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USE CASE · FINANCE

Transaction, customer, and credit data —
used freely, within the rules.

Privacy law, credit information law, Basel requirements — even as financial regulation tightens, RealDataEcho opens the way with synthetic data, so credit modeling, fraud detection, and anomalous-transaction simulation can move forward without friction.

CHALLENGES

The data challenges facing financial firms
come down to three

Tightening data regulation

Amendments to privacy and credit information laws are making external use and cross-team sharing increasingly difficult.

Scarce fraud and anomaly cases

Fraud accounts for less than 0.1% of all transactions — too little training data caps detection model performance.

Barriers to external collaboration

Blocked data sharing with fintechs and research institutes slows down new product and model development.

SCENARIOS

Three core scenarios on the finance floor

The synthetic data patterns most often used in real deployments.

SCENARIO 01

Training datasets for credit scoring models

Safely convert real customer transactions and delinquency histories into synthetic data. Shorten the credit model development cycle with training data you can share even with external development vendors and fintechs.

  • Training sets that can be shared with external development vendors
  • Proven parity — AUC at 94% of the original data
  • Automatically clears credit-information-law de-identification standards
Synthetic transaction dataset (summary)
Customer IDCredit GradeDays OverdueMonthly VolumeChurn
SYN-0001B+02,140,000No
SYN-0002A03,850,000No
SYN-0003C14620,000Yes
SYN-0004B31,470,000No
Model AUC (vs original)
0.94
SCENARIO 02

Fraud and anomaly detection simulation

Augment scarce fraud cases more than 10× with synthesis. Solving the class imbalance problem improves the fraud detection system's detection rate and false-positive rate at the same time.

  • 10× augmentation of scarce fraud cases
  • False positives down 32% · true detection rate +18%
  • Simulation and stress testing of new fraud patterns
FDS performance comparison
Trained on original only — detection rate
0.71
Trained with synthetic augmentation — detection rate
0.89
False-positive reduction
−32%
PoC validated
SCENARIO 03

MyData and open banking product PoCs

Pre-validate MyData-based products and services without touching real customer data. Run fast market-fit tests even before personal data consent procedures are in place.

  • Validate user scenarios before launch
  • PoC data ready on day one for fintech collaboration
  • Development cycle cut from 6 weeks to 2 on average
Shorter PoC cycles
Before (waiting on real-data approval)
6 wks
After adopting RealDataEcho
2 wks
Time saved
−67%
OUTCOMES

Measured results in the finance domain

Averages measured in PoCs with major Korean banks and card issuers.

0.95
Distribution similarity
Validated on transaction and credit data
+18%
Higher FDS detection rate
With synthetic-augmented training
−67%
Shorter PoC cycles
From 6 weeks to 2 on average
< 0.01%
Re-identification risk
Passes credit-information-law standards
"
"Sharing data with our external development vendor used to take four weeks on average. After switching to synthetic data, we can distribute it the same day — and the model performance is nearly identical to the original, which surprised us."
Kim O.Head of Data Analytics · Major Korean commercial bank

Put your financial data to work — freely, within the rules.

Banks, cards, securities, fintech — start a domain-tailored PoC with a 30-minute demo.

Request a Demo → Ask About a Finance PoC