GBG Introduces Fraud Detection Module Via Transaction and Payment Monitoring Using ML

Date : 28-May-2020
Location: Singapore


GBG,the global technology specialist in fraud and compliance management, identity verification and location data intelligence.

Key Takeaways:

GBG, today announced its expansion of Artificial Intelligence (AI) and machine learning (ML) capabilities for its transaction and payment monitoring solution, Predator, making deep learning and predictive analytics available to their entire digital risk management customer journey.

  • Instinct Hub, GBG Digital Risk Management and Intelligence platform for fraud detection.
  • New module, transaction and payment monitoring solution, Predator.
  • GBG Machine Learning utilizes Random Forest, Gradient Boosting Machine and Neural Networks – three leading and proven algorithms for fraud detection.
  • GBG Machine Learning is designed to simplify machine learning deployment for both fraud managers and data scientists, removing the need to have a data scientist in-house or having to work back to back with the vendor to lower cost of operation.
  • The solution takes a “white box” approach to provide an open and transparent modelling process for ease in model governance and meeting regulatory requirements.
APAC Covid-19 Fraud Risk Survey - Most vulnerable fraud type
APAC Covid-19 Fraud Risk Survey - Most vulnerable fraud type

  • June Lee, Managing Director, APAC,GBG,said, standard fraud model deteriorates over time due to the evolving nature of fraud. Therefore, only continual and autonomous model training is able to counter fraud detection effectively.
  • Michelle Weatherhead, Operations Director, APAC,GBG,said,the COVID-19 resulted in a surge of e-banking transactions, hence, fraud detection reaches a new height of urgency and importance.
  • Dr Alex Low, Data Scientist, GBG,said, the SME segment, which usually poses higher credit risks, banks could leverage ML to spot irregularity in borrower patterns by assimilating both identity, profile and behavioural type data,to mitigate the risks.
June Lee, Managing Director, APAC, GBG
June Lee, Managing Director, APAC, GBG

  • Based on GBG’s “Understanding COVID-19 Fraud Risks” poll results in April, 37% of respondents see transaction fraud as the fraud typology that they are most vulnerable to.
  • Today machine learning provides an average of 20% uplift in fraud detection, GBG Machine Learning has performed well to provide incremental alerts on missed frauds for our customers.
Editor's Comments:
  • In-house data scientists take over external consultants in deriving business insight for businesses; in turn, AI software platforms complement the former by providing a higher level of automation.