Fraud Detection System Design Interview

Implementing Data Analytics For Fraud Detection

Many insurance companies use different Fraud detection tools to detect fraud. But a more dependable framework is needed to make the fraud detection process more successful. We have listed here few steps on how to implement analytics for fraud detection

Many organizations have realised the increasing the importance of fraud analytics. But in a hurry they are opting for expensive fraud detection solutions that do not match with the companys strengths and weaknesses. Therefore organizations should do SWOT analysis before starting with fraud detection program in order make it work to the fullest.

• Build a dedicated fraud management team

Traditional companies do not have a specific team for fraud detection. But these days it is important to have a dedicated team that works to find and prevent frauds in the organization. The team should have a proper flow and a proper reporting fraud detection system.

• Build or buy option

Integrate all the databases in the organization and remove all unwanted things from the databases.

• Layout relevant business rules

Companies should come up with business rules after doing research on the resources and expertise of the company. There are different types of fraud and few of which are specific to particular industry. The external vendor cannot build a robust fraud detection solution without getting the proper inputs from the organization or company.

• Setting the threshold

• Predictive Modelling

• Forward looking analytics solutions

What Is Credit Card Fraud Detection

Credit card fraud is a term that has been coined for unauthorized access of payment cards like credit cards or debit cards to pay for using services or goods. Hackers or fraudsters may obtain the confidential details of the card from unsecured websites. When a fraudster compromises an individual’s credit/debit card, everyone involved in the process suffers, right from the individual whose confidential data has been leaked to the businesses who issue the credit card and the merchant who is finalizing the transaction with purchase. This makes it extremely essential to identify the fraudulent transactions at the onset. Financial institutions and businesses like e-commerce are taking firm steps to flag the fraudsters entering the system. Various advanced machine learning technologies are at play, assessing every transaction and stemming the fraud users in its nip using behavioral data and transaction patterns. The process of automatically differentiating between fraudulent and genuine users is known as .

Get Closer To Your Dream of Becoming a Data Scientist with 150+ SolvedEnd-to-End ML Projects

Q12 The Crop Yield In India Is Degrading Because Farmers Are Unable To Detect Diseases In Crops During The Early Stages Can Ai Be Used For Disease Detection In Crops If Yes Explain

AI can be used to implement image processing and classification techniques for extraction and classification of leaf diseases.

Image Processing Using AI Artificial Intelligence Interview Questions Edureka

This sounds complex, let me break it down into steps:

Image Acquisition: The sample images are collected and stored as an input database.

Image Pre-processing: Image pre-processing includes the following:

• Improve image data that suppresses unwanted distortion
• Enhance image features
• Image clipping, enhancement, color space conversion
• Perform Histogram equalization to adjust the contrast of an image

Image Segmentation: It is the process of partitioning a digital image into multiple segments so that image analysis becomes easier. Segmentation is based on image features such as color, texture. A popular Machine Learning method used for segmentation is the K-means clustering algorithm.

Feature Extraction: This is done to extract information that can be used to find the significance of a given sample. The Haar Wavelet transform can be used for texture analysis and the computations can be done by using Gray-Level Co-Occurrence Matrix.

Classification: Finally, Linear Support Vector Machine is used for classification of leaf disease. SVM is a binary classifier which uses a hyperplane called the decision boundary between two classes. This results in the formation of two classes:

Don’t Miss: How To Ask Good Interview Questions

I Am New To Machine Learning Can I Learn It Without Any Difficulty

No doubt! Machine Learning is in high demand, and at the same time, employers need professionals who have the right skills for building applications for the future.

Here, at Intellipaat, we have created our program by acknowledging that our learners come from varied backgrounds. So, we curate it from the basic level and gradually increase the difficulty level to grasp all the concepts taught in the program quickly. Further, we make sure that the learners skills would be equivalent to 6-month experience in this technology by the end of the program.

