Imagine this: your company’s customers are targeted by a new fraud scheme. You have no way to detect it until complaints pour in. You urgently need to identify who is most vulnerable to protect them—now, not weeks from now.
A natural question follows: could you use your rich historical data to predict susceptibility? If yes, that means patterns within your data hold clues about fraud risk [1]. Understanding those patterns could guide your prevention strategies.
We refer to this discovery process as profiling: extracting the most specific, minimal set of patterns that characterize a cohort—whether that cohort is customers, products, stores, or any other business entity. Profiling is foundational to risk modeling, market intelligence, and fault analysis. Yet it is rarely treated as a discipline of its own. As a result, data science processes move far slower than business realities demand.
Where things stand
Today, profiling is usually a side effect of feature engineering and model building. When a new question arises, data scientists hand-craft domain-specific features and run models to see which ones matter. It works, eventually. But meanwhile, the business landscape shifts, and opportunities fade.
Take the fraud example: by the time a skilled analyst stitches together transaction logs, demographic variables and behavioral signals, the fraud impact may already be severe. Businesses cannot afford that delay, and often resort to blunt, imprecise countermeasures.
This isn’t an isolated problem. Across industries, data science remains largely unscalable: each new question still starts from scratch.
Fast-tracking profiling
Businesses constantly ask: “For this cohort, what patterns distinguish them from the rest?”. Whether the cohort is churned customers, high-margin products, or suppliers prone to delays, today’s data science workflows still take weeks of manual feature engineering.
Tomorrow, this can happen nearly instantaneously.
Large AI models now encode rich representations of text, images, logs and tables. By streaming raw multi-modal entity data through these models, systems can capture high-dimensional feature sets that implicitly contain behavior, context and semantics [2].
Then, modern machine learning techniques [3] can, on demand, distill this massive feature space into a minimal profiling feature set for the cohort of interest. With that, systems can:
Surface a cohort’s unique “fingerprints”, making its distinctive traits crystal clear.
Scan your entire database to flag other entities sharing those fingerprints.
Explain these signals in plain language, empowering non-technical users.
Scale effortlessly: the same workflow supports any new profiling question without restarting from zero.
Empower analysts via a conversational UI—refining and iterating their questions in minutes instead of weeks.
The result is a continuous, real-time loop of discovery instead of episodic, labour-intensive projects. Data scientists can shift their focus from ad-hoc feature engineering to system stewardship—ensuring data quality, robustness and ethical guardrails—while the business gains a data science engine that moves at the speed of strategic inquiry.
[1] These patterns are not necessarily causal; they may simply correlate with the fraud risk through shared hidden causes.
[2] These aren’t just opaque last-layer embeddings, but whole-model representations enabling interpretable features. See Towards Monosemanticity: Decomposing Language Models with Dictionary Learning (Anthropic).
[3] For example, approaches inspired by one-class classification, PU learning, and related techniques.
Through focused partnerships and consulting, modell.ai can help engineer efficient profiling capabilities and unlock rapid, data-driven decisions.
We are particularly interested in exploring this opportunity with large transactional datasets. Let's talk!