Regulatory Sandbox (RS) – Fourth Cohort on Prevention and Mitigation of Financial Frauds – Test Phase
The Reserve Bank had announced opening of the Fourth Cohort under Regulatory Sandbox vide Press release dated June 06, 2022 for theme ‘Prevention and Mitigation of Financial Frauds’.
2. The Reserve Bank received nine applications of which six have been selected for the ‘Test Phase’. The entities, as per details below, shall commence testing of their products from February 2023.
| Sl. No. | Sandbox Entity | Description |
| 1 | Bahwan Cybertek Private Limited | The 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. |
| 2 | Crediwatch Information Analytics Private Limited | The 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. |
| 3 | enStage Software Private Limited (Wibmo) | The 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. |
| 4 | HSBC in collaboration with enStage Software Private Limited (Wibmo) | The product 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. |
| 5 | napID Cybersec Private Limited | The product ‘napID Fraud Filter Layer’ locks the login form, payment form, ATM, POS machines and enable it only for the authorised user to initiate the transaction using their credentials via napID Zero-Factor Authenticator app. |
| 6 | Trusting Social Private Limited | The 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 residential and office address of users. This helps to verify the accuracy and stability of address/ location declared by prospective customers. Further the credit insight feature uses mobile subscriber data to risk rank the active subscribers bases in order to assess the risk of default of a loan applicant. |
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