Professor Nick Ryman-Tubb


AI/Machine Learning Impresario/Business Analytics Group
+44 (0)1483 684438
18 MS 02
Book appointment : https://doodle.com/poll/72bxdbmsa67z3nre

Biography

Areas of specialism

Fraud Detection and Management; Neural Networks / Deep Learning / Machine Learning; Data Science and Business Analytics; Practical deployment of intelligent systems; AI / Symbolic Systems; Payments and Banking systems; Industry and Business Collaboration

University roles and responsibilities

  • MSc Business Analytics (Machine Learning) - Teaching
  • MSc Data Science (Practical Business Analytics) - Teaching
  • Supervisor (MSc or PhD)

My qualifications

2016
PhD (Understanding Payment Card Fraud through Knowledge Extraction from Neural Networks using Large-Scale Datasets)
University of Surrey
2010
MSc - Computer Science (Explaining Payments Fraud)
City University, London.

Previous roles

2017
FITS is in the start-up phase and aims to become an independent, not for profit research institute to disrupt the way that financial fraud is managed and the how criminals abuse payment systems. It is modelled on the membership model of the 5G Innovation Centre at Surrey. My duties include financial and membership from government and large financial institutions and to promote research excellence through planned papers with A-journal publications. As a recognised expert in financial crime and AI/machine learning, I am regularly invited to industry conferences, e.g. with an audience of over 4,000 at Money2020 Europe in June 2017. FITS was founded by myself working with Prof. Paul Krause at Surrey, as the culmination of my 25+ years reducing financial fraud using advanced computational intelligence and business analytics. (www.fits.institute)
Financial Innovation in Transactions & Security (Institute)
2000
I founded MindsI to focus on my big data analytics algorithms using innovative and practical approaches to neural computing, evolutionary computation, neuro-symbolic, AI and other learning and optimisation techniques.
mindsI Analytics Limited
2010 - 2016
I joined the research team as a part time Director of Industrial Liason. I undertook my own research into neural computing and methods for knowledge extraction cumulating in a granted US patent.
Department of Computing, the University of Surrey
2012 - 2014
I completed a due diligence process working with an investor who acquired aiCorp in 2012. As CTO I worked to protect 150 banks, 3m+ merchants, 0.5bn cards and over 20bn payment card transactions a year from fraud working with well-known financial businesses such as Global Payments, Barclays, ING, Visa, Standard Chartered, FNB, Nedbank, RBS, RBC, Shell and China Construction Bank. I put in place a new agile development approach, substantial support improvements, grew the development team, met and worked closely with existing customers and recommended an innovation programme and new product roadmap. It became clear that the board and I had a different view on the ability of the recruited CEO to innovate or grow the business.
The ai Corporation Limited
2011 - 2014
I worked as Chairman with Thoughtified Limited that was founded in 2009, incubated by SETsquared, by two extremely bright computing students, Aaron Mason and Georgios Michalakidis. Thoughtified worked in advanced web development, pattern recognition, predictive analytics and visualisation of big data.
Thoughtified Limited
2008 - 2010
Appointed a Research Fellow and worked on my own research for a new approach to using neural networks and extraction for fraud detection applications for payment cards. See my paper "SOAR – Sparse Oracle-based Adaptive Rule Extraction: Knowledge extraction from large-scale datasets to detect credit card fraud", published by the IJCNN 2010.
City University, University of London
1987 - 2000
CEO/Founder. As CEO and founder positioned the firm to be a leading player in the risk scoring and fraud management industry. Neural offered two main software products that were based on a proprietary neural computing system created by myself called AMAN. Grew revenues at an average 40% p.a., with customers such as JP Morgan, Lloyds Bank, Standard & Poor's, Nationwide, Bradford & Bingley, British Gas, Orange, Vodafone, Telkom
Neural Technologies Limited

