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6 Entities to Test Products for Prevention & Mitigation of Financial Frauds

6 entities to test products for prevention & mitigation of financial frauds

The Reserve Bank of India had announced the opening of the Fourth Cohort under Regulatory Sandbox in June 2022 for the theme ‘Prevention and Mitigation of Financial Frauds’. The RBI has received nine applications, of which six have been selected for the test phase. The entities will commence the testing of their products in February 2023.

Sandbox entities include Bahwan Cybertek, Crediwatch Information Analytics, enStage Software, HSBC in collaboration with enStage Software (Wibmo), napID Cybersec and Trusting Social.

Bahwan Cybertek’s solution ‘rt360 Real Time Monitoring System’ provides a comprehensive surveillance mechanism for monitoring transactions and events from loan accounts on a continuous and real-time basis.

Crediwatch Information Analytics’ solution ‘Crediwatch EWS’ is an early warning system for credit monitoring and fraud identification that looks at data across public and private data sources to uncover early signs of trouble for commercial borrowers.

enStage Software (Wibmo)’s product ‘Trident FRM’ is a risk based authentication solution aimed at providing a frictionless (without OTP) transaction experience for low-value transactions based on risk assessment of users.

HSBC, in collaboration with enStage Software (Wibmo), has come up with a product, which is a closed user group AI/ML-based solution, which helps in card-not-present fraud detection by leveraging the power of collaboration across banks and financial institutions using historical transactions and contextual information through advanced technologies.

napID Cybersec’s product ‘napID Fraud Filter Layer’ locks the login form, payment form, ATM, POS machines and enables it only for the authorised user to initiate the transaction using their credentials via napID Zero-Factor Authenticator app.

Trusting Social’s product ‘Trusting Social CI & AV’ helps in address verification by running its proprietary AI algorithms on non-personally identifiable data on subscriber usage, location signals and other such parameters to predict the residential and office addresses of users. This helps to verify the accuracy and stability of the address/location declared by prospective customers. Further, the credit insight feature uses mobile subscriber data to risk-rank the active subscriber bases in order to assess the risk of default of a loan applicant.


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