Solution · Data Science
Models that change the decision.
Models that survive an auditor, a regulator, and a macroeconomic cycle. We use the simplest method that wins — and reach for advanced AI only where it demonstrably pays back.
Models that survive an auditor, a regulator, and a macroeconomic cycle. We use the simplest method that wins — and reach for advanced AI only where it demonstrably pays back.
Credit, pricing, stock, retention — the calls that move your P&L are made daily, and mostly from experience and habit. The data is there; it just doesn't reach the moment of decision.
And where models do exist, too many stay in notebooks — impressive in the demo, absent from the workflow, indefensible in front of an auditor.
We build models that live inside the decision: scored in seconds, explainable to the applicant and the regulator alike, measured against the money they move.
Each engine starts from a pattern we've already run in production — credit, fraud, churn, pricing, stock — trained on your data, using the simplest method that wins.
Lower risk losses. More accurate forecasts. Targeted spend on customers, SKUs and channels that actually return. Models you can defend on paper.
Mid maturity. Useful as soon as production data is reliable and the team can support a model life-cycle. Audit-grade work needs a platform where every number can be traced and reproduced.
Where this lands in practice. Each row opens with the detail — and what it buys you.
Approve, decline, limit and terms — decided in seconds, not committees. Scorecards, rules and affordability checks in one engine, explainable to the applicant and the regulator alike.
The payoff. Approval speed of a fintech, loss rates the risk committee signs off.
One price for everyone means your best customers subsidize your worst. Price each loan by its actual risk — margin protected on the risky end, growth unlocked on the safe one.
The payoff. Your best customers stop subsidizing your worst.
Non-performing loans are a portfolio, not a pile. We score recovery likelihood per exposure, match each segment to the right strategy — restructure, collect, sell — and price the portfolio for either outcome.
The payoff. The workout desk works the cases where working matters.
Scoring every transaction in the moment it happens — catching the fraud without strangling the checkout. Tuned to your loss data, monitored for drift from day one.
The payoff. Losses fall faster than false alarms rise.
Who's about to leave, why, and what offer changes their mind — lifecycle-aware models feeding next-best-action, not a slide deck. Retention spend goes where it actually retains.
The payoff. Save offers go to the customers who would actually have left.
The right upsell or cross-sell at the moment the customer is listening — in the app, at the till, in the call. Propensity models served in real time, measured against revenue, not clicks.
The payoff. The right offer while the customer is still listening.
Some customers take ten others with them when they go. Network analysis over calls, contracts and households finds who actually sways whom — so retention protects the customers who matter beyond their own bill.
The payoff. Retaining one customer keeps ten.
Ad inventory priced by demand, audience and moment instead of a static rate card — yield managed continuously across channels, with the floor prices your sales team can defend.
The payoff. Revenue per slot rises without a single extra impression.
Forecast-driven stock levels and allocation per SKU and location — less capital sitting on shelves, fewer empty ones. Reorder points that move with the season, not with last year's spreadsheet.
The payoff. Less capital on the shelves, fewer empty ones.
Through the Moberg Delivery Framework — the same five stages, governance and engineering standards we run on every engagement, from business case to long-term run. Microsoft has independently validated this practice — but judge us on a model your auditor signs off and your board defends.