Research

Research interests

Research projects

Supervision

Postgraduate research supervision

Postgraduate research supervision

My teaching

Courses I teach on

Postgraduate taught

Postgraduate taught

Undergraduate

My publications

Highlights

Cyber-fraud is lucrative to criminals, with a low risk of being convicted. There is a growing number of data breaches where criminals target legitimate computer systems to obtain large quantities of sensitive data that contain sufficient information so that they can then be used in payment card and other forms of bank fraud.  My research over the last six years indicates that this situation will not change without changing the approach used to detect and manage fraud.  My recent research focuses on the use of neural networks and AI to detect payment card fraud.  I continue to work to bring a greater awareness of the issues and to promote strong research in collaboration with industry and the financial regulators.  The level of fraud has been increasing since the introduction of payment cards and continues to increase. Our research has demonstrated that this is due to the nature of the complex data available, out-dated approaches to detection and management and the current economic and regulatory framework: providers see their losses through fraud as an acceptable cost to business.  I believe that we can change that…

Publications

Ryman-Tubb, N.F. (2013). PATENT: Automatic rule discovery from large-scale datasets to detect payment card fraud using classifiers. U.S. Patent 8,543,522.
View abstract View full publication
A set of payment card transactions including a sparse set of fraudulent transactions is normalized, such that continuously valued literals in each of the set of transactions are transformed to discrete literals. The normalized transactions are used to train a classifier, such as a neural network, such that the classifier is trained to classify transactions as fraudulent or genuine. The fraudulent transactions in the set of payment card transactions are clustered to form a set of prototype transactions. Each of the discrete literals in each of the prototype transactions is expanded using sensitivity analysis using the trained classifier as an oracle, and a rule for identifying fraudulent transactions is generated for each prototype transaction based on the transaction's respective expanded literals.
N Ryman-Tubb (1993). A development path to success in neural computing. Expert Systems Applications, 9(5), pp.5-9.
Nick F Ryman-Tubb ; Artur d'Avila Garcez (2010). SOAR — Sparse Oracle-based Adaptive Rule extraction: Knowledge extraction from large-scale datasets to detect credit card fraud,in Neural Networks (IJCNN), The 2010 International Joint Conference on (pp. 1-9). IEEE.
View abstract View full publication
This paper presents a novel approach to knowledge extraction from large-scale datasets using a neural network when applied to the real-world problem of payment card fraud detection. Fraud is a serious and long term threat to a peaceful and democratic society. We present SOAR (Sparse Oracle-based Adaptive Rule) extraction, a practical approach to process large datasets and extract key generalizing rules that are comprehensible using a trained neural network as an oracle to locate key decision boundaries. Experimental results indicate a high level of rule comprehensibility with an acceptable level of accuracy can be achieved. The SOAR extraction outperformed the best decision tree induction method and produced over 10 times fewer rules aiding comprehensibility. Moreover, the extracted rules discovered fraud facts of key interest to industry fraud analysts.
Nick Ryman-Tubb (2000). Impact of e-commerce on credit scoring, Credit Control, 21, pp.11-14.
Eduardo Alonso, Nick Ryman-Tubb (2010). Computational Neuroscience for Advancing Artificial Intelligence: Models, Methods and Applications: Models, Methods and Applications. CHAPTER: Neural-Symbolic Processing in Business Applications : Credit Card Fraud Detection
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In recent years there has been increased interest in developing computational and mathematical models of learning and adaptation. Computational Neuroscience for Advancing Artificial Intelligence: Models, Methods and Applications captures the latest research in this area, providing a learning theorists with a mathematically sound framework within which evaluate their models. The significance of this book lies in its theoretical advances, which are grounded in an understanding of computational and biological learning. The approach taken moves the entire field closer to a watershed moment of learning models, through the interaction of computer science, psychology and neurobiology.
N Ryman-Tubb (1996). They all stink!: Chemometrics and the neural approach, Proc. SPIE Vol. 2878, Sixth, Seventh, and Eighth Workshops on Virtual Intelligence, Acadmic/Industrial/NASA/Defense Technical Interchange and Tutorials. International Conferences on Neural Networks, Fuzzy Systems, Evolutionary Computing, and Virtual Reality. Mary Lou Padgett and Thomas Lindblad, Eds., p.117
