Traditional approaches and legacy point fraud management platforms have limitations that can result in too many false positive alerts to investigate and high operational costs, a condition that enables malicious activities to go undetected.
Typically, these solutions produce evidence of activity after fraud has taken place, which is a classic example of too little, too late.
A major shortcoming of these point solutions is that data fed into their engines lack context, and there is a reliance on often pre-determined, non-empirical rules to make a judgment on the legitimacy of transactions.
Disparate systems and databases make it difficult to identify, authenticate and validate citizens interactions quickly. At the same time, they can introduce more friction into the journey, especially if citizens must provide their details more than once to different departments or contact you via different channels.
By breaking down point solutions silos, wrapped around advanced analytics and integrating systems flexibly, you can implement a fraud ecosystem that provides a single citizen (fraud) view for the transaction.
This allows you to identify and authenticate customers with a greater degree of certainty, identify and react to potential fraud quicker, and remove friction from your customer journeys with fast, consistent services across all channels.
Recent advances in a range of technologies from big data, machine learning and improvements in API’s have coalesced to build new approaches to detecting fraud.
These can detect anomalous and outlying behaviours and activities in real time, wrapped around a strategy and workflow management capability, to provide accurate risk assessments so that mitigations can be triggered quickly.
The UK Government has shown progress since the NFA report in 2010 that talked about “centric” and “connectivity” by creating a data mindset through the Digital Economy Act (DEA) 2017 and the Open Data Agenda, but clearly there is still progress to be made.
The Cabinet Office has built a data sharing and analytics toolkit, launched data sharing pilots and is embedding skilled analysts across government departments to assist in the fight against fraud. But criminals and hackers are already using advanced technologies, including AI, to harvest information and perform fraud at machine-level speed.
To keep pace with the criminals, the public sector needs to consider enhancing legacy methods of fraud detection progressing work on new approaches that use:
With a holistic approach to data, workflow and decisioning layered across all detection points the public sector can severely impact the cost of fraud across the Government estate.
Experian are headline sponsoring ‘The International Counter Fraud Data Analytics Conference 2020’ in March. Along with other public and private sector partners, will be sharing our experiences of using data and analytics to fight fraud and error in Government. We’re looking forward to being part of this discussion and helping drive the fight against fraud in the future.
Want to be a part of the discussion? Register here