Features for Identifying Anomalies in Financial Transactions
Tarun Verma
Founder & Head of Trading at Raytrace | Indian Army(merit-in) | AI Product Manager
Identifying anomalies in financial transaction data is at the top priority for the digital payment platforms. This is the key driver of their business. Credit/debit card transactions, cheque transactions, peer-to-peer money transfer, purchase of goods from merchant’s websites, conversion of one currency to another, bank transfers, all require accurate, fast anomaly detection to conduct business safely and protect merchants and customers from devastating losses.
If we figure out anomalies in the above-mentioned transactions, possibly we can detect the fraud and AI based anomaly detection systems can be helpful to detect anomalies beforehand by finding the patterns which can be-- outliers, exceptions, peculiarities that deviate from expected behaviour within datasets. Fraud detection uses anomaly detection to uncover behaviour intended to mislead or misrepresent an actor. The goal of anomaly detection system is to identify bad actors (humans or programs), fraudulent transactions, or network intrusions and take actions based on such identification which can be alerting fraud investigation team, freezing, or suspending accounts, devices, or cards.
Why AI can provide the best solution to the problem
Types of anomalies
Vintage style of fraud detection
Earlier systems used for fraud detection were rule based. They pose few challenges.
Input data for modelling can be grouped into below categories
Customer’s email address, age of his account, number of devices customer has used in the past, fraud rate of customer’s IP address, billing address, average spending.
Average order value, no. of failed transactions, no. of orders placed in past few weeks, basket content.
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Mode of payment, fraud rate of issuing bank, similarity b/w customer name and billing name, cards from other countries.
Shipping address matches the billing address, shipping country matches country of customer’s IP address, fraud rate at customer’s location, time b/w transactions of two retail locations in the past 1 or 2 weeks, number of retails locations per day.
No. of emails, phone numbers or payment methods shared within a network, age of customer’s network. These features focus on network topology to detect any fraud. For example, an account is being shared within the family in a same house v/s account takeover where 100s of accounts use the same few devices.
Velocity of orders, time spend on a page, length of time b/w adding a new card and making an order. This tells about customer’s behaviour, if he is pasting card number into the checkout, if he is using a password vault.
Collect real time rate of fraud by categories like, region, country, ASN card digits, email domain. The real-time traffic monitoring helps merchants to expand their businesses into new markets.
During the modelling whether we choose to go with logistic regression, decision tree, random forest, or neural networks the set of input parameter will look like as mentioned above. There can be some additions to the above list but mostly fall under these broad 7 categories.
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