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Fraud analytics

Fraud analytics involves using analytical tools and techniques coupled with human oversight to identify, detect, and prevent fraud within organizations. By integrating diverse data sources, it identifies patterns and anomalies that indicate potential fraud, which allows organizations to proactively manage risks. Effective fraud prevention combines various methods such as continuous analysis, machine learning, and predictive analytics to enhance detection capabilities and improve organizational performance.

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FRAUD ANALYTICS M S M 5 3 3 B U S I N E S S A N A L Y T I C S & D A T A G O V E R N A N C E Simran Mondal 1918107 Christ University Bangalore
1. INTRODUCTION 2. IMPORTANCE 3. FRAUD DETECTION & PREVENTION 4. STEPS 5. METHODS 6. TOOLS 7. CRITICISM 8. CASE STUDY 9. REFERENCES F R A U D A N A L Y T I C S PRESENTATION SUMMARY
Fraud Analytics is the process of integration of analytical tools and techniques with human interaction In other words, it involves gathering and storing relevant data and mining it for patterns, discrepancies, and anomalies. The findings are then translated into insights that can allow a company to manage potential threats before they occur as well as develop a proactive fraud and bribery detection environment. What is Fraud Analytics? 01 F R A U D A N A L Y T I C S “Organizations lose an estimated 5 percent of their annual revenue to fraud, according to a survey of fraud experts conducted by the Association of Certified Fraud Examiners.”
Why do we need Fraud Analytics? 02 Identify Hidden Patterns: Taking advantages of the digital fingerprints and tracing back to the fraud is one of the advantages of using a Fraud Analytics tool 1 2 3 4 5 Data Integration: Fraud analytics plays an important role in integrating data. It combines data from various sources and public records that can be integrated into a model. Enhance existing efforts: Fraud Analytics is one step ahead of the traditional data storage and retrieval mechanism by integrating data from various sources and files. Harnessing unstructured data: Fraud analytics helps in deriving the best value from unstructured data. Unstructured data is the place where more fraudulent activities take place. This is where text analytics plays an important role in reviewing the unstructured data and preventing fraud from taking place. Improve the performance: Fraud Analytics is a highly flexible and adaptable process which can be customized to the needs of your organisation
Fraud Detection & Fraud Prevention Fraud Detection Fraud Prevention It is the identification of actual or expected fraud to take place in an organization. Fraud detection in today’s world involves a comprehensive approach to match data points with activities to find what is abnormal ● Fraud detection occurs during the fraud attempt ● The goal of fraud detection is to mitigate fraud ● Sophisticated fraud detection solutions also reduce false positives which improves the user experience and increases the productivity of fraud teams It is the implementation of a strategy to detect fraudulent transactions or banking actions and prevent these actions from causing financial and reputational damage to the customer and financial institution ● Fraud prevention occurs before the fraud attempt ● The goal of fraud prevention is to reduce the risk of future fraud 03
Steps to create your Fraud Programme Create a profile that includes all the areas where fraud is expected to occur and the possible types of fraud in those areas. Measure the risk of fraud and the overall exposure to the organization. Prioritize the risks based on fraud. Follow Fraud testing method to find for indicators of fraud in particular areas of organization Establish risk assessment and decide where to pay closer attention Monitor the activity and communicate it throughout the organization so that employees in the organization are aware about the happening in the organization If there is any fraud found out, inform the management immediately to solve out the issue and to find out why it happened Fix any broken controls Expand the scope of the program and repeat the process 04
Methods of Fraud Analytics CONTINUOUS ANALYSIS 05 Ad-Hoc is nothing but finding out fraud by means of a hypothesis. It allows you to explore. Transactions are scanned in order to find any discrepancies or fraud It involves hypothesis testing and fraud investigation Sampling is the method of testing a sample for fraud rather than the whole population. More effective with larger population Its main disadvantage is that it may not be able to fully control the fraud detection as it takes only few population into consideration. It means creating and setting up scripts to run against big volume of data to identify the frauds as they occur over a period of time. It involves periodic notifications and real time detection of fraud. It helps to find out frauds that are not normal. Calculate Statistical parameters to find out values that exceed averages of standard deviation. Look at high and low values and find out the anomalies(indicators of fraud) there. Benford’s distribution is non- uniform with smaller digits more likely than the larger digits. Using Benford’s law you can test certain points and numbers and identify those which appear frequently than they are supposed to and therefore they are the suspect. SAMPLING 2. AD- HOC 3.REPETITIVE/ CONTINUOUS ANALYTICS 4. ANALYTICAL TECHNIQUES 5. BENFORD’S LAW
Effective tools to minimize fraud based activities Data analytics & similar technologies are used to help organizations to detect & prevent fraud based activities Fraud analytics is an umbrella term covering a lot of technologies 1. Business Intelligence: In the fraud management space, BI can be thought of as a descriptive performance reporter. It summarizes available data to provide business dashboards and insights to business leaders and fraud managers so they can make more informed decisions. A. Management Information: MI relates to creating executive dashboards, data visualisation, data storytelling and any other reporting methods. B. Data Warehousing: A data warehouse is a large collection of business data used to help an organization make decisions. 06
