Understanding Fraud Analytics – New Way to Combat Fraud

Fraud analytics relies on the use of big data analysis to detect and prevent online financial fraud. It can help financial organizations learn about ongoing fraud trends and build safeguards to protect themselves.

More and more people rely on online banking for the convenience it offers. The 2020 lockdown acted as a catalyst for online banking. With the ever-increasing number of users, online financial fraud numbers have also gone up. Out of all types of financial fraud, Account takeover fraud is the most prominent.

With fraud analytics, financial institutions can gain deeper insights into financial fraud, fraudulent behaviors, and how to protect against fraud.

Financial institutions today have to apply robust fraud management measures to ensure their and their customer’s security.

Challenge of Financial Fraud

Financial institutions are obligated to protect their customer’s sensitive information from fraudsters. Over time, keeping information secure has become more complex as customers can access their accounts from multiple channels. Customers can use mobile banking apps, online banking, or call the bank’s customer service to perform financial activities.

This opens up the bank to several risk points. A fraudster could log in using the mobile app with stolen credentials and the bank would have no way to distinguish between a legit user and a fraudster.

It is also becoming increasingly easy for fraudsters to steal credentials. To give you an idea, the dark web has over 15 billion credentials that you can buy for next to nothing.

The average price for banking credentials is as low as $15.43 for a single consumer. If a fraudster wants to buy credentials for an organization’s key system, the average price is $3,139.

Types of Online Financial Crime

  • Account Takeover Fraud

ATO is one of the most prominent types of financial fraud. A fraudster uses stolen credentials to take over an existing online account. The fraudster then uses the account to commit financial fraud.

  • Sim Swap

Sim Swapping is another type of account takeover fraud. In this type of fraud, the fraudster uses a victim’s personal information, to try and convince the mobile company to port the victim’s phone to another number.

When the mobile company ports the number, the fraudster conducts financial fraud and the victim is unaware until it’s too late.

  • Phishing Attacks

Phishing attacks are aimed to target less technically proficient users. This type of fraud happens when a fraudster impersonates a legitimate company/service provider. Then the fraudster sends a text/email to the user asking them for their personal information.

Once a less suspecting victim shares their personal information, the fraud begins.

  • Malware

Fraudsters use several methods to gain a victim’s personal information. This includes trying to trick a victim into installing malicious programs on their device. This malware is designed to log keystrokes, corrupt data, or make the device unusable until the victim pays a ransom.

  • Card Not Present

CNP is becoming more prevalent because of a growing trend of eCommerce shopping. Fraudsters use stolen credit card accounts to make online transactions.

How do Fraud Analytics Help in Financial Fraud Management?

Online fraud is ever evolving and financial institutions need to keep finding new ways to combat fraud. Traditional methods of fighting fraud are not up to the standards. Fortunately, there is a huge pile of data that financial institutions can use to predict and detect fraud. 

Just having a username and password isn’t enough to protect customers and institutions against fraud. When someone accesses, or attempts to access a victim’s information, there is behavioral data that banks can use to verify if this is a legitimate transaction or not. 

Vital data that financial institutions can use to detect fraud include:

  • What device a user is using?
  • If the device has been previously registered with the bank.
  • Can the user verify their identity with a fingerprint?
  • Does the transaction data fit the previous patterns?

These types of data can be broken down into four categories:

  • Knowledge: Something that a user knows, such as passwords, identity information, username, etc.
  • Possession: This type of data signifies something that a user has, such as a mobile phone.
  • Inherence: This is something that a user is, such as a fingerprint, retinal data, palm print, etc.
  • Behavioral: Something that a user does. Any activities that form a pattern, such as their requested transaction, or a series of transactions.

By analyzing these data and combining them with big data, fraud analysts can discover hidden patterns. 

Banks, since forever have been operating on a fixed set of rules that examine requests and provide a yes/no decision. These rules are based on increasing fraud techniques which expands the rules sets and these rules end up becoming too complex. 

Even the most complex traditional rules don’t adapt to hidden or unknown threats. Having systems that haven’t adapted to the latest developments leads to a huge number of false positives.

Machine learning solutions can collect massive amounts of data. These solutions can also analyze heaps of data and assign a real-time risk score for a customer. 

This is how fraud analytics help in detecting and preventing online financial fraud.

ML Models for Fraud Detection and Prevention

Fraud analytics is applying machine learning techniques to financial data. Fraud analysts use machine learning to examine all the valuable data to determine whether the transaction is high-risk or low-risk.

Based on the outcome, machine learning solutions offer recommendations to either allow or block the transaction. There are also cases where multi-factor authentication is needed before approving a transaction.

There are two different types of machine learning solutions. Unsupervised, or supervised. Unsupervised machine learning models analyze unstable data sets to find anomalies in the data. The model can also detect otherwise hidden relationships in the data to suggest a function or instruction set to describe the underlying dimensions of the data. 

Supervised machine learning models on the other hand are trained using labelled data. These models predict the likelihood of fraud. The way to train supervised models is by presenting them with legitimate and fraudulent data and commanding them to analyze the data to develop an instruction set or an algorithm. 

This algorithm is then used on other examples to verify the capabilities of the model. A perfectly trained supervised machine learning model can identify known and unknown patterns. These models are most likely to provide an accurate risk score for a requested transaction.

Data Analytics Techniques to Fight Financial Fraud

Data science is also a part of the solution to fight fraud. Financial institutions collect behavioral, device, and transactional data of every customer. Analyzing this data through a fraud detection system can help in the detection and prevention of financial fraud. 

But the analysis can only be as great as the data available in the data set. If a financial institution has great data available, there are several data analysis techniques that a machine learning-based fraud system can use to fight fraud. 

Predictive analysis is looking at available data and making predictions about the future. Using past events to figure out a pattern and then showcase the potential prosperity for fraud.

Pattern recognition is another data analytics technique that businesses can use to combat fraud. Machine learning models analyze data sets to detect anomalies and identify patterns that are different.

Machine learning algorithms can learn from the data and make predictions for future events.

Forensic analytics is examining the causes and consequences of a financial fraud event. By analyzing the data and relationships between the cause and the consequences, it is possible to identify potentially fraudulent behavior and expose cooperation between fraudsters.

fraud analytics definition