1. Capitalising on Analytics as a Security Measure
Payments Iran 2016
Author: Suzana Stojaković – Čelustka, PhD

2. Contents
Utilizing predictive analytics to anticipate and combat fraud in a digital world
Rolling out data mining and predictive modelling to proactively manage risk
Preventing and predicting fraud in real time without affecting the experience of the customer
Analyzing past failures of data to forecast and handle uncertainty
Developing analytics to uncover the true identity of individuals and groups
Diminishing the chance of data breaches with intelligent analytics

3. 1. Utilizing predictive analytics to anticipate and combat fraud in a digital world

4. Introduction Definition:
Payment fraud is any type of false or illegal transaction completed by a cybercriminal. The perpetrator deprives the victim of funds, personal property, interest or sensitive information via the Internet.
These days fraud goes international. This fraud is perpetrated by international fraudsters who understand how to work the payment system to their advantage, at the detriment to the business.
The aim is to extort money from the business and they have many elaborate scams associated with their international fraud activities.

5. Problems with Fraud in Digital World
Fraud attacks often do not come from where one would expect!
Fraud patterns evolve quickly and constantly
As companies put in place measures to prevent fraud, perpetrators quickly adapt and find ways to circumvent them.

6. Analyzing Fraud Patterns
Identifying fraud patterns means to find out:
Where fraud comes from
How it happens
Who is involved
What areas of the business it impacts

7. Need for Predictive Analytics to Combat Fraud
Critical information needed to detect fraud patterns is often buried in very high and fast-growing volumes of data
Common analytical tools appear ineffective to scan fast such a huge amount of data
Predictive analytics enhances capabilities to detect fraud, but also contributes to better prevention of potential future fraud.

8. 2. Rolling out data mining and predictive modelling to proactively manage risk

9. Investigating Data about Fraud
The range of data to examine in order to properly identify fraud trends is increasingly diverse.
The data are structured and unstructured.
Fraud detection is Big Data problem!
To proactively manage risk of fraud we use data mining techniques to obtain useful data and then we use predictive modeling to understand obtained data.

10. Data Mining vs Predictive Analytics
Data mining deals with structured data (statistical methods) – investigating data
Predictive analytics deals with structured and unstructured data – gives decision rules based on mathematical models

11. Data Mining Methodology
Cross Industry Standard Process for Data Mining
CRISP – DM Methodology
First 3 steps are 80% of solving the problem

12. Supervised and Unsupervised Learning
There exist history records with known patterns of fraud
Analysis consists of finding similarities to known recorded patterns
Disadvantage – doesn’t recognize unknown patterns
Unsupervised learning
Searches for behavioral patterns which may lead to fraud actions (heuristics)
Advantage – can recognize new patterns of fraud

13. Data Mining Techniques
Objective: To obtain meaningful data for predictive models
Technique
Usage
Algorithms
Classification (or Prediction)
Predict group membership or a number (Example: recognizing known patterns of fraud)
(Supervised learning)
Auto classifiers, Decision trees, Logistic, SVM, Time series, etc.
Segmentation
Classify data points into groups that are internally homogenous and externally heterogeneous
Identify cases that are unusual (Example: searching for new patterns of fraud)
(Unsupervised learning)
Auto clustering, K-means, etc.
Anomaly detection
Association
Finds events that occur together or in sequence (Example: searching for new patterns of fraud)
APRIORI, Carma, Sequence

14. Predictive Models
Objective: To find useful relationships in large data sets
Technique
Models used
Classification
Rule induction models
Traditional statistical models
Machine learning models
Segmentation
K-means model
Kohonen model
Two-step model
Auto cluster node to automate the analysis
Association
APRIORI models
Carma models

15. 3. Preventing and predicting fraud in real time without affecting the experience of the customer

16. Need for an Intelligent System
To prevent and predict fraud in real time without affecting the experience of customer we need an intelligent system capable to:
learn or understand from experience
acquire and retain knowledge
respond quickly and successfully to a new situation
make proper decisions, etc.

