Abstract: A system and a method are performed by a processor. A set of challenge questions for authenticating a plurality of customers to perform high-risk activities in their respective accounts associated with an entity is received. A machine learning model is used to determine an authentication score for each challenge question in the set and used to rank them. An electronic request on a computing device from an unverified customer who desires to perform a high-risk account activity is received. The unverified customer is authenticated either as a fraudster or a verified customer based on answers to the ranked challenge questions. The processor performs either allowing the verified customer to perform the at least one high-risk account activity with a respective account associated with the verified customer or blocking the fraudster to perform the at least one high-risk account activity.

Abstract: A system includes memory devices storing instructions, and one or more processors configured to execute instructions performing method steps. The method may include self-supervised training of a bidirectional recurrent neural network (RNN) model to enable reconstruction of an input vector from a global latent vector. The training may optimize parameters for real-time anomaly detection of customer interactions. The global latent vectors may comprise encrypted representations of customer behavior. After training, the system may produce optimized vector embeddings to represent customer behavior using a trained encoder of the model. The encoder may encrypt vectors in real-time for each customer session and determine a security measurement between vectors associated with previous customer sessions and a current session. Based on the security measurement, the system may require an additional security action from a customer to authenticate a customer session.

Abstract: A method executed by a processor may include storing account activity data in a database which identifies prior account activities performed by customers in their respective accounts associated with a financial institution. The database may be accessible only by computing systems of the financial institution. A set of challenge questions may be received for authenticating customers to perform high-risk activities in their respective accounts that are based on prior account activities. A machine learning model may be used to determine an authentication score used for ranking each challenge question in the set of challenge questions. An electronic request may be received from an unverified customer who desires to perform high-risk activities in an account. Challenge questions may be selected based on the ranking. The unverified customer may be verified when correctly answering the selected challenge questions. The verified customer may be allowed to perform high-risk activities in the account.

Abstract: A method executed by a processor may include storing account activity data in a database which identifies prior account activities performed by customers in their respective accounts associated with a financial institution. The database may be accessible only by computing systems of the financial institution. A set of challenge questions may be received for authenticating customers to perform high-risk activities in their respective accounts that are based on prior account activities. A machine learning model may be used to determine an authentication score used for ranking each challenge question in the set of challenge questions. An electronic request may be received from an unverified customer who desires to perform high-risk activities in an account. Challenge questions may be selected based on the ranking. The unverified customer may be verified when correctly answering the selected challenge questions. The verified customer may be allowed to perform high-risk activities in the account.

Abstract: A method executed by a processor may include storing account activity data in a database which identifies prior account activities performed by customers in their respective accounts associated with a financial institution. The database may be accessible only by computing systems of the financial institution. A set of challenge questions may be received for authenticating customers to perform high-risk activities in their respective accounts that are based on prior account activities. A machine learning model may be used to determine an authentication score used for ranking each challenge question in the set of challenge questions. An electronic request may be received from an unverified customer who desires to perform high-risk activities in an account. Challenge questions may be selected based on the ranking. The unverified customer may be verified when correctly answering the selected challenge questions. The verified customer may be allowed to perform high-risk activities in the account.

Abstract: A method executed by a processor may include storing account activity data in a database which identifies prior account activities performed by customers in their respective accounts associated with a financial institution. The database may be accessible only by computing systems of the financial institution. A set of challenge questions may be received for authenticating customers to perform high-risk activities in their respective accounts that are based on prior account activities. A machine learning model may be used to determine an authentication score used for ranking each challenge question in the set of challenge questions. An electronic request may be received from an unverified customer who desires to perform high-risk activities in an account. Challenge questions may be selected based on the ranking. The unverified customer may be verified when correctly answering the selected challenge questions. The verified customer may be allowed to perform high-risk activities in the account.