11 Oct 2022
How Data Science Will Mitigate Credit Card Fraud
Works at Ekeeda
Credit card fraud or cybercriminal activities are a never-ending process; no matter whether you implement an end number of & high-end ultra-modern technologies to counter them. Malicious activities are increasing at breakneck speed with easy passing day; even though there is the rise of cutting-edge technology, as it becomes easier to get access to credit card details.
You fill up an online form or at the shopping mall, you book an airline ticket or make a purchase on Myntra or Flipkart, and your credit card details are immediately fetched by the system, which is likely to be hacked at some point or the other.
As per RBI statistics, 7.68 crore credit card users spent around ₹71,429 crore in May 2022 on online purchases. While the amount paid through swipes at Point of Sale (PoS) machines was ₹42,266 crores; the credit card spending surpassed 1.13 lakh crore in May from ₹ 1.05 lakh crore in April 2022.
But with this, counterfeit transactions are on a steep rise, as most credit card companies are striving to find a robust solution to credit card fraud issues. Many credit card firms want to draw fraudulent financial transactions to track financial losses and fraudulent activities. To put an end to such criminal activities and mitigate the financial loss of billions of rupees, several credit card companies & banks combined forces to leverage big data tech along with data science techniques at their best to fight credit card fraud. Before we go into how big data and data science can help evade credit card fraud let's understand the basics.
In order to monitor systems against fraudulent activities – businesses, entities, and organizations believe specialized data analytics techniques like data mining, data matching, and sounds like function, Regression analysis, Clustering analysis, and Gap. Data analytics and data science technology won’t replace the need for humans, who scrutinize the content & findings but will track the trends and possible problems substantially faster than people could without help.
Some Shady Activities On Your Credit Card
Credit card owner makes a transaction from a device for the first time
Number of transactions that take place from different devices within a day
Two transactions from the same credit card occur in different cities within a short period of time
The usual spending amount/month is largely exceeded
A sudden big-ticket buying is done
Our blog will help you understand credit card fraud and how data science will play an important role in detecting fraud & mitigating the risk associated with it.
What We Will Cover In?
Introduction to Credit Card Fraud
Big Data: A Blessing For Credit Card Firms
How Big Data Tech will help Identify Credit Card Fraud
The Obstacles Faced By Credit Card Firms
Efficient Ways to Reduce Fraud Activities
The Bottom Line
Are you curious to know more about Data Science? Read: Data Science Skills, Education and Best Job Roles For Beginners
What Is A Credit Card Fraud?
To put it simply, credit card fraud means using credit cards or debit cards without any authorization, possibly when lost or stolen, with malicious intentions to use the fund limits of the card holders. And several people could be victims of fraudsters like: Card issuers, Cardholders, payment gateway service providers, Banks, credit card payment systems, vendors, payment processing companies, etc.
Hope you’re now clear on how credit card fraud will affect customers, card issuers, and merchants since the economic concepts go on toss due to the cost of illicitly bought merchandise. Companies spend millions to secure themselves from such frauds and scams. Best of the technologies, software and tools are kept in the arsenal to use against such frauds. But you need a human being equipped with the right skills to fight against it. These humans are termed as ‘Data Scientists’, who will maintain the voluminous amount of data, and work on things like data processing, data mining, and visualization to come up with valuable insights on any fraud activity data. Data science skills will play the utmost important role in determining potential frauds on credit/debit cards.
Big Data: A Blessing for Credit Card Firms
In prior days, credit card firms would detect credit card scams by bringing attention to suspicious transactions and summoning investigators to examine & review every transaction meticulously. The process would include users calling and even calling up to interrogate them about their verification details under the data protection act. It was indeed a time-consuming and tedious cost and would incur inventory costs for companies on calling, and utilizing manpower to personally visit the cardholder's address to verify it. The number of credit card frauds is increasing every year at a staggering rate of 8-9% by volume between 2021-22, showing that things were not so much effective as they seemed. This calls for advanced techs such as Big data and data science techniques to help users & other players like card holders, banks, payment gateway providers, etc., combat credit card fraud.
As we enter the modern-day technological sound world, credit card companies have started to utilize Big data techniques to grab malicious transactions in real-time and thus prevent incurring big losses; thereby no need to wait for users’ authentication.
