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VFACE

Financial operations, secured and intelligent.

Fraud detection AI, blockchain settlement, and digital banking platforms — for financial institutions where trust is the product.

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$4.5T
Annual Fraud Losses Globally
90%
Reduction in Fraud Detection Time (avg)
60%
Faster Settlement via Blockchain

The challenge

Banks must modernise faster than fraud and regulation move

Fraud is faster and smarter

Rules-based systems miss new patterns. Real-time ML catches fraud while keeping false positives low.

Core systems hold back change

Decades-old cores slow every launch. API layers and phased modernisation unlock speed without a big-bang risk.

Regulation keeps expanding

PSD2, DORA, Basel III and AI rules demand evidence, not promises.

What we deliver

AI Fraud Detection

Real-time transaction scoring models that detect fraud patterns across millions of daily transactions with sub-50ms latency — reducing false positives by up to 75% compared to legacy rules engines.

Blockchain Settlement Networks

Hyperledger Fabric permissioned networks for interbank settlement, trade finance, and cross-border payment reconciliation — reducing settlement cycles from days to hours.

Digital Banking Platform Development

Cloud-native digital banking infrastructure — mobile-first customer interfaces, API banking layers, and core banking modernisation programmes for challenger banks and incumbent digital transformation.

Risk Management & Stress Testing

ML models for credit risk scoring, counterparty risk assessment, and regulatory stress testing — integrated with existing risk frameworks and reporting systems.

How we build

Engineering principles behind every engagement

Risk-aware by default

Every build starts with the risk and control framework the bank already uses.

  • Control mapping
  • Model risk documentation
  • Three-lines-of-defence alignment

Technology we work with

  • IBM watsonx
  • Hyperledger Fabric
  • Apache Kafka
  • Kubernetes
  • Red Hat OpenShift
  • IBM QRadar
  • Spring Boot
  • PostgreSQL
  • Temenos APIs
  • Splunk
  • Terraform
  • HashiCorp Vault
  • IBM watsonx
  • Hyperledger Fabric
  • Apache Kafka
  • Kubernetes
  • Red Hat OpenShift
  • IBM QRadar
  • Spring Boot
  • PostgreSQL
  • Temenos APIs
  • Splunk
  • Terraform
  • HashiCorp Vault

Your first 90 days

What you can expect, phase by phase

Days 1–30

Assess and align

  • Risk and control mapping
  • Data and system landscape
  • Regulatory gap review
  • Use-case business case
Days 31–60

Build under control

  • Model or platform build in sandbox
  • Security and model validation
  • Integration via API layer
  • Compliance sign-offs
Days 61–90

Shadow and launch

  • Shadow-mode run vs production
  • Staged go-live
  • Monitoring and incident plan
  • Scale roadmap

Client outcome

Banking
Digital-first regional bank (2.3M daily transactions)

A Southeast Asian digital bank was losing $4.2M annually to transaction fraud, with a legacy rules engine producing a 12% false-positive rate that was blocking legitimate customer transactions and driving churn among high-value account holders.

89%Reduction in fraud detection time
$3.1MAnnual fraud losses prevented
75%Reduction in false-positive rate

VFACE deployed a real-time ML fraud detection layer processing 2.3M daily transactions with sub-50ms scoring latency. The model reduced the false-positive rate from 12% to 3%, restoring $1.1M in recovered customer lifetime value from previously blocked transactions and eliminating the fraud analyst alert backlog that had grown to 4-day review cycles.

Solution

  • →Real-time fraud scoring model deployed on IBM Cloud with sub-50ms p99 latency
  • →Automated model retraining pipeline updating fraud patterns weekly without engineering intervention
  • →Blockchain-based transaction audit trail satisfying MAS TRM evidence requirements
  • →Analyst workflow integration reducing alert investigation time from 22 minutes to 4 minutes per case
IBM Cloud
IBM Watson
Hyperledger Fabric
Apache Kafka
Python / XGBoost

Industry benchmark

Banking programmes: VFACE vs typical

Fraud loss reduction

VFACE programmes40%
Industry average22%

False-positive alert rate

VFACE programmes18%
Industry average45%

Lower is better

Time to launch a new digital product

VFACE programmes12 wks
Industry average30 wks

Lower is better

Figures are indicative ranges from typical engagements compared with published industry surveys. Results vary by scope and starting point.

Client voices

What our clients say

40%

lower fraud losses

“Fraud losses fell by 40% in the first two quarters, and our investigators finally had clear reasons behind each alert.”
C. NavarroChief Risk Officer, Regional retail bank

Security and governance

Regulatory compliance and operational resilience

PSD2 and DORA

Strong customer authentication and ICT resilience evidence built in.

Model risk

Documentation, validation and monitoring aligned to SR 11-7 style guidance.

Data protection

GDPR, tokenisation and encryption of sensitive financial data.

Auditability

Immutable logs for every decision, change and access.

Insights and news

What is happening in the industry, and what we think

Industry headlines, last 30 days

Headlines via Google News. Third-party content; VFACE does not endorse the linked articles.

From the VFACE blog

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Frequently Asked Questions

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