What Kind Of Projects Will You Work On In This Applied Ml Certification Training

• As part of this course training, you will work on real-world projects in the fields of e-commerce, automation, marketing, sales, banking, Internet, insurance, and more.
• Our projects include building a chatbot to answer customer queries, building a recommendation system, fare prediction for taxi booking, analyzing the trends of COVID-19 with Python, customer churn classifier, etc.
• Upon successful project completion, your skills will be equivalent to 6 months of industry experience.

Recommended Reading: Waiting For Asylum Interview Date

Major Challenges Facing Fraud Detection Ways To Resolve Them Using Machine Learning

Fraud detection has been one of the major challenges for most organizations particularly those in banking, finance, retail, and e-commerce. This goes without saying that any fraud negatively affects an organizations bottom line, its reputation and deter future prospects and current customers alike to transact with it.

More often than not, for any fraud detected, the organization ends up paying for the losses. Additionally, it takes the good customers away from them while attracting more fraudsters.

Given the scale and reach of most of these vulnerable organizations, it has become indispensable for them to stop these frauds from happening or even predict all suspicious actions beforehand at all times.

Frauds can range from really small like non-payment for e-commerce orders to threatening like public exposure of customers credit card details.

Machine learning comes to the rescue here. On setting up automated data science processes with deep learning algorithms, organizations can greatly reduce the risk of their exposure to most of such frauds.

Existing Fraud Detection Methods

Detecting and reducing fraud using artificial intelligence isnt new. There are machine learning models already being deployed by enterprises across the globe.

Most modern fraud detection methods involve a domain expert tasked with 2 responsibilities –

1. They are needed to gather historic transaction data, and

Objective Of The Study:

The main objective of this work is to design automated software that will automate our banking sector and equally aid in the total elimination of fraudulent from gaining access to the hiding information in our various banking sectors.

The Objective Of The Study Focuses On The Following:

Furthermore, the study will show the effect in the manpower of the security department because on most occasions charging the manual information system to an automated information system will equally cause redundancy.

Also Check: What Are Good Questions To Ask At A Job Interview

Pscu Fraud Detection Representative Interview Questions

584

Anonymous Employee in Biloxi, MS

Application

I applied online. The process took 4 days. I interviewed at PSCU in May 2022

Interview

The interview process was very welcoming and Kevin was very professional and thorough about the overview of the position. His calm voice and well mannerism made me accept the job offer without one doubt I made the right decision.

Develop The Data Engineering Transformation And Modeling Pipelines

After we have envisioned the architecture of the fraud detection solution, we will start the development of the data engineering, transformation, and modeling pipelines. I have listed key activities for each of those pipelines in the graph below.

• For the data engineering pipeline, we need to ingest and merge the data from different sources, aggregate the data based on business metrics, and set up batch processes.
• For the data transformation pipeline, the main goal is to improve the data quality, deal with data issues such as missing & incorrect data and convert the data so that it could be fed into machine learning models.
• For the machine learning model pipeline, we focus on building and comparing diversified ML models based on key business metrics. A module for automated model accuracy testing and re-training is a necessity in the production environment to avoid model drifting issue.

Don’t Miss: What To Say In Interview Thank You Email

Introduction To Fraud Detection Algorithms In Machine Learning

For years, fraud has been a major issue in sectors like banking, medical, insurance, and many others. Due to the increase in online transactions through different payment options, such as credit/debit cards, PhonePe, Gpay, Paytm, etc., fraudulent activities have also increased. Moreover, fraudsters or criminals have become very skilled in finding escapes so that they can loot more. Since no system is perfect and there is always a loophole them, it has become a challenging task to make a secure system for authentication and preventing customers from fraud. So, Fraud detection algorithms are very useful for preventing frauds.

• ID Document Forgery

Payment Fraud: These types of fraud are very common in todays card systems for banking. Fraudsters can steal cards, make counterfeit cards, steal Card ID, etc. Once they steal the confidential data of a user, they can buy things, apply for a loan, and pretty much anything they imagine.

