“Disruptive Approaches Are Driving Change” Derek Dempsey On The Fraud Industry: Part Two

Rosalind Madge our consultant managing the role
Posting date: 2/28/2019 9:40 AM




This is the second part of our interview with Derek Dempsey, Fraud Analytics Director for FICO. To read the first part click here.


How have Data & Analytics impacted the detection, and prevention, of Fraud? 

Big Data is interesting. We want to use as much Data as possible but you have to be sure the Data you have is reliable and robust. Bad Data could lead to significant issues for anyone in Fraud detection. 

But really, Data & Analytics are the fuel that drives effective Fraud detection. Our world has used these tools very effectively for many years and we have always been at the forefront in the use of new techniques. Effective Fraud detection relies heavily on using the best Data available and the right analytical techniques to extract useful information. Furthermore, it is constantly evolving as more information becomes available. 

What impact do you think Machine Learning and AI can have on Fraud prevention and detection? 

In the last few years we’ve seen an explosion of new interest and new techniques, with lots of new players coming into the market. 

Companies like FICO have been using Machine Learning for Fraud detection for 25 years so this isn't new. What is new is a whole generation of new technologies, new companies and disruptive approaches that have been driving change. 

For us, apart from increased competitive pressures, one of the other big changes is that companies have built large Data Science teams and have a bigger appetite to build their own solutions using open source technologies. However, it’s one thing building these great models, it’s another getting them to operate effectively and correctly in the real world. The level of governance that we are subject to is enormous just to make sure that our models perform as they should. This will be a challenge to the newcomers. They are here to stay though, and should drive better performance and better Fraud detection. 

Where AI techniques are set to make an impact is in AML and compliance monitoring. These have used rule-based techniques due to regulatory pressures, but it is clear that more advanced techniques are required to provide better detection of money laundering and terrorist financing. However, businesses do require us to provide explainability but regulators are saying the will look favourably on AI usage if it can provide this. The more AI techniques are used, the more this issue of explainability is going to be important. 

Why is the development of analytics, tools and techniques so important within different industries?

I think domain knowledge of the Data and business will always be important. 'Specialisms' occur and always will. The analytic techniques, the tools and languages can be standard although some are more appropriate for different specialities. However, the differences in Data and business model always results in specialised applications to address this. 

For example, something like Card Fraud or AML require techniques that can analyse and process huge volumes of transactions, whereas something like Claims fraud or Application Fraud won't have this requirement and other factors can be more relevant. However, there is little doubt that Financial Services and Telco have actively sought AI technologies while others have been more fearful of so-called 'black box' approaches. 

How are Big Data and Data Science tools, such as Hadoop, helping combat Fraud effectively? 

There’s no question from an analytics perspective that Python and R are the two languages that Data Scientists are using. But it helps to have specialists in technical skills, such as DevOps and DataOps, to provide the technical expertise that allows them to build models and utilise Data most effectively. 

As for Hadoop, it makes sense for Data Scientists to have an understanding, but ultimately I see that skillset as one belonging to the technical specialists. This is why I firmly believe that every Data Science team should have a supporting tech function.  

What are the latest technologies helping combat Fraud? 

From an analytical perspective there has been a recent focus on graph analytics and network analytics as these can be applied to external Data. These approaches have been around for a while however and are limited to certain types of Fraud. We’re also seeing more unsupervised techniques being used as these do not rely on prior fraud case data so can be applied to a wider set of cases. 

Another new area has been adaptivity. This is a model that adapts over time depending on operational feedback from the analysts. Again this is not new but you really need to balance the impact of this new information compared to how the model is currently working and so is very challenging. As long as you can maintain a sufficient degree of explainability you can ensure the process is sufficiently well governed. 

There has been a significant move to cloud-based technologies where companies can reduce their implementation and maintenance costs. We are just about to release our new Falcon X platform, which is a cloud-based fraud platform, that will allow clients to use all the FICO capabilities but also allow them to develop their own analytics as well. 

I think potentially the biggest change will be the progressive adoption of biometric authentication. SCA is a requirement for all high-value transactions in PSD2 and this requires two-factor authentication from inherence, ownership and knowledge: so something you are, something you have or something you know. I think biometrics will start to play a big role in authentication and, hopefully, will provide much greater identity security. 

Another trend I think we may see is the growth of digital IDs. There is already a well-advanced program in the Nordics called BankID and the concept of a digital ID seems inevitable at some point. 

Derek spoke to Senior Consultant, Rosalind Madge. Get in touch with Rosalind or take a look at our latest job opportunities here.

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