Wie kann Mobile Payment Analytics die Sicherheit verbessern?

In my experience analyzing mobile payments requires considering a more diverse range of factors to get the full picture. Here are some additional non-exhaustive elements to consider -Transaction type: retail payments, P2P transfers, online payments, etc. -Payment method: credit/debit card, e-wallet, QR codes, NFC payments, DCB etc. -Transaction frequency: important indicator of their engagement and preferences. -Category of products/services purchased -Conversion rate: helps evaluate the effectiveness of the checkout process. -User behavioral analysis -Transaction security: Examine fraud attempts -Integration with other services: loyalty programs, financial management apps, etc.

Mobile payment analytics encompass tools and methodologies for gathering, measuring, and analyzing data from mobile payment transactions. This includes details like the number, volume, value, location, time, device, channel, and payment types. By leveraging these analytics, businesses can uncover valuable insights into their mobile payment operations, customer preferences, market trends, and potential revenue opportunities, enabling informed decision-making and strategic planning to optimize mobile payment services.

Customers are appreciative of occasionally being contacted for anomalous purchases if the approach is not too disruptive to their shopping. Companies can set thresholds for purchases in general or have them on a client by client basis and flag transactions that seem out of the ordinary. Depending on the frequency and amount of these, the vendor can either choose to freeze the account until the purchase authenticity is verified, or simply provide a warning to the client and freeze the account if there is no response within 24 hours (or if more anomalous purchases are made).

The basic elements in terms of cybersecurity to prevent fraud in mobile payments as I observe it every day are: Real-time transaction analysis: to identify suspicious transactions and block them before they are allowed to go through. Behavioral anomaly detection: to help identify unusual behavior, such as transactions carried out from unusual geographic areas, etc. A use of machine learning to detect fraud schemes: Machine learning algorithms can be trained to recognize fraud schemes by analyzing large amounts of mobile transaction data. They can identify trends that would be difficult to detect manually. Device and application monitoring: to help detect fraud attempts, such as malicious apps. Not forgetting analysis of social media

We clearly see at Evina that this analysis of mobile payments is an essential pillar of data protection in mobile payment transactions! In addition to guaranteeing compliance with regulations (PCI DSS, RGPD, CDP, etc.), it uses several means of data protection to reinforce security and confidentiality, such as: Data encryption that ensures only authorized parties can access information, Or Data anonymization which preserves user confidentiality while allowing analysis of payment trends and behavior. These means, combined with proactive, real-time monitoring of user access and activities, make it easier to detect any suspicious or unauthorized activity and enable rapid response to threats.

Above all, I would tend to say that analytics plays a vital role in providing the information needed to plan, implement and evaluate improvements in all areas of business… Without proper analytics, efforts to improve the mobile payment can be ineffective or even counterproductive. From this postulate we can cite the standard improvement levers which have to be used in our ecosystem: the imperative monitoring of transactions in real time to avoid surprises (bad ones in general, etc.), the detection of fraud and other anomalies, the precise identification of the latter to feed pattern fraud schemes, systematic prevention of attacks which will succeed to improve risk management and compliance with existing or future regulations.