markets. Its management expects revenue to increase as the business acquires more customers, but expansion will also require spending on employees, technology, marketing and infrastructure.

An analyst building a financial model needs to estimate how these changes may affect the company's future performance.

The model might consider customer growth, average revenue per customer, employee costs, operating margins and capital expenditure. Different assumptions can produce very different financial outcomes.

AI may help organise data or generate an initial outline for the analysis. However, the analyst must decide whether the assumptions reflect the company's business model and market conditions.

If customer growth is overestimated or operating costs are underestimated, the model could present an overly optimistic picture of the business.

This is why financial modelling education should focus on understanding the logic behind the numbers, not just learning spreadsheet formulas.

Can AI Build a Financial Model for You?

AI tools can assist with financial modelling, but the quality of their output depends on the information supplied, the complexity of the task and the reliability of the underlying calculations.

They may help explain spreadsheet formulas, suggest ways to structure a model, generate draft assumptions or identify inconsistencies that require further investigation. Some tools can also support the analysis of large datasets.

However, a financial model used for investment decisions requires careful validation.

Imagine an analyst asking an AI tool to prepare a discounted cash flow valuation for a company. The tool may generate a model structure and suggest inputs for revenue growth, operating margins and the discount rate.

The analyst must still verify the company's historical financial information, assess the assumptions and check that the calculations are correct.

The discount rate matters because it reflects the risk associated with future cash flows. Terminal value assumptions also have a substantial influence on the final valuation. If either is inappropriate, the model may produce a misleading estimate of the company's value.

AI-generated calculations can also contain errors, use outdated information or make assumptions that are unsuitable for the business being analysed.

For this reason, learning how to build and review a model manually remains useful. It gives analysts a foundation for checking automated outputs rather than accepting them without question.

Three Financial Modelling Areas Every Aspiring Analyst Should Understand

Students considering an investment banking course in Bengaluru should pay attention to the core modelling methods used to analyse businesses and investment opportunities.

1. Three-Statement Financial Modelling

A three-statement model connects the income statement, balance sheet and cash flow statement.

The income statement shows revenue, expenses and profit. The balance sheet presents the company's assets, liabilities and shareholders' equity. The cash flow statement explains how cash moves through operating, investing and financing activities.

These statements are interconnected. For example, an increase in sales may affect revenue and profit, but the impact on cash also depends on when customers pay and when the company settles its expenses.

A three-statement model helps analysts understand these relationships and assess how changes in business performance affect the company's overall financial position.

2. Discounted Cash Flow Valuation

Discounted cash flow, commonly known as DCF, is a valuation method that estimates a company's value based on its expected future cash flows.

Because future cash flows are uncertain, they are discounted to reflect the time value of money and the risk associated with the business.

The result depends on several assumptions, including projected cash flows, the discount rate and terminal value.

AI can assist with research and preliminary calculations, but analysts need to understand the financial logic behind these inputs. A valuation is only as useful as the assumptions supporting it.

3. Comparable Company Analysis

Comparable company analysis estimates a business's value by examining how similar companies are valued by the market.

Analysts may compare businesses using measures such as enterprise value to EBITDA or price-to-earnings ratios. The appropriate measures depend on the industry, the companies being compared and the purpose of the analysis.

Selecting suitable comparable companies requires judgement. Two businesses operating in the same industry may have different growth rates, margins, debt levels and risk profiles.

AI may help organise company information, but analysts must determine whether the comparisons are meaningful.

Learning these methods helps students understand how financial analysis supports valuation and transaction decisions.

Why Bengaluru Is Relevant to Aspiring Investment Banking Professionals

Bengaluru is widely associated with technology, startups, multinational companies and a growing professional services ecosystem. The city also has roles connected to corporate banking, financial analysis, investment banking support, research and transaction-related activities.

For example, Wells Fargo lists investment banking and corporate banking roles in Bengaluru, illustrating the presence of financial services work in the city. <Link url="https://wf.wd1.myworkdayjobs.com/en-US/WellsFargoJobs/job/Investment-Banking-Analyst_R-570563-1" title="View an Investment Banking Analyst role in Bengaluru"/>.

The local technology environment also makes the relationship between data and finance particularly relevant. Financial institutions increasingly use technology to support research, reporting, risk analysis and other business processes.

