How PaywithRing is Walking the Extra Mile for Security & Customer Convenience

Picture this. It’s a Sunday morning. You are relaxing on your couch enjoying your morning cup of tea when suddenly you get an SMS stating that you have defaulted on a loan EMI. You are shocked because you haven’t taken any loans, so the question of default does not simply arise.

Welcome to the dark reality of India’s digital boom, which has empowered consumers in terms of convenience on one side but has also generated a golden opportunity for cybercriminals to carry out online frauds. Scamsters are constantly innovating to find new ways to con people by misusing technology such as spoofing, phishing, transaction fraud, identity theft etc. With rising instances of frauds, the responsibility of preventing scams without hindering the experience of genuine customers lies more with businesses rather than the consumers.

To overcome this massive challenge and stay one step ahead of scamsters, PaywithRing has employed a series of latest security measures such as:

Machine learning-based fraud detection solutions

Machine learning-based fraud detection models, can analyse several transactions and user behaviours on a real-time basis and raise a red flag when there are irregularities such as location change, changes in the transactional amount etc. PaywithRing has employed cutting-edge Deep Learning machine models, that keep learning continuously and automatically from fraud instances without the need for retraining. Such models are very efficient in detecting new types of frauds.

Global device identifier (GDI)

Proprietary unique device detection systems such as GDI used by organisations can detect if the device (smartphone/tab) used by the applicants is linked to a positive reputation without disturbing their overall browsing experience. While such data is never shared with 3rd parties, GDI works in a privacy-conscious manner by analysing the applicant’s device and associated identities across various digital channels to reduce risk in real-time.

Behavioural fraud detection

Behavioural fraud detection analyses parameters such as typing speed and the pace at which the applicant is moving through the workflow etc., to identify potential frauds.

Open-source mapping solution – H3

H3 grid open-source mapping solution is a hexagonal geospatial indexing system that uses tiered linear indexes to organise data. Geospatial velocity radars can detect sudden transaction spikes from certain areas and help identify common fraud patterns.

1: n facial dedupe

1: n facial dedupe verifies the applicant’s photograph appearing in the identity proof with the organisation’s existing user database to eliminate potential frauds and safeguard the interests of genuine customers. For instance, if a fraudster tries using a PAN of an existing customer to avail of loans, this innovative technology can detect the abnormality and prevent fraud.

To conclude, online fraud detection and prevention is a continuous process. By using the power of cutting-edge technologies PaywithRing has taken the battle against scamsters to the next level while ensuring a wow experience for genuine customers.