17. Intelligent System Concept
KNOWLEDGE BASE
(ANALYTICS, MODELS, INFERENCE RULES)
INFERENCE ENGINE (REASONING, DECISION MAKING)
KNOWLEDGE AQUISITION SUBSYSTEM
ADAPTATION SUBSYSTEM
OBSERVING ELEMENTS
EXECUTIVE ELEMENTS
ORGANIZATION LEVEL
COORDINATION LEVEL
EXECUTION
LEVEL

18. How It Works Part of IS Level Function Needed capabilities
Observing elements
Executive
Collect data
Speed, Accuracy
Knowledge acquisition subsystem
Coordination
Data mining, preparing meaningful data for the next level
Speed, Reliability, Obtaining „clean data”
Knowledge base
Organization
Predictive modelling and analytics, stores inference rules
Algorithmic diversity, Ability to deal with data complexity, Precision
Inference engine
Select inference rules to perform
Cognitive abilities
Adaptation subsystem
Adapt and transfer inference rules to execution elements
Executive elements
Execute rules

19. Needed Characteristics of an Intelligent System
An intelligent system for fraud detection and prevention has to be:
„Invisible” to customer
Fast
Reliable
Precise (small amount of false positives or negatives)
Small
Adaptive
Distributed

20. 4. Analyzing past failures of data to forecast and handle uncertainty

21. Reliability of an Intelligent System
An intelligent system has to be reliable and to have ability to learn from past experiences and past failures.
This is characteristic required for both knowledge database (predictive models and analysis) and data acquisition subsystem (data mining techniques)
Even the best predictive models can fail due to low quality of data and uncertainty contained in collected data!
Data is typically far from complete, frequently ambiguous, and often scattered over many different data sources.

22. Entity Analytics for Improving Reliability of Data
Whereas predictive analytics attempts to predict future behavior from past data, entity analytics focuses on improving the coherence and consistency of current data by resolving identity conflicts within the records themselves.
An identity can be that of an individual, an organization, an object or any other entity for which ambiguity might exist.
Identity resolution can be vital in fraud detection!

23. Learning Capabilities of an Intelligent System
AI (Artificial Intelligence) techniques as e.g. Machine Learning (ML)
The process of machine learning is similar to that of data mining. Both systems search through data to look for patterns. However, instead of extracting data for human comprehension - as is the case in data mining - machine learning uses that data to detect patterns in data and adjust program actions accordingly.
Machine learning algorithms are often categorized as being supervised or unsupervised. Supervised algorithms can apply what has been learned in the past to new data. Unsupervised algorithms can draw inferences from datasets.

24. 5. Developing analytics to uncover the true identity of individuals and groups

25. Entity Analytics in Fraud Detection
Characteristic
Predictive Analytics
Entity Analytics
Fraud detection
Records flagged as potentially fraudulent if they have typical characteristics of fraudulent action
Records flagged as potentially fraudulent if related to known fraudulent records, or if originating from same individuals but with different identities.

26. How It Works
One of the more important data preparation activities involves recognizing when multiple references to the same entity are the same entity (within the same and across data sources).
For example, it is essential to understand the difference between three transactions carried out by three people versus one person who carried out all three transactions.
After determining when entities are the same (resolved), even deeper understanding is achieved by recognizing when these resolved entities are related to each other (such as sharing a home address).

27. 6. Diminishing the chance of data breaches with intelligent analytics

28. Benefits
Today’s best fraud management and intelligent analytics solutions have many benefits. They:
Identify fraud patterns and trends more precisely
Enable going after the less known and more complex patterns and networks, and detecting earlier to minimize the damage of cleverly hidden suspicious transactions.
Provide the needed capabilities to analyze a wide variety and very high volume of data very fast, leveraging in-memory computing technology.
Help fraud investigators by reducing false alerts resulting from inadequate fraud detection mechanisms

29. Can Intelligent Aanalytics Bbenefit a Wider Audience?
The innovation brought by predictive and intelligent analytics touches many other areas of the business, and in areas such as governance, risk and compliance (GRC), their use will develop to enable better predictability of risk, increased insight in areas of control weakness, support for internal audit programs, and so on.
These multiple applications create a high demand for experts such as data analysts and specialized business analysts.
It is important that new predictive technologies become approachable also for the non-experts, and more readily consumable by their most interested audience.

30. Needed Levels of Expertise with Growing Complexity of Analytic Tools

31. Conclusions
The combination of traditional fraud management solutions complemented by predictive and intelligent analytics not only enhances capabilities to detect fraud, but also contributes to better prevention of potential future fraud.
It enables a deeper, more forensic approach against fraud, helping users to improve the effectiveness of their investigations by better focusing on new types of fraud risks, and continuously updating and refining their fraud detection strategies using the data from predictive analyses.

32. Questions?

33. Thank you!