This article summarises our February 2026 webinar AI and project finance models: industry update, presented by Rickard Wärnelid (opens in a new tab) (Partner, Head of APAC Financial Advisory and Energy), Joshua Grimm (opens in a new tab) (Director, APAC Energy, Singapore) and Rael Levinsohn (opens in a new tab) (Associate Director, APAC Energy).
Artificial intelligence is no longer a fringe topic in project finance. In recent months the market has seen AI tools for Excel and financial modelling develop rapidly, and the conversation has moved from “should we explore this?” to “how do we adopt it responsibly?”
When we announced a webinar on AI and project finance models in February 2026, registrations far exceeded the usual audience for a financial modelling webinar. As Rael Levinsohn noted, there has scarcely been a course in the past 18 months where someone hasn’t asked about AI: what is going on, what do we think about it and are we going to lose our jobs?
The headlines tell part of the story. Financial news outlets ask whether AI will kill Excel and entry‑level investment banking roles. AI firms are paying investment bankers to help train their models. Major consulting firms have faced scrutiny for using AI in government reports without adequate disclosure. LinkedIn feeds are full of claims of full financial models built in minutes. The pace of change is real, and so is the noise. For the project finance community, separating the signal from the noise matters.
Why AI adoption in project finance modelling is different
Much of the current AI discussion centres on corporate finance, M&A and FP&A workflows. Project finance modelling works under a distinct set of constraints. Models must be auditable, transparent and traceable. Lenders, sponsors and government stakeholders all need to trust the numbers, and that trust has always been built on being able to follow every calculation from input to output. Black‑box solutions, however impressive, do not meet this standard. For an example of this transparency in practice, see our tutorial on cash flow available for debt service (CFADS).
“The outputs are somewhat irrelevant if you don’t have a spreadsheet with logic that explains every step. That’s completely different to almost every other field. If you’re building a house and someone shows you the architect’s blueprints, no one says show me the working papers. But in project finance, if you don’t have the model, you say: how do I trust that this is right?”
This is why the project finance sector’s response to AI differs from what we see in consulting, legal or even statutory audit. As Joshua Grimm observed, the demands and requirements have always been different for project finance, and the same is proving true for AI adoption.
AI governance, data leakage and the reality of adoption
The webinar mapped out the concerns the presenters hear from clients, lenders and advisers. At the governance level, organisations are working through responsible use, the risk of data leakage and the difference between enterprise platforms such as Microsoft Copilot and the fast‑moving field of AI startups. One major consulting firm’s experience, where AI‑generated errors in a government report drew significant scrutiny and were followed by internal guidance discouraging staff from emailing files to personal AI tools, showed how seriously stakeholders take transparency about AI use.
On the modelling side, the concerns are more specific: AI hallucinations and verification risk, the auditability of AI‑generated outputs, undocumented assumptions or logic and review liability. If AI contributes to an error, who is accountable? Then there is the wider worry about controlling templates and modelling methods when anyone with an AI tool could generate model structures outside established standards.
“For most organisations, the governance and the responsible use of AI is that there is no AI. The tools are banned. You can’t get access to them. That is the vast majority of people’s experience. And even those who do have access have zero guidance, zero encouragement and no framework for how to use it.”
One of the most common anxieties is not about AI itself but about being left behind. Many organisations feel that everyone else is far ahead. In reality, adoption across project finance is very uneven. When course participants are asked who has access to AI tools at work, the answer is almost always “zero access”. That gap between perception and practice matters: there is still time for organisations to develop careful, governance‑first approaches.
AI platforms for financial modelling: ChatGPT, Copilot and Claude
The webinar named the platforms and categories that generate the most discussion: OpenAI’s ChatGPT, Google Gemini, Microsoft Copilot, Anthropic’s Claude, the Shortcut platform and a constantly shifting roster of AI startups. But there is a clear gap between what people talk about and what they actually use at work.
ChatGPT dominates private use: it is the platform people know and feel comfortable prompting. In professional project finance, if people have access to anything at all, it is Copilot. The webinar stressed starting with the tools you actually have, rather than waiting for a theoretically better platform your organisation may never approve.
“A very common thing we hear is people saying, I’m waiting for Copilot, but what’s the point? Claude is so much better. But they haven’t actually tested Copilot yet. We’re seeing a lot of people extremely informed about what they think, and having done very little.”
Excel Agent Mode was highlighted as a significant development and the entry point for understanding AI‑assisted modelling. In a recent internal workshop, Agent Mode built dashboards with charts in moments: half the output was completely useless, the other half was excellent. For spreadsheet‑level tasks the results can be remarkable, but for structured project finance models the tools lean towards the LET function and named ranges everywhere, which work but are not readable by people. It lets people with low skill do impressive things, which makes it extremely powerful and extremely dangerous. For readable Excel techniques, our tutorial on using LOOKUP to replace VLOOKUP and HLOOKUP shows the clarity project finance models need.
