What is fraud detection like with machine learning?
Machine learning methods for fraud detection can be divided into supervised and unsupervised models, where supervised methods demand a large amount of data, such as fraudulent and non-fraudulent transactions in equal amounts to train the model. In fraud detection, machine learning is a collection of artificial intelligence (AI) algorithms trained on your prior information to suggest risk rules.
Difference between machine learning and artificial intelligence
However, while every type of machine learning counts as artificial intelligence, not all artificial intelligence uses machine learning.
AI is a larger concept designed to create machines that simulate human thought. Machine learning is a subset of AI that allows machines to learn from information without needing to be reprogrammed.
It’s also worth mentioning that machine learning has its own subset, called deep learning. It uses algorithms and structures modeled after the human brain.
The advantages of AI for misrepresentation of the board
Because machines do a better job of processing large data sets than humans, what you get is the ability to parse and target large amounts of information. This means:
Better predictions on large data sets: The more information you feed a machine learning engine, the more trained it becomes. This means that while it is challenging to find patterns in large data sets for humans, it is quite the opposite with an AI-driven system.
Economic and effective solution: Instead of hiring more risk operations agents, you only need a machine learning system to analyze all the information provided, regardless of volume. This is ideal for businesses that have occasional peak seasons, high data traffic, online payments, or user access.
Disadvantages of machine learning in fraud detection
Despite having its advantages, there will always be cases where old-fashioned manual reviews will be preferable.
Less Control: This is true on the downside of machine learning engines, they can make mistakes and no one will notice.
That being said, an improperly calibrated machine learning engine can create an endless cycle where more false positives are not flagged as false, the less accurate results will come down the line.
What is fraud analysis?
It can help financial organizations predict future fraudulent behavior and apply rapid detection and mitigation of fraudulent activity in real time.
Account takeover (ATO), a particularly popular form of financial fraud, skyrocketed more than 280% between the second quarter of 2019 and the second quarter of 2020. Financial institutions must, more than ever, apply comprehensive risk management measures. fraud to protect their customers’ accounts.
The challenge of financial fraud
Banks and other financial institutions have a responsibility to their customers to protect their data and finances against fraud or theft. This has become a complex task, at least in part, because customers can access their accounts through financial transactions on multiple channels. They can do their banking through a mobile banking app, an online banking portal, by calling the call center, or even by visiting the bank in person.
But how can you verify that the person logging into an online bank account is really that person and not a scammer logging in with stolen credentials? Cybercriminals can purchase them from an average of $15.43 for consumer credentials to more than an average of $3,139 for credentials for an organization’s key systems.
Financial crime use cases
Account Takeover: Account takeover occurs when a fraudster uses stolen credentials to access an existing online account, for example, at a bank or merchant.
Sim Swapping: This is a form of ATO in which the scammer uses the victim’s personal information, stolen from a data breach or extracted from other information sources, such as social media, to convince the mobile phone company to transfer the victim’s phone number to the scammer’s mobile phone.
Phishing: A phishing attack is when the scammer impersonates a legitimate website in an email or text to get the victim to eventually divulge personal information or transfer funds.
Malware: Fraudsters use various methods, including phishing, to trick victims into loading malicious software on their device to record keystrokes, corrupt data, or render the device useless unless a ransom is paid.
Card Not Present (CNP): In CNP fraud, the fraudster uses a stolen credit card account to complete a transaction where the physical card is not required, such as an online purchase at an e-commerce site. Like other forms of fraud, credit card fraud is on the rise.
Fraud analysis is the key to financial fraud risk management
As banks put corrective measures in place, new threats appear. Traditional fraud prevention systems based on static rules cannot keep up.
Collecting a username and password at login is no longer enough to protect against fraudulent activity. When someone accesses or attempts to access, an account, there is other information that can be used to determine if they are a legitimate customer and whether or not the requested transaction is legitimate. This includes data such as:
Does the requested transaction fit your historical patterns?
In the sense of authentication, this data can be divided into four categories:
Knowledge: Something the user knows, for example, their password, their social security number, etc.
Possession: something that the user has, for example, his mobile phone, etc.
Inherence: something the user is, for example, their fingerprint, palm print, etc.
Behavior: something the user does or is doing, for example, your requested transaction
To answer all these questions it is necessary to access and analyze big data. One thing that banks absolutely do not want to do is add any undue friction to the customer session.
Traditionally, banks had a set of rules that reviewed applications and provided an approve or reject decision. They often result in too many false positives – legitimate transactions being blocked – and fraudulent transactions being lost. score in real time. This allows a risk-based fraud analysis solution to apply the right level of security, at the right time, using layered authentication.
Big data analysis techniques to combat financial fraud
Data science is part of the solution. Financial institutions collect enormous amounts of data about behavior, devices, and transactions. With good data, there are a number of big data analytics techniques that a machine learning-based fraud analytics system can use to combat financial fraud.
Predictive analytics examines patterns to make predictions about hitherto unknown future events to understand the potential or propensity for fraud.
Pattern recognition and anomaly detection identify events that do not conform to expected patterns. Machine learning algorithms can learn from data and make predictions about future events.
Author Bio :
Glad you are reading this. I’m Yokesh Shankar, the COO at Sparkout Tech, one of the primary founders of a highly creative space. I’m more associated with digital transformation solutions for global issues. Nurturing in fintech, supply chain, AR VR solutions, real estate, and other sectors vitalizing new-age technology.