Identity Theft: Attackers or cybercriminals can hack into their victims accounts and gain access to their credentials like, name, bank account details, email address, passwords, etc. They can use these credentials to cause harm to their victim. There are three types of identity theft: real name theft, account takeover, and synthetic theft.

Get 100% Hike!

Master Most in Demand Skills Now !

Top Features Of Fraud Detection Software

Fraud detection software should cover a lot of bases, from chargeback prevention to identity verification. Here are examples of must-have features:

• Risk rules: The cornerstone of fraud detection. Risk rules allow you to filter user actions based on the data you find. A basic risk rule would be: If the IP address belongs to a VPN, block the login attempt.
• Risk scoring: Some rules can be static. with the simple option to allow or reject user actions. More sophisticated systems let you play around with scores and thresholds, which give you a gauge of how risky an action is. For example: If a customer is not based in the same country as their payment card, add 5 points to their risk score.
• Real-time monitoring: Consider your needs. When it comes to preventing chargebacks or to complying with anti-money laundering regulations, your detection software must be able to monitor payments on your site. For ID verification and account protection, you must also be able to immediately block a suspicious action before its too late.
• Machine learning engine: Chances are that youll be poring over huge volumes of data. The best fraud prevention software will include an ML, or machine learning algorithm, that can help suggest risk rules based on your historical business data. Bonus points if its a whitebox system that allows you to understand the reasoning behind the risk rules.

Don’t Miss: What To Wear To An Interview In The Summer

Is The Material Effective In Preparing Me For The Ml Certification

Intellipaats Machine Learning program provides material that comprises all the necessary modules to learn this popular technology. These pre-recorded video lectures and material are highly effective as they allow you to complete the whole program at your own pace and take your time to learn the concepts thoroughly.

The Best Fraud Detection Software In 2022

Without further ado, lets look at our list of the best fraud detection software for the year including pros and cons and which problems they solve the best.

Disclaimer: Everything in this article was gleaned from online research and user reviews. We did not manually test the tools. However, we ensured the information was correct as of Q3 2022. Feel free to get in touch to request an update or correction.

You May Like: Interview Questions And Answers For It Support Engineer

Handling Payment Processing Delays

As discussed previously, an end-to-end payment request flows through many components and involves both internal and external parties. While in most cases a payment request would complete in seconds, there are situations where a payment request would stall and sometimes take hours or days before it is completed or rejected. Here are some examples where a payment request could take longer than usual:

• The PSP deems a payment request high risk and requires a human to review it.

• A credit card requires extra protection like 3D Secure Authentication which requires extra details from a card holder to verify a purchase.

The payment service must be able to handle these payment requests that take a long time to process. If the buy page is hosted by an external PSP, which is quite common these days, the PSP would handle these long-running payment requests in the following ways:

• The PSP would return a pending status to our client. Our client would display that to the user. Our client would also provide a page for the customer to check the current payment status.

• The PSP tracks the pending payment on our behalf, and notifies the payment service of any status update via the webhook the payment service registered with the PSP.

When the payment request is finally completed, the PSP calls the registered webhook mentioned above. The payment service updates its internal system and completes the shipment to the customer.

Dont Be Afraid To Double Check

One major shortcoming in inexperienced fraud interviewers is their reluctance to reconfirm statements, revisit subjects and ask for more examples. Often, interviewers dont like to give the impression that they dont understand or need a second explanation, but this step is crucial to a successful interview. Revisiting a subject multiple times and asking for more examples not only provides clarity, but can also identify inconsistencies in the interviewees statements. Interviewers should regularly go back through the details of a certain procedure or process that the interviewee has already explained, asking for more detail and examples, with questions like:

• I dont understand this specific process. Can you explain to me what the senior accountants role is again?
• It doesnt make sense to me that the checks are signed without the supporting documentation present. Can you walk me through the process again so I can understand the timing of the review?

The least of an interviewers worries should be looking uninformed or foolish. Nothing should get in the way of gaining a full understanding of what the interviewee is explaining.

