This decade saw major digital transformations across most, if not all, industries. And most evidently, in the banking sector due to the data-intensive and time-sensitive nature of its operations. It has become a top priority (and challenge) for banks to continuously improve and expand its business processes with digital adoption. Otherwise, they risk becoming obsolete very quickly amongst huge competition from players all over the world.
Enhance Analytical Models
One core task in banks is to discriminate good and bad risks so as to drive revenue, control costs and mitigate risks. While many banks have developed their own models, some of them are known to have an underperforming Gini coefficient (a measure of how powerful a model is in terms of its ability to discriminate between good risks and bad risks) of around 40-45%. N10’s predictive and anomaly detection models can provide several valuable indicators to evaluate the credit behaviour of bank’s consumers. By capturing the digital footprint of consumers, N10 can run the data through its cloud-based AI engine to baseline, detect deviations and find anomalies continuously and at real-time.
Drive Efficiency in Payment Processing
The IT systems running payment processing in banks spread across a massive network of servers, message buses and various applications. N10 can provide effective real-time monitoring and management of these systems, which will allow banks to process a high volume of payments with low latencies. This provides commercial benefits and ensures compliance with Service Level Agreements (SLAs). N10’s AI engine can also preempt and prevent catastrophic system outages so as to ensure that critical applications are always up and running. In the example of retail banking, N10 can provide analysis of the real-time health of applications, so that banks can ensure that end-users will be able to seamlessly access bank services 24/7. By replacing traditional system monitoring methods with smarter AI-driven solutions, N10 can also help customer support teams streamline their fault management processes and reduce the mean time to investigate (MTTI) and the mean time to repair (MTTR).