N Ryman-Tubb (1993). The use of neural networks to identify the characteristics of holiday-makers, The Journal of Database Marketing, 2(2), 140-149.
N Ryman-Tubb (1998). Combating Application Fraud,Credit Control, 19, pp.15-19.
View abstract View full publication
Fraudulent use of credit cards has long been recognised as a major problem for the financial industry. Since the early 1990s, companies have collaborated to implement systems to combat this kind of fraud. Many of the systems employed various forms of advanced technology including rule-based systems, pre-set to recognise specific indicators in data. Initially, this strategy worked and, as more fraudsters were caught, fraud declined. While traditional fraud detection systems are in a mature state, neural computing is an exciting technology, based on proven principles, that is continuing to rapidly evolve.
Nick F Ryman-Tubb, Paul Krause (2011). Neural network rule extraction to detect credit card fraud, In Engineering applications of neural networks (pp. 101-110). Springer, Berlin, Heidelberg.
View abstract View full publication
Neural networks have represented a serious barrier-to-entry in their application in automated fraud detection due to their black box and often proprietary nature which is overcome here by combining them with symbolic rule extraction. A Sparse Oracle-based Adaptive Rule extraction algorithm is used to produce comprehensible rules from a neural network to aid the detection of credit card fraud. In this paper, a method to improve this extraction algorithm is presented along with results from a large real-world European credit card data set. Through this application it is shown that neural networks can assist in mission-critical areas of business and are an important tool in the transparent detection of fraud.
N Ryman-Tubb (2000). An overview of credit scoring techniques, Credit Control, 21, pp.39-45.
N Ryman-Tubb (1995). Computers learn to smell and taste, Expert Systems, 12(2), pp.157-161.
View abstract View full publication
They all stink : food and drink, perfumery, household products, soaps, shampoos, paints, manufacturing processes, printing processes, waste products, contaminated air, and automotive emissions and environmental testing. In every case smell is a criterion of quality. Automated techniques to "smell" or "taste" liquids using mass spectroscopy and gas chromatography are time consuming, require skilled personnel and often do not give the information required for qualitative "tasting". A new technique is now available due to the advances in neural computing technology and multi-sensor array technology. The combination of these two approaches tries to simulate the human olfactory system in a simplified form. This paper shows that the recognition ability of an odour sensor array will be significantly improved using a neural computing approach in order to discriminate between similar odours.
N Ryman-Tubb (1992). Designing an electronic wine-taster using neural networks, ELECTRON. ENG., 64(783), pp.37-46.
N Ryman-Tubb (1999). Prime Time for Sub-Prime, Credit Management, 18-19.
N Ryman-Tubb (1994). Implementation-the only sensible route to wealth creating success: a range of applications, EPSRC: Information Technology Awareness in Engineering, London.
View abstract View full publication
The application of neural computing to help UK industry address some of their most pressing business needs has the potential ti improve its performance in a number of key areas, such as, business forecasting, marketing, quality control and innovative, world beating product development. The challenge to SERC (EPSRC) is to devise a strategy which will ensure that the foundational research work into the neural computing field continues while ensuring its eventual usability to industry.
Nick Ryman-Tubb (2016). Understanding payment card fraud through knowledge extraction from neural networks using large-scale datasets, (Doctoral dissertation, University of Surrey (United Kingdom))
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A novel approach to knowledge extraction from neural network classifiers when applied to payment card fraud detection is proposed. Existing Fraud Management Systems (FMS) use neural network classifiers but do not have the ability to explain their learnt patterns of fraud. Rule extraction from such classifiers with a high level of abstraction and linguistic simplicity is proposed. Decompositional knowledge extraction methods are found to be too reliant on the architecture of the fraud classifer and current pedagogical rule extraction methods produce rules that are not sufficiently comprehensible. In this thesis the Sparse Oracle-based Adaptive Rule (SOAR) pedagogical extraction algorithm is proposed to extract generalising rules that explain patterns of fraud. SOAR uses sensitivity analysis to avoid the exhaustive searches of other pedagogical methods. By projecting into discretised space, polytopes are formed by SOAR covering the class convex hull of the classifier surface. A methodological and verifiable empirical evaluation on publicly available datasets in various domains is undertaken. These results show that SOAR extracts comprehensible rules that are sound from a deep learning neural network. When SOAR is applied to large datasets provided by payment card issuers it discovered new fraud types that were of key interest to payment risk/fraud analysts. SOAR provides an improved understanding of fraud vectors that will lead to a more secure payment process through informed payment fraud prevention steps and this work could therefore alter how fraud management is undertaken in the future.