However, living in a major business hub does not automatically lead to an investment banking job. Candidates still need to demonstrate the financial knowledge, analytical ability and communication skills required for the positions they pursue.

For learners exploring an investment banking course in Bengaluru, it is worth looking for programmes that combine finance fundamentals with practical exercises rather than focusing only on theoretical explanations.

What Should You Look for in an Investment Banking Course in Bengaluru?

The right course should help you understand how financial concepts are applied to business decisions. Before enrolling, examine the curriculum, practical training and career support available.

A strong foundation in finance

Look for coverage of accounting, financial statement analysis, corporate finance and capital markets. These subjects help learners understand the information used in investment banking work.

Practical financial modelling

Check whether the course includes spreadsheet-based exercises, forecasts, valuation models and business case studies. Ask how much time learners spend building models themselves rather than only watching demonstrations.

Valuation and transaction concepts

Understanding company valuation, mergers and acquisitions and financing decisions can help students connect financial modelling with common investment banking activities.

Technology awareness

AI and data tools can support research and analysis. A useful learning approach should explain where these tools can help and why their outputs need verification. Strong financial fundamentals should remain central to the training.

Interview and career preparation

Review whether the programme includes CV guidance, mock interviews, mentoring or placement assistance. Ask for clear information about the scope of these services and the requirements learners must meet.

Students should also consider their existing knowledge, career objectives, learning schedule and budget before making a decision.

Boston Institute of Analytics offers an Investment Banking and Financial Analytics programme covering areas related to financial analysis and valuation. Prospective learners can explore the Investment Banking and Financial Analytics course to review the curriculum and determine whether it aligns with their goals.

How to Practise Financial Analysis Before Applying for Jobs

Understanding concepts is important, but practising them on realistic examples helps build confidence.

Start by selecting a publicly listed company and downloading its annual report. Review its revenue, operating expenses, profitability, debt and cash flow over several years.

Next, create a simple forecast using clearly stated assumptions. Estimate how revenue might change, identify the expenses that could increase and examine the possible impact on profit and cash flow.

You can then build a basic valuation model and test how the outcome changes when your assumptions change.

For instance, compare a scenario in which revenue grows steadily with another in which growth slows and operating costs increase. Consider what these differences mean for the company's projected cash flows and valuation.

Once you have built the model, use an AI tool to review the structure or explain specific calculations. Then independently check its suggestions against the financial statements and your own calculations.

This process helps develop two useful habits: using technology to improve efficiency and maintaining responsibility for the accuracy of financial analysis.

It also creates an opportunity to explain your work clearly. In an interview, being able to describe why you selected an assumption and how it affects the valuation may be more informative than simply presenting the final number.

Will AI Reduce Opportunities for Investment Banking Analysts?

AI is changing the nature of some tasks performed by junior finance professionals, particularly research support, document processing and routine analysis. However, it would be premature to conclude that all investment banking analyst roles will disappear.

Investment banking involves more than preparing spreadsheets. Professionals need to interpret business conditions, evaluate risks, communicate with colleagues and clients, support transaction execution and work within regulatory requirements.

The balance between automated and human-led work will vary across institutions and roles.

The more practical approach for aspiring analysts is to learn how to use technology responsibly while developing the financial understanding needed to evaluate its output.

This includes checking data sources, identifying unrealistic assumptions, understanding valuation methods and explaining the commercial implications of a financial conclusion.

Candidates who combine these capabilities may be better prepared for roles that increasingly bring together finance, data and technology. Nevertheless, employment outcomes depend on several factors, including qualifications, experience, recruitment requirements and individual performance.

Conclusion

Artificial intelligence is changing the way financial professionals research companies, analyse information and prepare financial models. But technology cannot remove the need to understand how businesses generate revenue, manage costs, fund operations and create value.

Financial modelling, valuation and financial statement analysis remain important foundations for investment banking. AI can support these activities, but analysts must still verify the data, challenge assumptions and interpret the results.

For students researching an investment banking course in Bengaluru, the goal should be to find a programme that builds these fundamentals and provides opportunities to apply them through practical exercises.

Learning to combine sound financial reasoning with responsible use of technology can provide a useful foundation for exploring investment banking and related finance careers.