A score you cannot explain is a liability, not an asset.
Scoring Engines are parametric financial and behavioural model families. They are deliberately separate from each other and from the credit workflow, so each view can be tuned, governed and audited for the portfolio it actually serves.
One number cannot carry two different questions.
Whether a business can afford an obligation and whether it has been behaving like a business that will meet one are different questions with different data behind them. Collapsing them into a single opaque score makes the model harder to improve and impossible to defend in a review.
Two families, and the inputs and controls they share.
Capacity and behaviour are different questions with different data behind them. Collapsing them into one score is what makes a model impossible to defend, so the figure keeps them apart.
Measure capacity
Statements, ratios, cash cycle and sector parameters combine into an analysis, score and internal rating appropriate to the product and segment.
Cut-offs, weights, overrides and segment rules are set and versioned by the risk team, not shipped in a release.
The signals and weights behind a result stay attached to it, for investigation, internal audit and external review.
Keep the view current
Payment patterns, utilisation and operating signals update the picture continuously, so a rating reflects the last quarter rather than the last filing.
Models are refit against what the group actually saw in its own book, in the segments it actually lends to.
From raw input to a decision that can be replayed.
Financial statements, bureau data, transactional feeds and internal history resolve onto one counterparty.
Features are computed and versioned, so a model always knows which definition it was trained on.
Financial and behavioural families run separately and produce distinct, labelled outputs.
Credit policy turns the outputs into a limit, a price and a decision inside Credit Lifecycle.
Realised performance flows back and becomes the training set for the next calibration.
Underneath the decision, not inside it.
Scoring produces the view. Credit Lifecycle decides what to do with it, and Early Warning watches what happens next.
Usable without the rest of the platform.
Scores into your own workflow
The engines can serve an existing origination system through defined interfaces, without adopting Credit Lifecycle.
Your feeds, your residency
Model inputs, storage location and retention follow the institution's own data model and controls.
Built for a model committee
Versions, training sets, performance and drift are recorded in a form a model risk function can actually review.
The ones that come up first.
Are these machine-learning models or rule-based?
Parametric: the structure and the parameters are explicit and set by the risk team, and every output exposes the signals and weights behind it. That is a deliberate constraint, because a result that cannot be reconstructed cannot be defended in a review.
Can we bring our own models?
Yes. The engines can run alongside an institution's existing models, and policy decides how the outputs are combined.
What data is required to start?
Financial statements and repayment history are enough for a first calibration. Transactional and behavioural feeds improve the behavioural family and are usually added second.
How often are models recalibrated?
On observed performance rather than a fixed calendar. The trigger, the training window and the approval are recorded with the version.
Bring a portfolio and the outcomes it produced.
The useful demo is a calibration on your own book.