Fraud doesn’t live in a single row. It hides in the relationships between doctors, hospitals, patients, and corporates. Sanket turns a raw claims file into an audit workspace with dashboards, relationship graphs, risk scores, and evidence-backed AI intelligence reports.
An investigator can read a claims file line by line and still miss the pattern: the doctor who appears across six unrelated hospitals, the patient on three corporate policies, the facility whose claims all route through one provider. Those connections are where organized fraud hides.
Sanket reconstructs the whole network from a single upload, scores every entity for risk, and writes the case file. Investigators start with leads, not a wall of rows.
Everything an investigator needs to go from a raw claims file to a defensible case: dashboards, graphs, scores, and AI-written reports, all in one workspace.
Upload an XLSX or CSV, map the columns, and Sanket builds a complete workspace from raw claims data: dashboards, searchable records, graphs, and scores.
Every claim becomes links between corporates, hospitals, patients, and doctors. Click to focus, filter by entity, and surface the clusters that signal collusion.
Doctors, hospitals, and patients each get a transparent 0-100 score built from rejection rate, claim volume, total amount, and cross-entity spread.
Generate a structured case file on any entity: profile, claim mix, counterparties, top claims, flag state, and a narrative grounded in retrieved evidence.
Flag suspicious entities with a reason and add investigation notes. Each flag is stored as evidence, so future reports retrieve it and build on prior work.
Full-text search across every claim field, status filters, and sortable records. Export the active dataset to XLSX or CSV for downstream review.
Total claims, claimed amount, rejection rate, unique doctors and hospitals, status mix, timelines, and the top entities. Summarized the moment a dataset is active.
Corporate to hospital to patient to doctor, rendered as a force-directed graph. Edges aggregate claim counts and amounts, so dense clusters and suspicious bridges become impossible to miss.
Each doctor, patient, and hospital is scored on rejection rate, claim volume, total amount, and cross-entity spread. Capped at 100, shown with risk-colored bars. The reasoning behind every number is clear.
For any entity, Sanket assembles a structured profile, counterparty summaries, and top claims, retrieves prior flags and notes as evidence, and writes a narrative. Cached and regenerable on demand.
Drop in a claims file and Sanket handles the rest: parsing, mapping, scoring, and reporting. Every dataset stays scoped to the investigator who owns it. Reports are grounded in retrieved evidence and cached for fast re-review, with regeneration one click away.
Five steps from a spreadsheet to a defensible investigation. No data engineering. No pivot tables. No manual report assembly.
Drop in an XLSX, XLS, or CSV claims file. Parsed right in the browser.
Sanket auto-maps likely fields; confirm or correct the mapping in seconds.
The dataset becomes an investigation session, owned and saved for later.
Explore trends, the network graph, and risk scores; flag entities and add notes.
Generate evidence-backed intelligence reports and export the dataset.
Investigators stop reading rows and start following relationships. The collusion ring that hides across six hospitals is now a cluster you can see and click into.
What used to take a day of pivot tables and manual report-writing now takes minutes. Upload, explore, and generate the case file from inside one workspace.
Every risk number is explainable. Scores are rule-based and transparent, so a finding holds up when challenged by an auditor, a partner, or a regulator.
Investigation memory compounds. Flags and notes are stored as evidence and resurface in future reports, so the team builds on prior work instead of repeating it.
Stop experimenting with isolated AI tools. Start building a unified, context-aware digital ecosystem that drives real results.