INETCO Solutions for Real-Time Payments

INETCO Solutions for Real-Time Payments

Start thinking real-time about real-time payments

Helping banks and credit unions enhance the service delivery, security and analysis of faster payments

The adoption of real-time payments is a high priority. But faster payment rails and settlement methods such as mobile wallets, push-to-card, contactless-enabled cards and API payments add more complexity when it comes to managing the end-to-end payment journey. With increasing payment speed and irrevocable payments services also comes a rising number of fraud attacks – a threat that is expected to grow as the time window for accurate risk assessment and authentication gets smaller and smaller.

INETCO solutions provide the real-time decisioning tools and “always on” data visualization that banks and credit unions need to manage their real-time payments channel and meet customer expectations around security, availability and faster payments service.

  • Adopt ISO20022, implement real-time payments rails and run new settlement methods without a hitch. 
  • Gain end-to-end visibility into every payment and isolate the root cause of real-time payments bottlenecks, failures and unexpected declines ~85% faster.
  • Keep your organization and your customers safe from fraud – detect, investigate and block suspicious real-time payments activity in milliseconds.

INETCO Insight use cases for real-time payments

Real-time data capture, ISO20022 protocol decoding and end-to-end correlation

Capture and decode ISO20022 and other payment transaction data in real-time. All remittance data, message fields, IP/network communications data and response timing information contained in each real-time payments transaction is made available for service performance monitoring, fraud detection and customer analytics. No clunky decode scripts required. Create a “ready to analyze” data pipeline so that you can leverage rich real-time payments transaction data in any machine learning model, predictive algorithm, on-demand analytics or fraud system of choice.

Real-time, end-to-end monitoring of faster payments

Continuously monitor both the send and receive directions of real-time payments. Profile each transaction to instantly know if there are failed transactions on the acquisition side related to no sufficient funds in the payer account or transfers exceed the transaction limit. Increase operational efficiency by proactively monitoring the stability of point-to-point real-time payments transactions – across all transition points and payment settlement methods – in one centralized location. A correlated view across both the acquisition side and payment rail connections results in a ~85% faster remediation of transaction, network and application bottlenecks or failure points.

Real-time payment fraud detection and rules based alerting

Investigate excessive numbers of real-time payments from one customer ID in milliseconds. Match payment requests with the payment submission transactions and instantly detect mismatched fields or when transaction links are missing due to man-in-the-middle malware or internal fraud attacks. Receive instant notification of suspicious payments activity related to account takeovers, money mules or false accounts. Track transaction anomalies using flexible metrics such as IP geographical proximity between two transactions.

Analysis of real-time payments customer usage and revenues

Decode all ISO20022 message fields, RFP/RFI remittance data and network communications information to fuel advanced real-time payments analytics. Understand fee revenue generation, forecast timing for reconciliation and request to pay, and gain insights into how customers engage. Predict transaction volumes and liquidity needs. Trend the adoption of faster payment options, average spend per customer, payment limit thresholds and account to account transfer volumes across B2B, C2B, P2P and bill pay.

Real-time payments risk scoring and adaptive machine learning

Flag, score and block known fraud patterns and new suspicious real-time payments activity in milliseconds. Continuously feed payment data into rules-based alerts and machine learning models to detect known fraud attack signatures and emerging patterns. Rebuild individual customer risk scoring models every time a real-time payments transaction occurs.

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