Real-time payments (RTPs)like IMPS and UPI are more susceptible to transaction declines than traditional payment methods, such as credit and debit cards. This is because RTPs are processed in real time, which means that there is less time to detect and prevent fraud.
The digital payment process is extremely complicated, with many moving parts and a lot that can potentially go wrong. When payment failures do occur they lead to abandoned shopping cards, frustrated customers, and average expenditures drop as consumers tend to spend less once their payment has been declined? – severely impacting bottom lines.
Even problems that affect only a small subset of transactions can quickly impact many customers before you even realize there’s an issue.?
With the ongoing growth in operational complexities, effectively monitoring payment transactions is becoming a radical challenge. At the same time, customers expect flawless service and availability, and are quick to seek other options when things don’t go as planned.
Fintech operators, therefore, need to reduce manual monitoring and increase automation and visibility into their operations.?
For any bank, payment processor or merchant concerned about streamlining operations and optimizing approval rates, transaction monitoring can help:
- Proactively isolate payment performance issues
- Prevent downtime and potential lost revenues
- Spot abnormal behavior and curtail fraud attempts
- Monitor profitability across all channels?
With transaction monitoring, merchants, acquirers and payments processors can gain unparalleled insights into transactions and trends, improving the performance, productivity and reliability of payment transactions across all touchpoints.
Global payments leaders are increasingly relying on AI to improve OpEx and customer experience by optimizing payment processes and conversion/approval rates.? Through the strategic use of AI-based business monitoring ,Fintechs can reduce payment failures including rejections, chargebacks, payment attempts, and refund requests — as well as better manage payment fees and rates, including transaction fees and FX rates fluctuations.
With AI-based autonomous transaction monitoring, users can:?
- Seamlessly monitor approvals, declines, returns and refunds
- Improve efficiency, accuracy and speed across the entire digital payments optimization process
- Boost payment approval and conversion rates
- Monitor merchants’ activity and protect revenue
- Monitor risk in payments
To coordinate the future of eCommerce and payments requires monitoring tools that lean heavily on AI & ML in real-time. Anodot helps fintechs stay on top of their business, deliver flawless customer experience and optimize revenue and OpEx, through timely anomaly detection and highly accurate forecasts for liquidity.??
- Fastest time to accurate detection.?Anodot autonomously distills billions of data events into the single spot-on alerts that you need to know about right now. Alerting in real time cuts time to detection, enabling proactive incident management by payment operations teams.?
- Full coverage.?Anodot collects and analyzes data across the entire payment? stack and ecosystem. All metrics are actively monitored, at scale, enabling operators to achieve full visibility over the total of services, processes, partners, customers and business KPIs.?
- Correlation.?Anodot’s patented correlation engine?correlates anomalies
?across the business for holistic root cause analysis and the fastest time to resolution, leading to significantly improved approval rate, availability and customer experience.??
- Autonomous.?Anodot is completely autonomous. There’s no need to define what data to look for or when, no manual thresholds to set up or update. New use cases can be added on the fly, and no monitoring maintenance is needed even as the business’s configuration changes.?
- Ease of use.?Anodot is built for business users, so no data science is required. It is easily integrated with all types of data sources, and just as easily applied to new services and use cases.
AI-based tools can help to reduce transaction declines in RTPs by:
- Detecting fraud in real time:?AI-powered fraud detection tools can analyze large volumes of data to identify patterns and anomalies that may indicate fraud. These tools can be used to flag suspicious transactions in real time, allowing payment providers to decline them before they are processed.
- Preventing legitimate transactions from being declined:?AI-based tools can also be used to help payment providers approve legitimate transactions that would otherwise be declined. For example, an AI-powered tool can analyze a customer's past transaction history to determine if a transaction is likely to be legitimate. This can help to reduce the number of false declines, which can improve the customer experience.
- Automating the transaction approval process:?AI-based tools can also be used to automate the transaction approval process, which can help to reduce the time it takes to approve legitimate transactions. This can be especially beneficial for businesses that process a high volume of transactions.
Some specific examples of how AI-based tools are being used to reduce transaction declines in RTPs:
- Visa Smarter Stand-in Processing (Smarter STIP): This AI-powered tool analyzes past transactions to generate decisions to approve or decline transactions on behalf of issuers if their systems go offline. Smarter STIP has been shown to reduce transaction declines by up to 50%.
- PayPal Fraud Detection System: This AI-powered system analyzes billions of data points in real time to detect and prevent fraud. PayPal claims that its fraud detection system has helped to reduce fraud losses by more than 50%.
- Mastercard AI Risk Management Platform: This AI-powered platform helps Mastercard customers to identify and mitigate fraud risks. The platform uses a variety of data sources, including transaction history, device data, and geolocation data, to assess the risk of each transaction.Overall, AI-based tools are essential for reducing transaction declines in RTPs. By detecting fraud in real time, preventing legitimate transactions from being declined,
Co-Founder & CEO, TechBulls | Product Development Services in FinTech
1 年Insightful article. I think AI's role will evolve rapidly across the FinTech landscape. The use cases you shared on RTP transaction declines and using AI for identifying fraud/ verifying legitimate transactions are strong. It would be great to see this in action though (unsure if this is already available). What are some of the other AI use cases you are seeing @Ram Rastogi? This post was a delightful read.
Driving inclusive real time payments
1 年Both failed transactions and fraud monitoring are separately the next big thing we need to solve. Thank you for highlighting Ram Rastogi ???? - these problems get harder (but more important) when we try to include banks and non-banks in the same system. Have you seen any published data from the successful IPS/RTPs on failed transactions?
Technology Evangelist
1 年Thanks Ram Rastogi ???? sir highlighting most concerns for top management. Anodot guys are simply brilliant with their AI/ML stack. I had seen GooglePay internally have good tool to monitor RTPs - their team get into action much faster for UPI declines then the bank's monitoring team. Fraud reduction will remain in top of chart as we keep on witnessing growth in E-commerce and Digital payments across the board.