The Application of Data Science in Fraud Detection and Prevention

Fraud can take many different forms and affect all sectors, although the extent of the damage varies from one industry to another. There are various measures and tactics that are used by the industries that commonly deal with fraud detection to stop it. The most crucial thing industries must accomplish is finding out the root cause of the fraud. 

Security professionals are on heightened alert because of the scammers' growing sophistication and refinement. Many organisations are now more vulnerable than ever to criminal activity involving data breaches and misuse due to the growing complexity of storing and managing business data. Scammers search for and use vulnerabilities in the infrastructure and IT systems now more than ever.

Manual checks for suspicious activity are simply insufficient when fraudulent attempts are too hard to identify in high volumes. Thankfully, systems that offer automatic controls significantly reduce the amount of human engagement necessary to combat fraud. The best method for identifying the underlying causes of frequent workplace fraud is Data Science. Data Scientists use methods and technologies that are combined in fraud analytics to assist spot possibly fraudulent transactions.

The ability to process vast amounts of data at once is the main advantage of employing Data Science and Data Analytics for fraud detection by Data Analysts. The information will surely help identify the areas that are most susceptible to fraud and how to effectively combat it. People can track trends and potential issues with data analytics far more quickly than they could without the aid of any technology tool.

Some of the major industries and areas where Data Science can be used to detect fraud include Taxation, the Banking industry, the Pharmaceutical industry, Cyber fraud and the Finance industry. 

• Data Science for Fraud Detection in Taxation: For many people, completing tax returns can be stressful. Some people worry about making arithmetic mistakes, while others worry about filing fraudulent returns. Both might lead to an audit. There is no denying that fraudulent refunds put more of a strain on the government and law-abiding taxpayers.
• Data Science for Fraud Detection in the Pharmaceuticals Industry: One of the most important industries for the majority of people is the medical industry. Fraud occurs when a pharmaceutical corporation overcharges a particular medication. These con games frequently include the government as well. By comparing the approval times for comparable generic pharmaceuticals to those for a drug that is pending approval, data analytics can be used to help. Looking at the data gathered, also helps in the detection of pharmacy refill fraud.
• Data Science for Fraud Detection in Banking Industry: Both Data Science and Data Analytics are used by financial institutions like banks to find and stop fraud. All client and bank communications are recorded by data analytics. Because of this, fraud can be easily identified and stopped before it harms the brand's reputation. All talks and activities that take place in the bank on a regular basis are constantly being recorded by the bank using data analytics. Data Science and Data Analytics both are perfect tools for identifying any illegalities occurring across all time zones and promptly responding to the wrongdoing, thereby somewhat lowering fraud. 
• Data Science for Fraud Detection in Cyber Space: Despite using a variety of methods and technologies, fraudsters leave a trail of transactional and behavioural data that helps with cyber fraud detection. These models gather data from documents like emails, exchanges on social media, contact centre notes, or agent reports. This helps follow changing patterns and spot new scams as they appear.
• Data Science for Detecting Fraud in Financial Industries: Since the introduction of digitalization, there have been various financial frauds. The extent and nature of financial fraud continue to change, despite the fact that financial institutions and banks have employed a number of measures to detect and counteract fraudulent activities. Data Science has made it possible to identify and stop fraud, using methods like behavioural analysis and real-time detection. Financial institutions may better comprehend suspicious activity, spot patterns, and spot out-of-the-ordinary transactions thanks to real-time analytics, most of which will undoubtedly aid in preventing fraud before it happens.

Software for detecting fraud on websites keeps an eye on, looks into, and stops it. There are several Softwares that are used frequently to stop fraudulent transactions that use stolen credit card data. Businesses can also verify user IDs during signup and login using fraud detection techniques and software. 

Some of the key techniques that are applied for higher efficiency are listed below. 

