Quantitative Lab with AI
AI applied to financial analysis in Panama
Artificial intelligence is useful in a finance department when it answers a question someone was already asking: how much cash will there be in week nine, which customers are about to pay late, what happens to the covenant if the rate moves.
The Quantitative Lab is where DB Consultants Group brings AI into the financial processes of its clients — with the judgement of a consulting firm, not the pitch of a technology vendor.
The starting point
Why most AI projects in finance stall
They start from the tool rather than from the decision. A model is installed, it produces an output nobody owns, and three months later the team is back on the spreadsheet it trusted.
The Lab works the other way round. It starts from a decision that is taken every week — paying, collecting, pricing, borrowing — and asks what would have to be known, and how much earlier, for that decision to be better. Only then does the question of technique arise.
That is also why the engagement begins with a small pilot on real data rather than with a platform.
The flagship service
AI-assisted cash flow management
The thirteen-week forecast is the natural place to start, because it is the tool where anticipation is worth the most and where the data already exists inside the company.
What it includes
A thirteen-week cash forecast kept continuously up to date, collection patterns learned customer by customer, scenarios and sensitivity, early warnings of liquidity pressure, and a weekly management dashboard.
What is delivered
A model calibrated on the company’s own data, the dashboard with its manual, training for the team that will run it, and monthly calibration support.
Who it is for
Companies that sell on credit, carry seasonality or service demanding debt. It does not require changing accounting or ERP systems.
What changes
Tension is seen weeks earlier, when there are still options: negotiating a term, moving a payment, drawing a line. Seen on the day, there are no options left.
The rest of the Lab
Six more ways it is used
AI readiness diagnosis
What data the company actually has, in what condition, and which processes would benefit first. It usually ends with a shorter list than expected, and a better one.
Financial models with simulation
Projections that produce a range rather than a single figure, with Monte Carlo simulation over the variables that genuinely move the result.
Automated financial reporting
The monthly pack that takes three days of manual assembly, rebuilt so it is produced in minutes and always from the same source.
Credit portfolio analysis with AI
Behaviour patterns, migration between categories and early warning of deterioration, alongside the portfolio due diligence work.
Demand and sales forecasting
Forecasts by product, channel and season, which feed straight into inventory policy and into the cash forecast.
Executive training in AI
Sessions for management and boards: what these tools do, what they do not do, and how to tell a real result from a demonstration.
The method
How an engagement runs
- DiagnosisTwo to three weeks. What decisions are taken, what data exists, in what condition, and where the anticipation would be worth most.
- Contained pilotOne process, real data, a defined period. The pilot has to prove something measurable before anything is scaled.
- ImplementationThe model, the dashboard and the routine are handed to the team that will use them, with the manual and the training.
- Follow-upMonthly calibration and indicators: weeks of anticipation gained, hours removed, accuracy against the previous method.
Principles
Three commitments that shape the work
Your data is yours
Client information is confidential and is not used to train public models. What is built stays with the company.
The decision remains human
AI prepares the analysis; the partners’ judgement signs it. A model that nobody can explain to a board is not a deliverable.
Measurable results
Every engagement defines beforehand how it will be judged: weeks of anticipation, hours eliminated, accuracy against the method it replaces.
Common questions
Frequently asked questions
Do we have to change our accounting or ERP system?
No. The Lab works with the data the company already produces, whatever system it comes from. Changing systems is a separate decision and it is not a prerequisite.
How large does a company have to be?
Size matters less than the quality of the data and the frequency of the decision. A mid-sized company that sells on credit and updates its cash position every week has more to gain than a larger one that decides once a year.
What is used to train the models?
The company’s own historical data, kept confidential. Client information is not used to train public models, and what is built is delivered to the company.
How does a pilot start?
With a diagnosis of two to three weeks, followed by one contained process on real data over a defined period. The pilot has to show a measurable result before anything is extended.
Who runs it afterwards?
The company’s own team. The handover includes the dashboard, the manual and the training; monthly calibration support is there so the model stays accurate, not so the company depends on the firm.
Can the work be delivered in English?
Yes. Models, dashboards, manuals and training sessions can be delivered in English or in Spanish, which matters when the local team works in Spanish and the shareholder or regional office reads in English.
Is there something we can look at before deciding?
Yes. The Economic Observatory published on this site (in Spanish) is a public sample of the Lab’s work: indicators built from primary sources and updated on a routine.
Is the engagement confidential?
Yes. The nature of every engagement is confidential and is not disclosed without the client’s written authorisation.
Let’s talk about where to start
A first conversation is enough to identify which weekly decision would gain the most from anticipation, and what data it would take. It is confidential and commits you to nothing.