Abstract: A system may include a processor that may detect one or more loan indicators present in banking data or credit data associated with a user. The processor may then retrieve additional data associated with the user in response to detecting the one or more loan indicators, such that the additional data may include data acquired from a home assistant device, a wearable device, a computing device, or any combination thereof. The processor may then determine a loan probability associated with the user based on the banking data, the credit data, and the additional data. The processor may then determine a pre-approval loan amount based on the banking data and the credit data in response to the loan probability exceeding a threshold and automatically send a notification indicative of a pre-approval loan amount to a computing device associated with the user.

Abstract: A customer service system that includes a customer device associated with a customer, an agent device associated with an agent, and a monitoring system. The monitoring system includes a memory that stores instructions and a processor that executes the instructions. The instructions, when executed by the processor, cause the monitoring system to monitor a conversation between the customer device and the agent device to identify a context of the conversation. Moreover, the instructions cause the monitoring system to identify a disclosure notice based at least part on the context of the conversation, in which the disclosure notice comprises an indication of a disclosure to be read in light of the context of the conversation.

Abstract: A system includes a client device including a processor, a telephony radio that supports telephony communications, and a data radio that supports data communications. The client device conducts, via the telephony radio, a call with a telephony service. Further the client device identifies a context of the call, identifies a subset of stored private data associated with the context of the call; and in parallel with the call, provides the subset of the stored private data, via a data radio, to a data service.

Abstract: An electronic device receives operational data indicative of an operational characteristic of a vehicle from a sensor of the electronic device, a sensor of the vehicle, and/or images/videos captured by a camera. The electronic device determines that the vehicle has had a potential incident and a likelihood that the potential incident has actually occurred based on analysis of the operational data. The electronic device also receives risk management data associated with the vehicle from a database, and determines a severity level for the potential incident based on the operational data and the risk management data. The electronic device then sends a notification indicative of the potential incident based on the likelihood that the potential incident has actually occurred and the severity level for the potential incident to a third-party remote system (e.g., of a towing service, an emergency service, or both) to request assistance.

Abstract: A computing device establishes communication with a financial institution communication device, and receives an instruction for a user to perform an action to authenticate the user. The action includes providing user-provided data indicative of a location. The computing device determines first location information based on the user-provided data. The computing device also determines second location information using a location sensor. The computing device authenticates the used based on determining that the first location information correlates to the second location information.

Abstract: Systems and methods are provided for dynamic routing of an automated telephony system. The automated telephony system facilitates functions desired by a caller, via an automated call with the caller. A machine-learning analysis system extracts data from the automated call, performs machine-learning via the extracted data to identify a likely motivation of the caller associated with the automated call, and provides the likely motivation to the automated telephony system. The automated telephony system then receive the likely motivation from the machine-learning analysis system and dynamically routes the automated call based upon the likely motivation.

Abstract: A system includes a client device including a processor, a telephony radio that supports telephony communications, and a data radio that supports data communications. The client device conducts, via the telephony radio, a call with a telephony service. Further the client device identifies a context of the call, identifies a subset of stored private data associated with the context of the call; and in parallel with the call, provides the subset of the stored private data, via a data radio, to a data service.

Abstract: A system may include a processor that may detect one or more loan indicators present in banking data or credit data associated with a user. The processor may then retrieve additional data associated with the user in response to detecting the one or more loan indicators, such that the additional data may include data acquired from a home assistant device, a wearable device, a computing device, or any combination thereof. The processor may then determine a loan probability associated with the user based on the banking data, the credit data, and the additional data. The processor may then determine a pre-approval loan amount based on the banking data and the credit data in response to the loan probability exceeding a threshold and automatically send a notification indicative of a pre-approval loan amount to a computing device associated with the user.

Abstract: A system may include a processor that may receive an indication of an amount of funds deposited into an account, convert the amount of funds into a number of tokens using a multiplier, and generate a visualization representative of a growth rate of the number of tokens over a period of based on an interest rate. The interest rate may be defined by a user with supervisor authority over the account. The processor may then display the visualization via an electronic display and periodically transfer an additional amount of funds from an additional account associated with the user to the account in response to the amount of funds remaining in the account after a period of time.

Abstract: A call center data transfer system includes one or more processors that are configured to receive call data related to information provided by a caller during a first portion of a call with a first customer service representative. The one or more processors are also configured to assign an identifier to the call in response to receipt of an indication that the call should be transferred from the first customer service representative, associate the identifier with the call data in one or more databases, receive the identifier as an input during a second portion of the call with a second customer service representative, and use the identifier to retrieve the call data from the one or more databases. The one or more processors are further configured to display the call data via a display for visualization by the second customer service representative.