US-20260127605-A1 - Intelligence Model for Analyzing and Determining Transaction Dispute Propensity

US20260127605A1US 20260127605 A1US20260127605 A1US 20260127605A1US-20260127605-A1

Abstract

Transactions may be disputed. Accordingly, streamlining and anticipating possible disputes with transactions may help achieve improved transaction processing efficiency and enhance customer satisfaction. In some arrangements, a transaction processing and dispute resolution system may determine a dispute propensity score for a transaction in order to anticipate and possibly avoid possible disputes for a transaction. This determination may be performed by configuring and applying transaction parameters to a neuro-symbolic artificial intelligence (AI) model. Additionally, the transaction processing and dispute resolution system may also automatically identify resolutions to transaction disputes when they arise using smart contracts. In such a system, the transaction-in-dispute may be analyzed using a neuro-symbolic AI model to identify possible updates to the smart contract rules. This allows the model to further adapt and learn based on new contexts and information.

Inventors

Jemlin Lucas
Suryanarayana Adivi
Pushkar Taneja

Assignees

BANK OF AMERICA CORPORATION

Dates

Publication Date: 20260507
Application Date: 20241104

Claims (20)

1 . A method comprising: detecting, by a transaction processing computing system, a transaction request by a first entity; determining, by the transaction processing computing system, one or more transaction parameters for the transaction request, the transaction parameters including a device type or model through which the transaction request was made, a geographic location from which the transaction request was received, and a unique identifier of the first entity; retrieving, by the transaction processing computing system from an electronic database, a transaction history associated with the first entity, the transaction history including one or more prior transactions requested by the first entity; extracting, by the transaction processing computing system, a plurality of transaction features from the one or more prior transactions requested by the first entity; generating, by the transaction processing computing system, a neuro-symbolic artificial intelligence model using the plurality of transaction features, wherein generating the neuro-symbolic artificial intelligence model includes: defining a plurality of symbolic rules using at least one of the plurality of transaction features; and generating a plurality of machine learning nodes and connections, wherein each of the nodes represents one of the transaction features and each of the connections represents a relationship between at least two of the nodes; determining, by the transaction processing computing system, a likelihood of a dispute arising from the requested transaction by inputting the one or more transaction parameters of the transaction request into the neuro-symbolic artificial intelligence model; determining, by the transaction processing computing system, that the likelihood of a dispute arising from the requested transaction is greater than a threshold; and in response to determining that the likelihood of a dispute arising from the requested transaction is greater than a threshold, requiring a multi-factor authentication process for the requested transaction.
2 . The method of claim 1 , wherein the neuro-symbolic artificial intelligence model is specific to a transaction type.
3 . The method of claim 1 , wherein determining that the likelihood of a dispute arising from the requested transaction is greater than the threshold includes: determining a confidence score of the determined likelihood; and applying the confidence score to the determined likelihood to determine a final likelihood score of a dispute arising from the requested transaction.
4 . The method of claim 3 , wherein determining a confidence score includes: determining an amount of training data used to generate the neuro-symbolic artificial intelligence model; and generating a confidence score based on a magnitude of the amount of training data.
5 . The method of claim 1 , further comprising: in response to determining that the likelihood of a dispute arising from the requested transaction is greater than the threshold, providing an instruction to place a hold on an account associated with the transaction.
6 . The method of claim 1 , further comprising: receiving a dispute for the transaction; determining dispute data and transaction data; determining a resolution for the dispute; and updating the neuro-symbolic AI model using the resolution, the dispute data, and the transaction data.
7 . The method of claim 1 , further comprising: in response to determining that the likelihood of a dispute arising from the requested transaction is greater than the threshold, implementing a monitoring task to monitor a status of the transaction at a specified frequency.
8 . The method of claim 7 , further comprising: in response to determining that the likelihood of a dispute arising from the requested transaction is less than the threshold, processing the transaction without requiring monitoring of the transaction status at the specified frequency.