N Ryman-Tubb (1999). Credit risk analysis software makes e-commerce safer. Credit risk analysis software makes e-commerce safer. ABA Banking Journal, 91(11), p.54.
N Ryman-Tubb (1998). Brain Power - Using Neural Networks for Anomaly Detection and Explain Telecoms Fraud. Mobile Asia Pacific, pp.20-22.
View abstract View full publication
Most mobile telecoms operators are aware that fraud is a costly business. A single handset, for example, used for two international conference calls at US$2 per minute, could generate losses of around US$8,000 in just one day, increasing to $57,000 for a week. In one recorded incident, over $800.000 was lost to fraud using just five handsets over a weekend.Despite the many measures already being taken to combat fraud, it is on the increase. Newer operators are especially vulnerable 'due to the requirement (often by shareholders) to gain new customers rapidly, leaving little time for careful credit management and customer screening. A novel neural network approach is described that learns to detect patterns of potential fraud. Early criticisms, relating to the lack of explanatory information on how a neural computer performs its task, have now been overcome. Techniques can be used to identify which input variables have the largest effect on a particular decision or prediction. Furthermore, neural computers can now be structured to incorporate prior expert knowledge and present results in a form that is meaningful to human users. In the fraud detection application, neural 'models' are trained to perform anomaly detection, i.e. detection of unusual calls based on previously observed behaviour.
Ben Jeaps, Nick Ryman-Tubb (1993). The DTI at Wembley, Expert Systems, 10(3), pp.184-187.
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The briefing was introduced by Ray Browne, Project Manager at DTI, who announced a set of neural computing (NC) awareness products. In the spirit of the campaign, the awareness product set is designed to provide UK companies with comprehensive ‘hands on’information and advice needed to evaluate how to take commercial advantage of the technology. The set was developed by Touche Ross with additional expertise provided by NC suppliers and is geared towards both technicians and the business manager.
N Ryman-Tubb, G Bolt (1997). The thinking computer - Neural Process Control of the Copper Extraction Process. Mine and Quarry, 26(3), pp.36-9.
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The mining industry is increasingly using neural computing to maximise mineral recovery and to ensure that the cost of production never exceeds the revenue generated by sales of raw materials. This article describes how these systems work and presents a case study where NTL worked with Australian mining company, Straits Resources Ltd. to improve efficiency at their Girilambone copper mine in New South Wales. (Abstract quotes from original text). Gone are the days of computers which could only do what they were told by humans. The neural computer has changed all that. Neural means "of the nervous systems", and the biological comparison is no coincidence, Just like the human brain, neural computers can learn from experience and apply their knowledge...such a system has great potential in the mineral extraction industries,
N Ryman-Tubb (1993). Learning to pump. Manufacturing Engineer, 72(6), pp.268-271.
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The rules that govern many of the processes within the manufacturing industry can be hard to define and even harder to write down. Nick Ryman-Tubb explains how neural network technology can help.
N Ryman-Tubb (1997). Looking at Neural Networks in the Detection of Telecoms Fraud. Land Mobile, 1997
View abstract View full publication
Intelligent computing technologies promise to be a powerful weapon in the global battle against telecoms fraud. It is a sad fact that organized crime has realized that there is easy money to be made in telecommunications. Existing fraud management systems have provided only limited protection against fraud and are unable to curb this growth. Neural computers are an example of an alternative approach to tackling some of the fraud problems that other computing tools have difficulty coping with. They do not replace conventional techniques but they augment them. Inspired by the biological processes of the human brain, neural computing has many human-like qualities. Since a neural computer learns, it does not need to be programmed with fixed rules or equations. It provides a radical new way of solving complex problems.
