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Explained: How Indian Banks can use power of Network Intelligence to combat fraud

Explained: How Indian Banks can use power of Network Intelligence to combat fraud

Explained: How Indian Banks can use power of Network Intelligence to combat fraud
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By CNBCTV18.com Contributor Dec 23, 2020 3:24:02 PM IST (Published)

Through improved collaboration, banks can create a community where real-time information on emerging risks is freely shared between members, including central infrastructure (CI) owners.

Payments on the UPI platform in India were a record 1.6 billion in August. This rise in digital transactions signals India’s dream of transforming into a cashless (or more realistically a less cash) economy is being realized.

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However, banks are rather nervously monitoring the rising volume of low-value digital payments. The reason being the incremental cost of processing these “low-value, high-frequency” payments is relatively high, and banks must constantly upgrade their systems to monitor fraud risks in line with the rising transaction load.
Criminal networks and banks’ fraud detection machinery are pitted against each other across a highly-interconnected digital ecosystem. While individual banks are increasingly employing machine learning and implementing multi-layer fraud detection strategies, staying a step ahead of fraudsters will require a more collaborative approach.
Creating a network for better intelligence 
Fighting fraud alone rarely pays off. That is why, despite their traditionally competitive nature, there is a need for closer cooperation between banks when it comes to fraud data and intelligence sharing.
Through improved collaboration, banks can create a community where real-time information on emerging risks is freely shared between members, including central infrastructure (CI) owners.
Network intelligence capabilities can enable the banking ecosystem to strengthen its fraud management infrastructure to the core. By sharing fraud intelligence, banks can more quickly identify threats, and alert the wider community to ever-evolving fraud tactics.
This essentially means that banks can incorporate these insights into their machine learning models more quickly and cost-effectively.
An added benefit of this approach is that CIs or governing bodies (RBI in India) can be more actively involved in coordinating or facilitating fraud defense across the community.
Without interfering in the decisions that member banks make regarding their fraud detection strategies, a community facilitates end-to-end communication between banks and the governing body, which increases transparency without compromising data-sharing compliance.
Overall, this collective front is better equipped to respond to new and emerging risks, preventing them from becoming endemic threats.
India’s reality on the ground
The Reserve Bank of India’s bid to create a Central Payment Fraud Registry for monitoring digital payment frauds on a real-time basis, providing customers with periodic aggregated risk data, is a step in the right direction.
According to a recent survey conducted by ACI and YouGov, nearly half of Indian consumers are more worried about fraud while making digital transactions amid the coronavirus crisis.
Per the study, when a fraudulent transaction does occur, around 60 percent of respondents would first call their bank to block their account. This indicates that consumers still consider their bank the first line of defense during this time of heightened awareness.
With the digital payment ecosystem experiencing robust growth, which has barely faltered even as consumer spending dipped due to lockdowns, fraud detection and management have become the top priority.
A central fraud registry will ensure a systematic response to fraud while equipping the banking ecosystem with insights and data to strengthen their machine learning algorithms.
Compliant, community information sharing
Banks can enable real-time sharing of their fraud models in meta-data format this is called compliant information sharing. Through compliant data sharing, the community can also share their key performance data that supports their efficacy.
Automatically stripping the metadata of any identifiable information resolves the burden and regulatory risks of extrapolating and submitting data externally. This enables the community to share more data at a lower risk.
While the central body – RBI or NPCI in the case of UPI transactions – controls quality and ensures consistency by pre-aggregating data, it does not decide which models the group adopts – the community does that.
Furthermore, members can adopt, adapt, or combine features with their models; however, they see fit – and with no limit on the number of models that can run concurrently.
In this way, participants can integrate proven model features into their own customized adaptive machine learning strategies. This drives unparalleled access to all the information needed to assess transaction risk levels.
Agility and adaptability are crucial in a real-time world
While we laud the developments leading the banking ecosystem towards the power of networked intelligence, it is crucial to keep in mind that deploying and constantly adapting predictive machine learning models is now the need of the hour.
Banks and other financial institutions need solutions that reduce their reliance on specialized resources and allow them to adopt a business-led machine learning strategy that is aligned with today’s fast-paced, 24/7 fight against fraud.
This means implementing solutions that support the complete model development process, allowing for easy access to examine and analyze data, calculate fraud scenarios, and document key modelling steps.
A robust fraud management mechanism that relies on networked intelligence, provided and leveraged by multiple stakeholders in the ecosystem, will increase efficacy. It will strengthen consumer confidence and protect banks’ reputations, ensuring that India continues to lead the way in digital payments.
--Damon Madden is  Principal Fraud Consultant – Payments Risk Management at ACI Worldwide.
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