Read Also: Technical Interview Questions For Engineering Manager

Downloading And Loading The Dataset

We will be using this standard dataset on Kaggle to demonstrate our steps, starting from exploring the dataset to building a model and finally evaluating the performance on several indexes. Once we have the data, creditcard.csv , we will use pandas to read the CSV data and obtain a DataFrame to work on.

# import the necessary packages import numpy as np import pandas as pd import matplotlib.pyplot as plt import seaborn as sns from matplotlib import gridspec ###################################### Load the dataset from the csv file using pandas data = pd.read_csv

What Makes Machine Learning An Absolute Must

ML is one of the most sought-after courses by data companies globally, owing to the immense pace at which the world is shifting towards AI and automation. Hence, by leveraging Intellipaats ML training, you will be exposed to numerous high-paying job opportunities and that too, at the starting few phases of your career.

Here are a few essential pointers that may make you think seriously about ML:

• There are over 7,332 ML jobs available in India alone, as per .
• Over 64,000 job openings are available for Machine Learning professionals in the USA, according to LinkedIn.
• As per Indeed, the average income of Machine Learning Engineers is US$140,579 per annum in the United States.
• The average annual income of Machine Learning Engineers in India is 685,100 .
• The growth rate for ML jobs is about 350%!

Also Check: Questions About Leadership For Interview

Why Should I Join This Machine Learning Online Course At Intellipaat

Intellipaats comprehensive Machine Learning online training with CCE, IIT Madras consists of hands-on projects and case studies. A few of the many reasons for choosing Intellipaats ML training online includes the following:

• You will learn various concepts such as ML using Python, classification techniques, linear algebra behind linear regression, logistic regression, supervised and unsupervised learning, and more.
• After completing the lectures, you will receive Intellipaats certificate, valued by 100+ MNCs worldwide.
• This Machine Learning program covers real-time ML projects and exercises that are highly relevant in the corporate world. It also includes an extensive curriculum created by industry experts.
• Our ML course classes will allow you to compete for some of the best positions in the worlds leading MNCs for higher salaries.
• We provide lifetime access to videos of these ML classes, resources, their free upgrades to the latest version, and 24/7 learning support.

Security With A Simple Swipe

Prevent fraud by taking your mobile first strategy to the next level, where security and usability are fully aligned with:

• A secure, 3D solution utilizing better built-in protection for eWallets, online banking and mobile payments
• Channels that can be secured using out-of-band communication protected by device-specific keys
• The assurance that the person taking the digital action is the person authorized to do so using asymmetric key cryptography

Don’t Miss: How To Prepare For A Teaching Assistant Interview

Sift One Of The Fastest Growing Anti

Launched in 2011, Sift is now a $1 billion company, funded in part by startup accelerator Y Combinator. They offer fraud protection for a whooping 34,000 sites and apps, including world-renowned names such as Airbnb and McDonalds.

Its key products include a Digital Trust and Safety Suite, which combines all the individual API tools into one complete solution. Then, there is a module specifically designed to authenticate users and to avoid ATO attacks, which includes the ability to enable 2FA at the same time.

For all your chargeback challenges, Sift offers a Payment Protection product, which uses the data from its global network. It analyzes payment information in real-time and uses machine learning to develop new risk rules.

Finally, you can also purchase the Dispute Management module, which is specifically designed to help monitor and log as much data as possible when going through a chargeback resolution process.

Among Sifts customers we find Airbnb, McDonalds and Doordash.

Pros of Sift

Cons of Sift

• Blackbox AI: What you gain in ease of use, you lose in terms of being able to understand why the AI suggests certain risk rules.
• No real-time social media checks: An important piece of the puzzle when it comes to ID proofing, reverse social lookup isnt available with Sift.
• No free trial: Youll need to go through the lengthy demo and sales calls before you can sign up for a contract and try Sift.

Sift Pricing

• Available upon request to the sales team.

Choose Sift If: