Financial frauds have been prevalent for a long time. Banks and all other industries have been trying to control frauds for quite some time. Credit card issuers and payment services have had to take major steps to reduce fraudulent activities.
Below are some examples of how AI can lend itself perfectly for detection of such frauds across credit card issuers and payment services.
Credit Card and Debit Card Frauds
In 2015, credit, debit and prepaid card issued worldwide reached around $21. 84 billion according to Bloomberg Report and is expected to increase by 45% by 2020. Some of the ways in which credit card and debit card frauds can be prevented are listed below:
Device Intelligence can be used to determine whether a device profile from which transaction is occurring is legitimate within a span of milliseconds, allowing banks to stop the frauds before they occur. Information can be collected from devices based on cookies and web beacons as soon as a customer visits a bank’s website (e.g. login, checkout, account creation, account registration).
The solution finds whether a device, which the user has utilized, has an abnormally high amount of online activity and uses it, in conjunction with other factors, to determine fraud patterns and recommend whether a transaction needs to be denied, approved or reviewed. All this can happen in less than a second.
Such a system can be trained using a list of previous frauds (both internal and external) that have occurred and have been detected by fraud analysts.
A real time machine learning algorithm can watch for both internal and external frauds that have similar device, transaction, behavioral, contextual, and account patterns to predict fraudulent behavior and provide each transaction a risk score.
Some of the data points that such a system can use to predict fraudulent transactions include:
- Determining past behavior of device or account
- Discovering whether a device is jailbroken / rooted devices
- Determining whether a device is virtual machine or emulator
- Defining attribute risks for a device
- Confirming geolocation mismatches
- Determining device type / OS / Screen Resolution mismatches
- Checking association with other devices and accounts
Behavioral intelligence can be embedded into applications to identify online fraudsters using their familiarity with application process, navigation, and input when they visit a bank’s website/ This can help banks reduce the number of false declines in the process of credit card application.
Some of the use cases that the behavior biometric solution can help solve are
- Identity proofing: Machine learning algorithms can be used to analyze account creation fluency, navigation fluency, advanced computer skills know-how, and low data familiarity in order to distinguish real user and an imposter even in cases where the user is new.
- Continuous Authentication: Assesses a user’s behavior to authenticate the identity of the logged in user in cases wherein logged in users forget to logout. Such a system can also use cognitive factors such as eye hand coordination, applicative behavioral patterns, usage preferences, device interaction patterns, physiological factors such as left, right handedness, press size, hand tremors, etc. and contextual information such as transaction, navigation, and device patterns to determine legitimate or fraudulent usage of the application.
- Fraud Prevention: Can perform real time detection of criminal behavior, malware, Remote Access Tools (RAT), aggregators, robotic activity, social engineering attacks, etc. that are not recognized by traditional fraud prevention methods. Based on the aforementioned criteria, machine learning can analyze the user’s behavior and provide him a risk score.
Decision Intelligence can also be used to reduce credit card fraud. As a user makes a purchase, deep learning models can be used to determine whether the type of purchase, time, location, and purchase cost along with an array of other data points such as IP address, device ID, email, phone number, etc. are in line with client’s previous transactions. Such an application can also check merchants’ system to confirm whether the customer or system is assigned a risk score. In the absence of a pattern that is consistent with fraud, the system can approve the transaction.
Machine learning can help merchants, financial service consultancies, and payment service providers differentiate between fraudsters and genuine customers. A customer’s digital identity can be determined by using data points such as email, phone, location, IP address, device ID, passport number, etc. These details are defined when an individual transacts online and are updated as the customer evolves.
Machine learning algorithms can also study a customer’s recent online activity such as payment behavior, social media, social security, IP location, device activity, and billing address. The more the data points that are available for a customer, the lower the risk score for that customer. Based on these inputs from the system, merchants and banks can improve their security to authenticate or assess the risk process.
These data can also be used to update the customer’s profile and determine the trustworthiness of the customer. This would allow merchants to be aware of fraudulent transactions such as chargebacks, fake account, spams, account takeover, etc.
For example, if the same fraudster lists different name variations when opening an account, say Kris Jefferson, Kris Jeff, Kris Jesse, etc. the algorithm will analyse data points (IP addresses, devices, bank accounts, payment behavior, etc.) from these logins to determine a risk score associated with such transactions.
The risk score, as well as the data points used to arrive at the risk score, can be shown to a human analyst who can intervene as and when required. This human intervention can be fed into the system to increase its future accuracy and improve its algorithms.
With the growth of technology and interest of companies in the field of data science, I am sure there are many other use cases that can be tackled by AI in this field, these forming just the tip of the iceberg.
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