9 . An apparatus comprising: a processor; and memory storing computer-readable instructions that, when executed, cause the apparatus to: detect a transaction request by a first entity; determine one or more transaction parameters for the transaction request, the transaction parameters including a device type or model through which the transaction request was made, a geographic location from which the transaction request was received, and a unique identifier of the first entity; retrieve, from an electronic database, a transaction history associated with the first entity, the transaction history including one or more prior transactions requested by the first entity; extract a plurality of transaction features from the one or more prior transactions requested by the first entity; generate a neuro-symbolic artificial intelligence model using the plurality of transaction features, wherein generating the neuro-symbolic artificial intelligence model includes: defining a plurality of symbolic rules using at least one of the plurality of transaction features; and generating a plurality of machine learning nodes and connections, wherein each of the nodes represents one of the transaction features and each of the connections represents a relationship between at least two of the nodes; determine a likelihood of a dispute arising from the requested transaction by inputting the one or more transaction parameters of the transaction request into the neuro-symbolic artificial intelligence model; determine that the likelihood of a dispute arising from the requested transaction is greater than a threshold; and in response to determining that the likelihood of a dispute arising from the requested transaction is greater than a threshold, require a multi-factor authentication process for the requested transaction.
10 . The apparatus of claim 9 , wherein the neuro-symbolic artificial intelligence model is specific to a transaction type.
11 . The apparatus of claim 9 , wherein determining that the likelihood of a dispute arising from the requested transaction is greater than the threshold includes: determining a confidence score of the determined likelihood; and applying the confidence score to the determined likelihood to determine a final likelihood score of a dispute arising from the requested transaction.
12 . The apparatus of claim 11 , wherein determining a confidence score includes: determining an amount of training data used to generate the neuro-symbolic artificial intelligence model; and generating a confidence score based on a magnitude of the amount of training data.
13 . The apparatus of claim 9 , further comprising: in response to determining that the likelihood of a dispute arising from the requested transaction is greater than the threshold, providing an instruction to place a hold on an account associated with the transaction.
14 . The apparatus of claim 9 , further comprising: in response to determining that the likelihood of a dispute arising from the requested transaction is greater than the threshold, implementing a monitoring task to monitor a status of the transaction at a specified frequency.
15 . The apparatus of claim 14 , further comprising: in response to determining that the likelihood of a dispute arising from the requested transaction is less than the threshold, processing the transaction without requiring monitoring of the transaction status at the specified frequency.
16 . A non-transitory computer-readable medium storing computer-readable instructions that, when executed, cause an apparatus to: detect a transaction request by a first entity; determine one or more transaction parameters for the transaction request, the transaction parameters including a device type or model through which the transaction request was made, a geographic location from which the transaction request was received, and a unique identifier of the first entity; retrieve, from an electronic database, a transaction history associated with the first entity, the transaction history including one or more prior transactions requested by the first entity; extract a plurality of transaction features from the one or more prior transactions requested by the first entity; generate a neuro-symbolic artificial intelligence model using the plurality of transaction features, wherein generating the neuro-symbolic artificial intelligence model includes: defining a plurality of symbolic rules using at least one of the plurality of transaction features; and generating a plurality of machine learning nodes and connections, wherein each of the nodes represents one of the transaction features and each of the connections represents a relationship between at least two of the nodes; determine a likelihood of a dispute arising from the requested transaction by inputting the one or more transaction parameters of the transaction request into the neuro-symbolic artificial intelligence model; determine that the likelihood of a dispute arising from the requested transaction is greater than a threshold; and in response to determining that the likelihood of a dispute arising from the requested transaction is greater than a threshold, require a multi-factor authentication process for the requested transaction.
17 . The non-transitory computer-readable medium of claim 16 , wherein the neuro-symbolic artificial intelligence model is specific to a transaction type.
18 . The non-transitory computer-readable medium of claim 16 , wherein determining that the likelihood of a dispute arising from the requested transaction is greater than the threshold includes: determining a confidence score of the determined likelihood; and applying the confidence score to the determined likelihood to determine a final likelihood score of a dispute arising from the requested transaction.
19 . The apparatus of claim 11 , wherein determining a confidence score includes: determining an amount of training data used to generate the neuro-symbolic artificial intelligence model; and generating a confidence score based on a magnitude of the amount of training data.
20 . The non-transitory computer-readable medium of claim 16 , further comprising: in response to determining that the likelihood of a dispute arising from the requested transaction is greater than the threshold, providing an instruction to place a hold on an account associated with the transaction.

