Predictive Analytics Suite on MindsDB
Risk teams had the data but not the access. Every forecast required scheduling time with a data scientist, writing tickets, and waiting days for results. The bottleneck was not the model — it was the interface between the model and the people who needed it.
A risk analytics platform serving enterprise clients across financial services and insurance verticals.
Non-technical risk managers could not self-serve predictive insights — every query required a data scientist as an intermediary, creating delays and dependency.
Quick Summary
We deployed MindsDB as the central ML interface for a risk analytics platform, adding a text-to-SQL layer and a React dashboard so non-technical risk managers can run forecasts themselves. Forecast turnaround dropped from days to seconds across 10+ deployed models, and the data science team is freed to build better models.
Challenges Identified
Risk managers could not write Python or access Jupyter notebooks — all forecast requests had to go through the data team, creating a backlog measured in days.
Ad-hoc questions could not be answered in real time — business reviews were held waiting on model outputs that were not prepared in advance.
Model management was opaque — risk teams did not know which model version was powering a forecast, making governance and audit difficult.
Multiple data sources — PostgreSQL, REST APIs, and flat files — had to be unified before any prediction could be run.
Sakha Solution
MindsDB deployed as the central ML interface — models registered and versioned inside MindsDB, queryable via standard SQL syntax.
Text-to-SQL query layer — risk managers describe the forecast they need in plain language; the layer converts it to a MindsDB query.
React dashboard with embedded query builder — business users interact with a point-and-click interface that generates and runs forecast queries behind the scenes.
Unified data connector — PostgreSQL, REST APIs, and CSV sources all normalised into a single virtual schema accessible from one query endpoint.
Technical Highlights
Impact
Measurable outcomes delivered to our client.
Risk managers now self-serve forecasts in seconds — what used to take a data science ticket now takes a SQL query or a plain-language prompt.
10+ predictive models deployed and actively used — including credit risk, churn, and claims frequency models serving live business decisions.
Forecast turnaround collapsed from days to seconds — business reviews run with live model output, not prepared slides.
Data science team freed for model development — backlog of ad-hoc requests eliminated, team focused on building better models rather than serving queries.
"Our risk managers are now running queries that used to take three days. That is not an incremental improvement — it is a different way of working."