Before this, credit card companies would use standard customer transactions to train their Machine Learning Algorithms. Once the users’ typical transaction patterns were discovered the algorithm would then forecast the probabilities of the specific transactions – if it's genuine or not. Companies would adjust particular limits on this, and if the transaction was more than the assigned value, it would get rejected. Although the basic remains the same in today’s modern world of data tracking by data scientists such as consumer buying habits, frequent credit card usage, instruments use for transactions, location, time, IP address locations, etc. So, if the particular user account displays various IP addresses across the globe at the same time, it would indicate that the account has been hacked. And the credit card firm will quickly resort to big data algorithms to handle the fraud situation with the loss prevention division. To get in-depth knowledge of Data Science, you can sign up for Ekeeda Data Science Program with 1:1 mentorship and an industry-aligned curriculum.
How Big Data Will Help Identify Credit Card Frauds?
Many businesses use big data analytics and data science techniques to prevent identity crises or theft. Other credit card providers like Mastercard, Visa, and RuPay, are also leveraging Big data and Machine Learning to assess the transactional data in real-time. Big data tech combined with data science and Blockchain techniques will protect their users’ data and will also do wonders for the process. Algorithms will ascertain the chances of fraud by inspecting the user’s spending habits and comparing each transaction. Suppose transactions look dubious like many buying or cash removals in a day when you actually haven’t done it, such transactions will get rejected.
An e-wallet will make you to save your credit card details on your smartphone. And you should also know that your mobile will be able to access the location through GPS – Global Positioning System.
But, due to the Data Science and Machine Learning algorithms, credit card fraud detection will be tracked in real-time and be prevented. Big data algorithms will efficiently identify if an online credit card payment is coming from another IP address than the usual one. Moreover, credit card companies will humungous amount of data invested in security protocols for the cloud, like authentication, one-time password, and encryption. Big data is like a dual sword. On one hand, the algorithms examine the frauds, and on the other hand with more available data, the data analysis will get more precise. A credit card is like a jackpot for fraudsters.
Obstacles Faced By Credit Card Companies
Despite several technologies and resources available, there are a few bottlenecks that need to be removed in the way.
A limited set of metrics is available to figure out the fraud detection system’s productivity & accuracy.
When consumer change their consumption patterns, several algorithms are at the risk of indicating it as fraudsters & sending wrong/false signals
Efficient Ways to Reduce Fraud Activities
There are two ways to reduce fraud-based activities: Statistical and Artificial Intelligence
Data pre-processing techniques to detect, validate, error correction, & filling up missing/incorrect data.
Calculate statistical parameters like averages, quintiles, performance metrics, probability distributions, etc.
Data Models and probability distributions of varied business activities - varied parameters or probability distributions.
Time-series analysis of time-dependent data.
Data Clustering and classification to seek out patterns
Data matching to match two sets of collected data. Trying to match sets of knowledge against one another or comparing complex data types.
Regression analysis to look at the connection between two or more variables of interest.
Gap analysis to work out whether business requirements are being met, if not.
Artificial Intelligence Techniques
Data processing to cluster, classify, and segment the info and automatically
Smart systems to encode expertise to detect fraud.
Pattern recognition to detect approximate classes, clusters, or patterns of suspicious behaviour
ML techniques to automatically identify fraud characteristics
Neural networks to generate classification, clustering, generalization, and forecasting
The Bottom Line
Supervised and Unsupervised Machine Learning algorithms should be updated to build new flexible programs. If you want to enhance fraud identification and evade discerning the users, make sure the systems are constantly learning processes about new data and finding new patterns to give valuable insights. Data Science Jobs are in huge demand across India. In fact, the demand is increasing rapidly every year. Experts say India is following the US, in terms of demand for skilled Data Science Professionals. The market analysis says over half of the current IT-talented individuals, engineers and commerce graduates, and statisticians should equip themselves with data science skills since their existing skills are getting outdated or nearly extinct.
LinkedIn’s Emerging Jobs report ranked 'Data Science', as the fastest-growing, most in-demand technology worldwide. You need to strike the right chord to enjoy an astonishing data science career. Data Science is a huge umbrella with varied kinds of job roles. It incorporates various job roles such as Data Science Architect, Business Intelligence Developer, Business Analyst, Data Engineer, Data and Analytics Manager, and Database Administrator.
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