Claude was singled out as the most talked‑about tool in project finance modelling, with many arguing it is the most powerful available. But almost no one in project finance has access to it at work. It remains a weekend activity for enthusiasts. Startups show a similar pattern: new tools launch weekly, and before governance teams approve one, another has taken over.
Where AI adds value in the project finance modelling lifecycle
A central theme was moving away from the all‑or‑nothing expectation of AI.
“This notion that AI can do absolutely everything, or AI can do absolutely nothing, is false. The key thing is: what iterative piece in the process could you make slightly better? It has to be a step‑by‑step process.”
Model documentation is one of the clearest wins. AI is very good at reading complex formulas and explaining them in plain language. Give it a seven‑line formula that very few people understand and it can explain what the formula does and convert it into a multi‑step calculation, bringing transparency where there was none. Our tutorial on debt sculpting to target DSCR without VBA is a good example of breaking complex logic into auditable steps.
Extracting data from contracts into structured input sheets is another practical use. The team recommends a stepwise approach: extract into a table first, confirm it has worked, then restructure, much as you would instruct a well‑trained colleague one step at a time rather than expecting one prompt to produce a finished model. When updated contracts arrive, often running to 200 pages, AI can quickly find what has changed. Bringing those updates into an existing model is harder. As Joshua Grimm noted, AI gets spreadsheets, but the structure of a project finance model is quite complex. Tutorials such as features of a cash flow waterfall in project finance and the loan life coverage ratio (LLCR) show the layered logic these models need.
Credit and investment paper preparation is an area where AI does well once the task moves from numbers to narrative. As soon as numbers are being turned into text, it performs exceptionally well. The metrics AI needs to interpret, such as average DSCR and cash sweep mechanics, are covered in our tutorials. Model review is similarly promising. Automating integrity checks and flagging errors suit AI’s pattern recognition, though a full model audit, with its strategic and commercial questions, remains beyond current tools.
Full model development, generating a complete, bankable model from scope documents, remains at the frontier. The tools produce formulas that work but are not readable by people, which undermines the trust project finance models exist to establish. As Joshua Grimm put it: will AI eventually build a full model? Absolutely. But it will be much more of an analyst than a full team.
How AI adoption in project finance compares with consulting, legal and audit
The webinar set project finance against other professional services. In consulting, AI already generates draft decks and storylines. In legal, dedicated platforms for clause detection and precedent retrieval mean firms are hiring fewer graduates and paralegals. In statutory audit, AI is used for volume review of invoices, contract tagging and benchmarking, largely within internal platforms to protect data. In financial due diligence, tools for general ledger anomaly detection and tie‑out automation are maturing fast.
The common thread is that AI is not doing everything. There is still a person analysing, reviewing and deciding in every case. Where processing content and language is the core task, as in consulting reports and legal documents, adoption has been fastest. Where numerical precision, auditability and stakeholder trust matter most, as in project finance modelling, uptake is slower but just as certain.
The future of AI in project finance: learning, agents and trust
The webinar outlined several developments likely to shape the sector. Organisational learning, capturing modelling methods, cross‑project experience and institutional knowledge in a form AI can use, is one of the most promising near‑term opportunities. Imagine a project finance bank uploading five years of credit papers with the models and source documents behind them. That would give AI a strong basis to learn what good looks like in that institution. The challenge is doing this at scale without compromising confidentiality across projects.
Tailored agents built for model development, credit paper drafting or sector benchmarking are a logical next step beyond general‑purpose AI tools. Building what the webinar called “project finance trust” needs full lineage, audit logs, change tracking and sign‑offs. The parallel with Python is instructive. Nearly a decade ago the question was whether Python‑coded models would replace Excel. They did not: Python was more efficient for some calculations but lacked the transparency stakeholders needed. AI faces the same test.
“In project finance, everyone is looking at that model to check and understand exactly what’s going on. It’s why we continue to use Excel. It’s a similar thing when it comes to the adoption of AI: yes, we could use it in certain elements, but we still need to keep that trust.”
The ultimate vision, from a Teams call to a model specification to an automated bankable model, remains aspirational. Some parts are already achievable, but as the presenters observed, the output is not yet very useful in a real project finance context. Technology alone will not close the gap between a model that works and a bankable one.
The organisations best placed will be those building the foundations now: governance frameworks, training for their teams and AI brought into their workflows in a controlled, step‑by‑step way. As Rael Levinsohn concluded, the prize is huge: projects going to market faster and deals done with less pressure and fewer late nights. Our jobs are here to stay, but they will be done quicker, better and smarter.
To explore these themes hands‑on, our course AI in Project Finance Modelling gives participants practical workflows, a prompt library and a governance playbook built for project finance teams.