Description

BACKGROUND Aspects described herein relate to electrical computers, systems, and devices for processing transactions and possible transaction disputes in an efficient, secure, and reliable manner. Online and computer transactions have rapidly evolved, and real-time transactions have become the norm. Although such transactions provide convenience and efficiency, disputes arising from such transactions are increasing and oftentimes pose significant challenges. Existing dispute resolution processes are largely manual and can be slow and labor-intensive. Accordingly, such processes may lead to delays, additional costs, and negative impacts on the customer experience and satisfaction. In one example, users have the capability to make payments through a mobile app to transfer funds to another user's account. However, in some cases, a transaction may be disputed by the sending bank due to various reasons, including the sender's account not having less than the amount of needed funds. Some current dispute resolution processes would require a customer to visit a physical bank facility or raise the complaint in a customer center, provide identification documents, and wait for the bank to investigate and resolve the issue. This process can take several days or even weeks, adding to computing processing load, consuming personnel resources, and causing frustration and inconvenience to the customer or user. BRIEF SUMMARY The following presents a simplified summary in order to provide a basic understanding of some aspects of the disclosure. The summary is not an extensive overview of the disclosure. It is neither intended to identify key or critical elements of the disclosure nor to delineate the scope of the disclosure. The following summary merely presents some concepts of the disclosure in a simplified form as a prelude to the description below. Aspects described herein relate to systems, methods, apparatuses and processes that combine artificial intelligence (AI) models, blockchain technology, adaptive smart contracts, and AI-enhanced oracles to streamline claims settlement and dispute resolution. In some arrangements, the systems, methods, apparatuses and processes implement a neuro-symbolic AI model to facilitate the settlement and resolution. The systems, methods, apparatuses and processes are configured to analyze complex rules and conditions, dynamically adjust security settings, and make informed decisions in real-time. In some examples, the neuro-symbolic AI model may provide reasoning capabilities to interpret complex rules and conditions, enabling the system to understand the context of each transaction and make informed decisions that consider the specific circumstances of each case. According to one or more aspects, systems, methods, apparatuses and processes may learn from transaction and/or dispute resolution data and adapt its behavior to improve dispute resolution, ensuring that the system becomes more effective over time. According to one or more aspects, the systems, methods, apparatuses and processes include smart contract orchestration features which are configured to provide reasoning capabilities to interpret complex rules and conditions, enabling dynamic adjustment of smart contract rules and behavior based on AI predictions and analysis. According to one or more further aspects, systems, methods, apparatuses and processes provide dynamic rule updates, such as rules applied in a smart contract. Such a feature may analyze data and context to suggest updates or modifications to the rules governing smart contracts, ensuring that the system adapts to changing circumstances and remains up-to-date. According to one or more aspects, the systems, methods, apparatuses and processes described herein provide fraud detection and prevention features. For example, the system's fraud detection and prevention capabilities may identify high-risk transactions and dynamically adjust security settings to prevent fraud, ensuring the security and integrity of transactions. According to further aspects, the systems, methods, apparatuses and processes are configured to provide more detailed and clearer explanations for dispute resolution decisions and improves transparency throughout the transaction completion and dispute resolution process, enabling stakeholders to understand the reasoning behind the system's decisions. These features, along with many others, are discussed in greater detail below. BRIEF DESCRIPTION OF THE DRAWINGS The present disclosure is illustrated by way of example and not limited in the accompanying figures in which like reference numerals indicate similar elements and in which: FIGS. 1A and 1B depict an illustrative computing environment for a transaction and dispute processing system and service in accordance with one or more aspects described herein; FIGS. 2A and 2B illustrate an example process flow for transaction processing and dispute resolution accordance with one or more aspects described