Financial Services
Point AI at your transaction data without waiting on review
GraphReplica turns your warehouse into a safe replica that behaves like production. Train fraud and risk models, run Gen BI, and ship in days.
The same customer keeps the same stand-in across accounts and transactions. Joins survive. PII never leaves your environment.
Source and safe replica
Swap the PII, keep the joins
Flip from the source warehouse to the safe replica. Names, emails, and account tails become consistent stand-ins while the keys that link your tables stay exactly where they were.
| cust_id | full_name | ssn_last4 | |
|---|---|---|---|
| C-4821 | Carla Mendez | carla.mendez@crestpoint.com | 2098 |
| C-4822 | Aaron Whitfield | a.whitfield@baylinemail.io | 5510 |
| C-4823 | Deepa Rao | deepa.rao@summit.co | 3744 |
| acct_id | cust_id | product | iban_tail |
|---|---|---|---|
| A-77310 | C-4821 | Checking | GB29 ... 3160 |
| A-77311 | C-4822 | Credit | GB29 ... 8827 |
| A-77312 | C-4823 | Savings | GB29 ... 4409 |
| txn_id | acct_id | amount | risk |
|---|---|---|---|
| T-90041 | A-77310 | $1,284.50 | low |
| T-90042 | A-77311 | $9,910.00 | high |
| T-90043 | A-77312 | $412.13 | low |
Every name, email, and account tail is a consistent stand-in. cust_id and acct_id never move. The join from customer to account to transaction holds across every row and every file.
Within about 5% of production performance.
Zero leakage across stress tests.
What you get
From blocked project to shipped model
Your teams sit on transaction data, customer records, and documents they cannot use. GraphReplica makes that data safe to use without slowing anyone down.
Train fraud and risk models on safe data
Point model teams at a replica that behaves like production. Patterns and relationships hold. Models stay accurate. Replica utility stays within about 5% of real-data performance.
Unblock Gen BI and coding agents
Aim BI tools and coding agents at a safe copy of your warehouse tables. Analysts and agents move now instead of waiting in the privacy review queue.
Ship in days, not review cycles
First safe dataset in under a week. Setup takes about an hour. Every project that used to stall on review starts moving the same week.
Why models stay accurate
Realistic stand-ins that keep your data connected
Joins survive across accounts, transactions, and customers
The same real customer gets the same stand-in everywhere. cust_id and acct_id never move. The link from a customer to an account to a transaction holds across millions of rows and years of history. Replacement consistency runs about 0.91 F1 with zero false identity merges.
Sensitive entities found, the rest left alone
GraphReplica detects names, account numbers, and identifiers across transaction tables and unstructured fraud-case notes, then replaces only those. Everything that is not sensitive stays exactly as it was. Detection runs about 0.93 F1.
Replicas that behave like production
Statistical shape and relationships carry over. A fraud model trained on the replica scores like one trained on real data. You build, evaluate, and red-team on data you can actually share.
Runs inside your environment, data never leaves
A container runs in your cloud, data center, or Databricks. Data never reaches Secludy and the run is air-gapped. Every run produces audit-ready reports. Built to meet GDPR, CCPA, and HIPAA requirements where relevant.
Bring a sample of your warehouse. We will hand back a safe replica that still joins.
Case study
A global commerce and payments platform
Large volumes of transaction data plus unstructured fraud-case documents, all needed for model work and all locked behind privacy review.
The situation
The fraud and risk team could not train or iterate models on production data without slow privacy review. Other vendors lost the relationships across tables. Their replicas turned one customer into different people and broke every join.
What GraphReplica did
It produced a safe replica that kept entity relationships across transactions, accounts, and customer records and across the unstructured fraud-case notes. The same customer kept the same stand-in everywhere. The data stayed connected.
The outcome
0% PII leakage. Model utility within about 5% of real data. First safe dataset in under a week. Joins held across 100M+ records and the fraud-case notes read naturally.
Weeks of privacy-review delay came off every project. Model teams now iterate on realistic data the same week they ask for it.
Fraud and risk modeling, global commerce and payments platform
Make your warehouse safe to use for AI
Book a demo and we will run GraphReplica on a sample of your data. Setup takes about an hour. Joins survive.