Using Advanced Analytics To Detect, Prevent And Tackle Fraud – A Cifas Use Case
This article is originally published at https://www.mango-solutions.com
The recent Transform Finance ‘Virtual Fraud in Financial Services’ event offered some fascinating insight into the risks facing the sector and how organisations are investing in advanced technologies to detect, prevent and tackle fraud.
An important and recurring theme was the role of data analytics in meeting the challenges presented by rising levels of fraud and the increasing sophistication of fraudsters. Bringing insight and experience to life for attendees, Mango Chief Data Scientist, Rich Pugh, was joined by Sandra Peaston, Director of Research & Development at Cifas to discuss their use of data and intelligence to support fraud prevention.
Cifas is the UK’s leading fraud prevention service, managing the largest database of instances of fraudulent conduct in the country. Its members are organisations from all sectors, sharing their data across to reduce instances of fraud and financial crime.
As a data-centric organisation, Cifas wanted to develop deeper insight into emerging fraud trends, understand which were the most significant and then quickly share that information with its members for further action.
Getting ahead of the game was key, and as Sandra Peaston described, “We wanted to use our data to speed up the early-stage intelligence process so our members didn’t need to report trends to us. Unlocking the power of the data we already hold was the challenge that took us to Mango.”
Having been approached by Cifas, Mango quickly deployed a team of data scientists to establish the right technical environment. As Rich Pugh explained, “The Cifas team has amassed some incredible data assets, but with many areas of potential focus the key question was: where could we deliver quick impacts against their priorities?”
The Mango project team focused on two core areas. The first was a ‘Match’ project, built to reduce false positive rates and improve the Cifas rules engine. This was supported via the development of a probabilistic matching engine prototype, designed to improve the existing matching and reduce member friction.
The second part of the solution was an ‘Intelligence’ project. This focused on the development of a fuzzy search capability and a signal detection tool to automate the previous manual fraud detection processes to uncover hidden and emerging fraud patterns. This insight would then be used to enrich intelligence and feedback to members.
As Sandra explained to event attendees, “We needed an intelligent way of dynamically identifying an emerging fraud trend, and key to this was the speed at which this happens. By working with Mango to uncover the huge power that sits within our data to a level of granularity that we couldn’t manage before, we can help members to prioritise and make them more efficient.”
Together, Cifas and Mango have deployed a best-practice framework using intelligence tools that demonstrably reveal hidden patterns that human beings would struggle to detect. Looking ahead, the teams will continue to innovate and use data science to unlock insight relating to fraud and e-crime, refining algorithms over time to become even more effective in countering criminal activity and finding ways to stay ahead of malicious actors.
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