Statistical Techniques

• Methods for detecting, validating, fixing errors, and filling blanks in missing or inaccurate data.
• Computation of a variety of statistical variables, including averages, quintiles, performance measures, probability distributions, and so forth. Examples of standards include average call volume, average call duration, and average bill payment delays.
• Models and probability distributions for a range of business activities, either in terms of a range of parameters or probabilities.
• The creation of user profiles.
• Examination of time series involving time-dependent data.
• To look for patterns and correlations between knowledge groupings, use clustering and classification
• Data matching - To match two sets of gathered data, data matching is used. The procedure is frequently carried out using loops or supported algorithms. attempting to match up knowledge sets or contrasting intricate data types. To eliminate duplicate records and find connections between two data sets for marketing, security, or other purposes, data matching is used.
• Sounds like - In order to find values that sound similar, functions are used. Finding potential duplicate values or inconsistent spelling in manually entered data can be done using the phonetic similarity technique. The ‘sounds like’ function converts the comparison strings to four-character American Soundex codes, which are supported by the primary letter, and thus the first three consonants after the primary letter, in each string.
• Regression analysis allows you to look at the connection between two or more variables of interest. Regression analysis calculates the relationships between a variable and its independent variables. This method is often wont to help understand and identify relationships among variables and predict actual results.

Artificial Intelligence Techniques: Some of the major Artificial Intelligence techniques used for fraud detection are,

• Smart systems to encode expertise for detecting fraud in the form of rules.
• Data Processing is used to group, classify, and segment the data, as well as automatically find patterns that will imply significant trends, particularly those associated with fraud
• Pattern recognition can be used to match inputs or automatically find approximative classes, clusters, or patterns of suspect behaviour.
• ML methods to automatically recognise fraud characteristics.
• Using neural networks, classification, clustering, generalisation, and forecasting will be generated independently. These results will then be compared to findings from internal audits or official financial documents like the 10-Q.

Machine Learning and Data Mining

The goal of earlier data analysis methods was to extract quantitative and statistical data properties. These methods make it easier to interpret data in a useful way and may encourage a deeper understanding of the mechanisms behind the data. While a knowledge analysis system that is used today possesses a significant quantity of background information and is prepared to complete tasks using that knowledge and, consequently, the supplied data in order to advance. Researchers have looked at concepts from the field of machine learning in an effort to achieve this goal. 

Data becomes information when it is processed to reveal significant patterns. Correct and accurate, and possibly helpful facts or patterns aren't just information; they're knowledge. The applications of AI and machine learning could potentially be divided into two groups: "supervised" and "unsupervised" learning.

According to the approach, these strategies look for accounts, customers, suppliers, etc. that act "unusually" in order to produce suspicion ratings, rules, or visual abnormalities.

Supervised Learning: Under supervised learning, all records are randomly sampled and manually categorised as “fraudulent” or “non-fraudulent”. To encourage a suitably large sample size, it may be necessary to oversample relatively uncommon occurrences like a fraud. These manually categorised records will then not train supervised machine learning systems. The algorithm should be prepared to categorise the new record as fraudulent or not after using this training data to create a model.

To detect fraud and budget fraud in the telephone network, it has extensively researched supervised neural networks, fuzzy neural networks, and combinations of neural networks and rules.

The detection of Mastercard fraud, telecom fraud, auto claim fraud, and health insurance fraud all use Bayesian learning neural networks.

Unsupervised Learning: It is necessary to mention some significant unsupervised learning research on fraud detection. For example, Bolton and Hand apply Peer Group Analysis and Break Point Analysis to the spending behaviour of credit card accounts. Peer-to-peer analysis can detect individual objects that begin to behave in a different manner than before. Another approach developed by Bolton and Hand for pattern fraud detection is “breakpoint analysis.”

Unlike Maverick analysis, breakpoint analysis operates at the account level. Breakpoints are observations in which abnormal behaviour of a particular account is observed. Both of these tools are suitable for consumer behaviour in MasterCard accounts.

Thus, some of the key benefits of the integration of Data Science in Fraud detection are listed below, 

• Receive prompt responses to a number of inquiries about fraud issues
• Predetermined flow for automatic data collecting
• Complete and quick access to all data through data indexing software (a method of grouping records according to several criteria)
• Eliminates double records, and errors, improving the quality of data
• High productivity vs. manual work
• Using inaccurate and incomplete data
• Creating a positive yield and fast return on investment
• An increased rate of fraud detection
• Quick detection and recovery of consequences of fraud activity

It is not advised to spend too much time searching for the ideal solution because there is no toolkit that can assist in beginning business fraud detection. One can just start fighting the fraud, using paid or unpaid software, a combination of statistical, data visualization, data mining, and filtering tools. The process of data analysis as a tool for preventing and detecting fraud can be used successfully in any field, especially in those where databases are, or, may be easily converted into electronic format.