Real-time fraud detection algorithms have proved extremely valuable for numerous big players in the financial services industry, most notably credit card companies.
As the number of transactions, third-party integrations, and users continue to grow, security threats in finance are increasing, and achine learning algorithms are becoming more accurate at detecting fraudulent behaviors.
For instance, customers’ location, amounts, financial transactions, time of transactions, merchant/vendors that they pay are recorded in real-time. Machine learning algorithms can analyze each action a customer takes and assess if the behavior is characteristic of previous behavior. Automatically, the system can request additional identification from the user to validate the transaction or block it if there is at least 95% probability of it being fraudulent. The speed at which the system processes data and makes a prediction helps to prevent frauds in real time, not after the crime has already been committed.
There are many reasons to require income or asset verification for potential customer, namely in the residential real estate industry. Cutting-edge companies in this space have ditched the dated method of requesting multiple pages of balance statements from the customer's bank account(s). They are instead taking advantage of new capabilities of banks and debit card providers to gain programmitic access to balance and income data.
Further, this data allows sellers to move past the idea that income is a single number. They can instead perform seasonanility and stability analysis against various income and spending streams, effectively allowing them to conceptualize their own credit score using much more granular data than what Big 3 credit bureaus can access. This process implicitly teases out whether or not the customer accurately reported his or her own income -- yet another piece of information to consider.