2. Data Science: Data science relates to a set of more sophisticated technologies for performing predictive and prescriptive analytics Predictive analytics is focused on making predictions about the future of unknown events (or, in the case of fraud, current events outcomes). Prescriptive analytics relates to choosing the optimal course of action based on the outcome of those predictions. A. Artificial Intelligence: AI relates to the computer implementation of human thought processes in a computerized and efficient fashion. 1. Machine Learning: Machine learning is a subset of AI that relates to the science of algorithms. Machine learning is a set of numerous algorithmic techniques can be used to extract complex relationships in data which a human could not find. 2. Deep Learning: Deep learning is a class of machine learning algorithms focused specifically on building “deep” (multi- layered) neural networks, a form of AI widely used in fraud detection. Fraud analytics can be defined as a multidisciplinary field that combines numerous quantitative sciences in order to better understand fraud. 07
08 How Data Analytics Can Assist in Fraud Detection It can handle massive amount of data It can help assess and improve internal controls It reduces sampling errors. It helps to revise or reinforce policies It can be used reactively or proactively It can track trends and possible problems substantially faster than people could, without help Data analytics in Fraud detection refers to the use of analytics software to identify trends, patterns, spotting anomalies, and exceptions in data. ● Applying fraud investigation skills to the data analysis results can help identify potential instance for fraud. ● Data analysis techniques alone are unlikely to detect fraud; human judgment is needed to decipher results.
Challenges Faced by Fraud Analytics 1. Changing Fraud Patterns overtime: fraudsters are always looking to find new and innovative ways to get around the systems to commit fraud. Hence it is important to update with the evolved patterns to detect. This results in a decrease in the model’s performance and efficiency. 2. Class Imbalance: An imbalance in the classification of fraud detection models (that usually classify transactions as either fraudulent or non-fraudulent) which makes it harder to build them. The fallout of this challenge is a poor user experience for genuine customers, since catching the fraudsters usually involves declining some legitimate transactions. 3. Model Interpretations: The models only give a score whether a transaction is fraudulent or not, without giving any explanation. 4. Quality of the Data: The results from analytics tests can only be as good as the data we feed in as input. 09
Case Study: How Fraud Analytics was instrumental in curbing airline loyalty fraud Airline travel miles are a coveted loyalty benefit, a gateway for customers to visit places new and old. To an unscrupulous ticket agent, travel rewards present a good-as-gold opportunity to fraudulently issue miles and then redeem them to book flights for personal and friends’ use. One major airline recently introduced data analytics into its fraud risk management processes to identify anomalies, patterns, and trends signalling the potential for fraudulent activity. The analysis honed in on a variety of data elements, such as number of air miles awarded to customers and agents, flights booked using air miles, dates of awards, dates of travel, and more. Analysis of these data elements can produce key indicators of potential fraud, such as: ● Anomaly detection: Were excessive air miles awarded by a single agent or to a single rewards account? ● Predictive classification: Are fraudulently awarded air miles being used to book particular flights? ● Clustering: Are there commonalities in the miles accruing to a rewards account, i.e., the same number of miles every Wednesday, or the same approving manager sanctioning the awards? ● By leveraging a variety of analytics models: and by testing hypotheses through analysis of combined datasets, the airline detected fraudulent activity earlier. Further, by carefully considering the requirements to operationalize fraud risk analytics, and inventorying current tools and technologies, the company determined that much of what it needed to perform the analytics was already in place, thereby substantially reducing upfront technology investments. 10
References 1. https://www.educba.com/fraud-detection-analytics/ 2. https://www2.deloitte.com/us/en/pages/advisory/articles/five-insights-into-fraud-risk-analytics.html 3. https://searchsecurity.techtarget.com/definition/fraud- detection#:~:text=Fraud%20detection%20is%20a%20set,or%20using%20stolen%20credit%20cards. 4. https://www.sas.com/en_in/insights/articles/risk-fraud/fraud-detection-machine-learning.html 5. https://www.crisil.com/en/home/our-businesses/grna/risk-and-analytics/financial-crime-analytics-capabilities/fraud- detection-and-analytics.html 6. https://www.rd-alliance.org/group/big-data-ig-data-development-ig/wiki/big-data-definition-importance-examples- tools#:~:text=Big%20data%20is%20a%20term,amount%20of%20data%20that's%20important.&text=Big%20data%20ca n%20be%20analyzed,decisions%20and%20strategic%20business%20moves. 7. https://www2.deloitte.com/content/dam/Deloitte/tr/Documents/deloitte-analytics/tr-fraud-analytics.pdf 8. https://www.fico.com/blogs/search?keywords%5B%5D=fraud 9. https://www.fico.com/blogs/controlling-obvious-and-hidden-fraud-threats-cristiano-ronaldo-syndrome 10.https://www.crisil.com/en/home/our-businesses/grna/risk-and-analytics/financial-crime-analytics-capabilities/fraud- detection-and-analytics.html