Abstract: Example embodiments of systems and methods for card production are provided. A card may include processing circuitry including a chip and memory. The card may include one or more antennas in communication with the chip. The card may include a first layer of material aligned within a perimeter of the card via laminate encapsulation. The first layer of material may comprise a non-rectangular shape. The first layer of material may be offset with a shape of the card. The first layer of material may be in communication with the chip. The first layer of material may comprise at least one selected from the group of steel, tungsten, titanium, or any combination thereof. The card may be compliant with one or more form factors.

Abstract: Disclosed herein are system, method, and computer program product embodiments for providing recommendations for splitting bills. The approaches disclosed include the ability to obtain information about a bill to be split (such as a photo of the bill), and then use several machine learning models to determine the ‘who,’ ‘what,’ and ‘where’ of the underlying transaction. In particular, machine learning models described herein are used to perform facial recognition of a ‘selfie’ taken when a transaction was made against social media accounts to determine participants of the transaction. The machine learning models may also identify expected pricing from data about a merchant associated with the transaction, and expected amounts for each participant based on the expected pricing.

Abstract: Systems and methods of anti-vishing OTP protection via machine learning techniques are disclosed. In one embodiment, an exemplary computer-implemented method may comprise: receiving a permission indicator identifying a permission by the user to detect OTPs and calls being received by a computing device; receiving an indication of an OTP data item being received; processing the OTP data item to determine a time duration during which a particular OTP included therein is valid; utilizing a trained OTP protection machine learning model to determine phone number(s) as presenting a security risk with respect to the OTP data item; and instructing the computing device to commence at least one security measure based at least in part on a contact list updated with an indication that the phone number(s) present a security risk with regard to the particular OTP during the time duration of the particular OTP.

Abstract: In some implementations, a device may identify an exchange, associated with an object, between a first user and an entity. The device may determine that the entity is associated with at least one characteristic that satisfies a condition. The device may transmit, to a device of the first user and based on determining that the entity is associated with the at least one characteristic that satisfies the condition, a first notification prompting the first user to provide information associated with the object. The device may receive the information associated with the object. The device may determine based on the information associated with the object and using a machine learning model, at least a second user predicted to have a level of interest in the object that satisfies a threshold. The device may transmit, to a device of the second user, a second notification that identifies the object.

Abstract: Disclosed herein are system, method, and computer program product embodiments for utilizing non-RAM memory to implement environmental impact scoring. An embodiment operates by identifying environmental impact components associated with a product, calculating the environmental impact value for each of the environmental impact components to generate a plurality of environmental impact values and scoring the product based on the plurality of environmental impact values to reflect an environmental impact score. Environmental impact scores may be displayed for customer consideration during a potential purchase.

Abstract: In some implementations, a system may receive a distribution request that is associated with a user account and an entity category. The system may identify a set of receiving entities associated with the entity category. The individual entities of the receiving entities may be approved to receive distributions associated with user accounts of the service provider. The system may determine, according to impact scores for the set of receiving entities, a ranking of the set of receiving entities according to respective information associated with individual entities of the set of receiving entities. The system may select, from the set of receiving entities and according to the ranking, a receiving entity from the set of receiving entities to receive the distribution as indicated in the distribution request. The system may facilitate a transfer of the distribution from the user account to the receiving entity.

Abstract: In some implementations, a system may receive engagement data associated with a user. The system may determine, using a style classification model, that the engagement data is associated with the style, wherein the style classification model is trained to identify styles of a subject based on reference images associated with one or more of the styles of the subject. The system may determine, based on the engagement data, a level of interest that the user has in one or more objects associated the style. The system may identify, based on the level of interest satisfying a threshold, a first object and a second object that are associated with the style. The system may configure an association between the first object and the second object. The system may provide the association to permit the user to interact with the first object, the second object, and/or the association.

Abstract: In some implementations, a system may receive an audio stream associated with a call between a user and an agent. The system may analyze the audio stream to identify a trigger associated with the type of information. The system may monitor, based on identifying the trigger in a first section of the audio stream, a second section of the audio stream for audio content that identifies user information associated with the user. The system may identify a subsection of the second section that includes the audio content, wherein the subsection is identified based on a characteristic of the audio content and the type of information. The system may alter an audio characteristic of the subsection to prevent the agent from receiving the user information via the audio stream.

Abstract: In some implementations, a system may receive an audio stream associated with a call between a user and an agent. The system may process, by the device and using a speech alteration model, speech from a first channel of the audio stream to alter the speech from having a first speech characteristic to having a second speech characteristic, wherein the speech alteration model is trained based on reference audio data associated with the first speech characteristic and the second speech characteristic and based on reference speech data associated with the first speech characteristic and the second speech characteristic. The system may extract the speech from the first channel that has the first speech characteristic. The system may provide, within a second channel of the audio stream, altered speech that corresponds to the speech and that has the first speech characteristic.

Abstract: Systems and methods of record annotation via machine learning techniques are disclosed. In one embodiment, an exemplary computer-implemented method may comprise: receiving at least one annotating content item being associated with at least one first record of a user; utilizing a trained machine learning model to: i) generate at least one derived annotating content item based at least in part on the at least one annotating content item and data of the at least one first record; ii) identify at least one second record related to the user based at least in part on: the data of the at least one first record and one or both of profile information and context information of the user; and iii) annotate the at least one second record with the at least one derived annotating content item.

