Fraud detection Presentation

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Statistical data modeling Data preprocessing Matching algorithms Peer group outliers and covariance Time-series analysis

Economy & Finance

Fraud Detection
Hernan Huwyler
Madrid, Spain
Fraud Risk Forum
January 2013

Who detects fraud?

33%

18%
14% 13%
6%

• Fraud Risk
• Internal Audit Management
FRM • Suspicions
• Tipoff • By —chance” Transaction
Reporting
Controls

PwC's Global economic crime survey 2012

legitimate
record
legitimate
record

legitimate
record

fraudulent record
out of 9 system flags

Objective:
maximize correct predictions and maintain incorrect
predictions at an acceptable level

Data Analysis

Identify Analytics
Internal
Data

Apply Analytics to Data
Industry
Data

Leads Refine

Data Analysis 1

Branch A Branch C

Invigilation Branch B
HQ
Branch D

Business Branch A

BI
Branch C

Intiligence Branch B Branch D

Data Cleansing Algorithms

Poor initial data
conversion Factiva World Check
System
consolidations
World Compliance
Manual data entry

Interfaces and Customized data
baches cleansing rules

Demos
Vendors / Employees - Conflicts of interests
Vendor Activity – Sequentiality and fetching
Treasury – Unrecorded Payments
Treasury – Abnormalities and triangulations
Several sources of data
Combined attributes
Normalization

Some techniques
Statistical data modeling
Data preprocessing
Matching algorithms
Peer group outliers and covariance
Time-series analysis

Open discussions
Supervised, unsupervised & hybrid techniques
Quality data
Design, implementation and evaluation
Visualization tools
E-business transactions
Best practices

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Recognized as the industry leader in analytics and with more than 36 years of experi¬ence, SAS provides a framework of capabilities to help insurers significantly improve their fraud management processes. With SAS, you get: • A hybrid approach to fraud detection, including link analysis • Streamlined case management. Systematically facilitate investigations, and cap¬ture and display all pertinent information without corrupting the system with duplicate data entry. • Advanced text analytics and data mining.

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Regulations, compliance and overall risk management place a significant operational burden on financial services. Online lenders are no different. You have to comply with multiple regulatory requirements, and you are- like any other financial service- very susceptible to fraud. If you want to prevent and reduce loan application fraud, your strategy and fraud detection system should include a combination of identity verification, account onboarding protection, and account monitoring. In this post, we’ll explain how identity verification and Know Your Customer processes are related, and how you can expand them for better fraud coverage. We’ve also provided specific recommendations for identity verification security tests, and account origination protection strategies that can help you prevent fraud during the loan application process.

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Credit card plays a very vital role in todays economy and the usage of credit cards has dramatically increased. Credit card has become one of the most common method of payment for both online and offline as well as for regular purchases of a common man. It is very necessary to distinguish fraudulent credit card transactions by the credit card organizations so their clients are not charged for the purchases that they didn’t make. Despite the fact that using credit card gives huge benefits when used responsibly carefully and however significant credit and financial damages could be caused by fraudulent activities as well. Numerous methods have been proposed to stop these fraudulent activities. The project illustrates the model of a dataset to predict fraud transactions using machine learning. The model then detects if it is a fraudulent or a genuine transaction. The model also analyses and pre processes the dataset along with deployment of multiple anomaly detection using algorithms such as Local forest outlier and Isolation forest. Nikitha Pradeep | Dr. A Rengarajan "Credit Card Fraud Detection" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-4 , June 2021, URL: https://www.ijtsrd.compapers/ijtsrd41289.pdf Paper URL: https://www.ijtsrd.comcomputer-science/data-processing/41289/credit-card-fraud-detection/nikitha-pradeep

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Join Star One Credit Union, a financial institution with more than $9B in assets, as they discuss how they leverage their Guardian Analytics financial crime prevention platform to detect anomalies and suspicious activity in their online, mobile, and payments channels. In these slides, Margarete Mucker, EVP of Operations from Star One Credit Union, shares what her organization is doing to protect their members from fraud.

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Although the recent Equifax breach that reportedly compromised 143 million accounts has sent consumers scrambling to protect their assets, financial institutions and corporations are likely to feel the impending effects of this attack as well. It is predicted there will be a surge in social engineering and BEC scams, as the hackers now have a wealth of information needed to impersonate individuals and corporate entities in a variety of contexts. However, the right strategic approach and technology solutions can effectively mitigate the fraud risk. In this webinar, guest speaker Andras Cser, VP, Principal Analyst in Security & Risk from Forrester Research, will provide an expert industry perspective and discuss what financial institutions and corporations can do to protect themselves. You will gain insight into how organizations are raising their defenses, understand the best practices to minimize fraud loss, and learn how advanced analytics solutions can help minimize the risk.

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Accurate enterprise-wide data combined with data-driven fraud analytics can have a transformational effect on banking and related industries. This presentation provides tips and insights on using technologies like neural network predictive modeling, user behavior-based pattern recognition and statistical big data analytics to reduce the risk of fraudulent activities in the enterprise. For more information on CA Security solutions, please visit: http://bit.ly/10WHYDm

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With credit card fraud dramatically on the rise, particularly in the form of card-not-present (CNP) fraud across Internet and Mail Order/Telephone Order (MOTO) channels, it is important for private label issuers to understand the depth of this problem and how it affects their merchant portfolio and their ability to accept private label cards. Private label cards were often considered to be “low risk”, relative to traditional bank cards, but our current analysis has shown the contrary: fraudsters are increasingly using private label cards as the payment instrument in CNP channels and merchants are at great risk if specific strategies are not put in place to stop it.

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Dear Delegates, Corporate fraud costs businesses hundreds of millions of dollars each year. It affects livelihoods and is a common cause of corporate failure. It is the responsibility of the board of directors to prevent fraud by putting in places the appropriate controls and review procedures. This program shows you why Accounting Information System (AIS) Threats are ever increasing. Control risks have also increased in the last few years because there are computers and servers everywhere, and information is available to an unprecedented number of workers. Distributed computer networks make data available to many users, and these networks are harder to control than centralized mainframe systems. With the introduction of 3 levels of COSO and value driven ERM, things should be under control. Recent events at SATYAM proves that in reality things are getting out of control. So, what went wrong ? Is it time to train the auditors ? Recognising the challenges that organisations are facing in combating Fraud, CSI In Practice is pleased to present this 2-days Workshop on Enterprise Fraud Risk Management. This will serve as an excellent opportunity to learn how best to conduct an internal investigation to protect your organization and step up on controls to deter fraud.

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