N Ryman-Tubb (1996). They all stink!: Chemometrics and the Neural Approach. Sixth, Seventh, and Eighth Workshops on Virtual Intelligence, Acadmic/Industrial/NASA/Defense Technical Interchange and Tutorials, 1996
View abstract View full publication
They all stink : food and drink, perfumery, household products, soaps, shampoos, paints, manufacturing processes, printing processes, waste products, contaminated air, and automotive emissions and environmental testing. In every case smell is a criterion of quality. Automated techniques to "smell" or "taste" liquids using mass spectroscopy and gas chromatography are time consuming, require skilled personnel and often do not give the information required for qualitative "tasting". A new technique is now available due to the advances in neural computing technology and multi-sensor array technology. The combination of these two approaches tries to simulate the human olfactory system in a simplified form. This paper shows that the recognition ability of an odour sensor array will be significantly improved using a neural computing approach in order to discriminate between similar odours.
N Ryman-Tubb (1993). How Thomas Cook used their Neurals - Customer Product Selection Using Neural Networks. Direct Response, 1993
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Has your customer data got millions of records, each carrying multiple data fields.
N Ryman-Tubb, G DaNeil (1997). PATENT: Method for analysing data in the exploration for minerals, Patent WO1997034169 A1, 1997
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A method of locating a mineral deposit, the method comprising the steps of: (i) obtaining a plurality of data sets of indicators of physical or chemical conditions at a plurality of sites in an area known to contain the mineral, at least one of the indicators being an indicator of the mineral; (ii) training neural anomaly identifying means to identify anomalies in the data sets; (iii) obtaining a plurality of the indicators at a plurality of sites in a search area thought to contain the mineral; (iv) inputting the indicators obtained from the search area to the trained anomaly identifying means to obtain an indication of the location of the mineral deposit; and (v) visualising the indication.
N Ryman-Tubb, P Krause (2011). Neural Network Rule Extraction to Detect Credit Card Fraud. Engineering applications of neural networks (EANN), 2011
View abstract View full publication
Neural networks have represented a serious barrier-to-entry in their application in automated fraud detection due to their black box and often proprietary nature which is overcome here by combining them with symbolic rule extraction. A Sparse Oracle-based Adaptive Rule extraction algorithm is used to produce comprehensible rules from a neural network to aid the detection of credit card fraud. In this paper, a method to improve this extraction algorithm is presented along with results from a large real-world European credit card data set. Through this application it is shown that neural networks can assist in mission-critical areas of business and are an important tool in the transparent detection of fraud.
N Ryman-Tubb, P Krause, W Garn (2018). How Artificial Intelligence and machine learning research impacts payment card fraud detection: A survey and industry benchmark. Engineering Applications of Artificial Intelligence, 76, pp.130-157.
View abstract View full publication
The core goal of this paper is to identify guidance on how the  can better transition their research into payment card  towards a transformation away from the current unacceptable levels of payment card fraud. Payment card fraud is a serious and long-term threat to society (Ryman-Tubb and d’Avila Garcez, 2010) with an  forecast to be $416bn in 2017 (see Appendix A). The proceeds of this fraud are known to finance terrorism, arms and drug crime. Until recently the patterns of fraud () have slowly evolved and the criminals  (MO) has remained unsophisticated.  such as smartphones, mobile payments,  and contactless payments have emerged almost simultaneously with  breaches. This has led to a growth in new fraud vectors, so that the  for detection are becoming less effective. This in turn makes further research in this . In this context, a timely survey of published methods for payment card fraud detection is presented with the focus on methods that use AI and . The purpose of the survey is to consistently benchmark payment card fraud detection methods for industry using transactional volumes in 2017. This benchmark will show that only eight methods have a practical performance to be deployed in industry despite the body of research. The key challenges in the application of  and machine learning to fraud detection are discerned. Future directions are discussed and it is suggested that a cognitive computing approach is a promising  while encouraging industry data philanthropy.