Abstract: In some implementations, a credit decision platform may receive a credit request from an applicant and obtain domestic historical data associated with the applicant from a credit bureau device. The credit decision platform may obtain access to an email account associated with the applicant based on determining that the domestic historical data associated with the applicant is insufficient to process the credit request. The credit decision platform may identify, using one or more machine learning models, a set of email messages included in the email account that are relevant to the credit request and may analyze content included in the set of email messages to generate non-domestic historical data associated with the applicant. The credit decision platform may generate a decision on the credit request based on an estimated creditworthiness of the applicant, which may be determined based on the non-domestic historical data.

Abstract: A processor may receive data and generate a quantified representation of the data by processing the data using at least one machine learning (ML) algorithm, the quantified representation of the data indicating a sentiment of content of the data. The processor may automatically revise the content of the communications data. The revising may include determining a reaction to the content of the communications data, generating a quantified representation of the reaction, determining a difference between the quantified representation of the reaction and the quantified representation of the communications data, identifying, based on the difference, a portion of the content having an unintended sentiment, and replacing the portion of the content with different content.

Abstract: Disclosed embodiments may include a system that may receive an indication that a user is accessing an ATM, receive, from the ATM, average session duration data over a predetermined period, generate, using a machine learning model, a busyness score for the ATM based on the average session duration data over the predetermined period, and determine whether the busyness score for the ATM exceeds a busyness score threshold. When the busyness score for the ATM does not exceed the busyness score threshold, the system may cause the ATM to present, via a first graphical user interface, a default ATM experience. When the busyness score for the ATM exceeds the busyness score threshold, the system may cause the ATM to present via, a second graphical user interface, a busy ATM experience.

Abstract: Disclosed embodiments may include a system that may receive an interaction message associated with an interaction a user has with an application or website, the interaction message may include an error message or a repeated action message. The system may identify, using a first machine learning model, one or more issues associated with the interaction message, retrieve one or more troubleshooting steps mapped to the one or more issues, and generate a first message comprising the one or more troubleshooting steps and a feedback request on an effectiveness of the one or more troubleshooting steps. The system may transmit the first message to the user, receive feedback from the user in response to the feedback request, and determine whether the feedback is negative. When the feedback is negative, the system may transmit a second message to a representative requesting the representative call the user.

Abstract: Systems and methods disclosed herein provide automatic expense categorization of transactions or expenditures based on a machine learning (ML) model trained using anonymized transactional data for expenditures that are stored in a public blockchain. The anonymized transactional data for the expenditure and the expense category, may be distributed throughout the blockchain network and recorded in the blockchain. In some implementations, an expenditure may be submitted to the blockchain network for expense categorization. The transactional data for the expenditure may be anonymized to remove confidential and personal identifying information (PII) before it is distributed throughout the blockchain network. Each participating node of the blockchain network may utilize the ML model to identify an expense category for the expenditure. The participating nodes may provide a consensus mechanism in order to arrive at a shared understanding of how to categorize the expenditure.

Abstract: A system may include one or more processors, a memory in communication with the one or more processors, and storing instructions, that when executed by the one or more processors, are configured to cause the system to predict user travel. The system may receive transaction data, and extract travel information from the transaction data. The system may assign a confidence score to the travel information based on comparing the travel information to previous travel information. The system may determine whether the confidence score is greater than or equal to one or more thresholds. Responsive to determining the confidence score is greater than or equal to one or more thresholds, the system may perform one or more fraud prevention activities.

Abstract: In some implementations, a device may obtain interaction data for an evaluator in a communication session with an individual based on interactions of the evaluator with the individual during the communication session, wherein the evaluator is to provide the evaluation of the individual. The device may obtain evaluation information identifying the evaluation of the individual by the evaluator. The device may determine a degree of bias of the evaluator toward the individual during the communication session based on at least the interaction data. The device may adjust the evaluation of the individual based on the degree of bias of the evaluator.

Abstract: In some implementations, a device may determine an interaction profile for a provider that is to engage with a user in a communication session. The interaction profile may be based on interaction data relating to interpersonal interactions involving the provider during one or more previous communication sessions. The interaction profile may indicate a bias of the provider in connection with one or more categories of users. The device may generate, based on the interaction profile, a visual representation that depicts at least a face of a person for presentation to the provider during the communication session. One or more characteristics associated with the one or more categories of users may be absent from the face of the person. The device may cause presentation of the visual representation to the provider during the communication session.

Abstract: A system may include processor(s), a memory in communication with the processor(s), and storing instructions, that when executed by the processor(s), are configured to cause the system to facilitate reservation trading. The system may identify an upcoming reservation and a booking rate of the reservation. The system may receive comparable reservation list rates to generate a predicted list rate of the reservation. The system may suggest the first user request a refund or place the reservation on an exchange based on whether the reservation is refundable and whether the predicted list rate is greater than the booking rate. The system may receive a resell rate from the first user to transmit to a second user. The system may receive a selection of the reservation and payment information from the second user. The system may process the payment information and transfer the reservation from the first user to the second user.