A system and method for settling of a transaction is provided. The system includes a server arrangement having a rules based engine and a meta-object system comprising a runtime utility engine configured to compile multiple meta-object instances, wherein the server arrangement is configured to receive a lump sum payment proposal from the user, the lump sum payment proposal comprising only that amount the user is presently willing to pay, process information comprising the lump sum payment proposal according to the rules, and present a second transaction settlement offer set to the user including the lump sum payment proposal and a plurality of offers limited to a plurality of installment payment options with no further immediate payment offered beyond the lump sum payment proposal.

Financing tools can provide a flexible credit services to customer. A credit service provider can collect personal data from clients that can include a mobile telephone number and a legal name of the client as well as purchase information from a merchant. Based upon the collected data, the system can determine a client credit risk. The system can make a credit decision to offer a client credit to purchase goods or services based upon the credit risk.

Financing tools can provide a flexible credit services to customer. A credit service provider can collect personal data from clients that can include a mobile telephone number and a legal name of the client as well as purchase information from a merchant. Based upon the collected data, the system can determine a client credit risk. The system can make a credit decision to offer a client credit to purchase goods or services based upon the credit risk.

A suite of fluidless predictive machine learning models includes a fluidless mortality module, smoking propensity model, and prescription fills model. The fluidless machine learning models are trained against a corpus of historical underwriting applications of a sponsoring enterprise, including clinical data of historical applicants. Fluidless models are trained by application of a random forest ensemble including survival, regression, and classification models. The trained models produce high-resolution, individual mortality scores. A fluidless underwriting protocol runs these predictive models to assess mortality risk and other risk attributes of a fluidless application that excludes clinical data to determine whether to present an accelerated underwriting offer. If any of the fluidless predictive models determines a high risk target, the applicant is required to submit clinical data, and an explanation model generates an explanation file for user interpretability of any high risk model prediction and the adverse underwriting decision.

A suite of fluidless predictive machine learning models includes a fluidless mortality module, smoking propensity model, and prescription fills model. The fluidless machine learning models are trained against a corpus of historical underwriting applications of a sponsoring enterprise, including clinical data of historical applicants. Fluidless models are trained by application of a random forest ensemble including survival, regression, and classification models. The trained models produce high-resolution, individual mortality scores. A fluidless underwriting protocol runs these predictive models to assess mortality risk and other risk attributes of a fluidless application that excludes clinical data to determine whether to present an accelerated underwriting offer. If any of the fluidless predictive models determines a high risk target, the applicant is required to submit clinical data, and an explanation model generates an explanation file for user interpretability of any high risk model prediction and the adverse underwriting decision.

This systemic risk management system comprises: a sampling means which, given a set of interbank loans, i.e., loans of funds from any of multiple banks to any borrower included in the aforementioned multiple banks, generates a sample which represents the aforementioned set modified by means of a reconnection in which the aforementioned borrower of the interbank loan selected from the set is replaced with a selected bank; an important transaction designation means which selects multiple of the aforementioned generated samples on the basis of the scale, derived on the basis of the set represented by the sample, of a bankruptcy of the multiple banks resulting from the effects of the collapse of prescribed investments of at least one of the multiple banks, and which designates an important interbank loan on the basis of the interbank loans included in the aforementioned selected multiple samples in which reconnections have been made; and an important bank designating means which designates an important bank on the basis of the designated important interbank loan.

This systemic risk management system 100A comprises: an important bank designation unit 12 which, from multiple banks involved in interbank loans, designates an important bank on the basis of the smallness of a first bankruptcy scale, which is the scale of bankruptcy of the multiple banks that would be brought about due to the effects of a collapse of prescribed investments, funded by at least one of the multiple banks, in the case of preventing the bank included in the multiple banks from bankrupting due to the effects of the collapse of the aforementioned prescribed investments; and an important loan designation unit 13 which, from one or more interbank loans made by the important bank, designates an important interbank loan on the basis of the smallness of a second bankruptcy scale, which is the scale of bankruptcy of the multiple banks due to the effects of a collapse of the aforementioned investments in the case of preventing bankruptcy of the bank that is the borrower of the interbank loan included in the aforementioned one or more interbank loans and bankruptcy of the important bank caused by said collapse.

Methods and systems for extending credit associated with credit ratings are disclosed. One or more credit scores of a person may he received and, if the credit scores are insufficient to qualify the person for a credit line from a lender, the lender may inform the person what actions can be undertaken to improve the credit scores. Once completed, the lender may then extend a credit line to the person. Alternatively, a lender may receive a first credit score of a person and determine the actions to be undertaken in order to improve the credit score to a second credit score. Pre-determined time periods for completion may he established. The lender may determine different credit lines based upon different credit scores and extend a credit line to the person or upgrade/downgrade the credit line if certain actions are completed or not completed.

A method and corresponding system to determine an optimized credit limit assignment using reinforcement learning techniques in order to maximize a reward function for a given bank. A reinforcement learning module is configured to use a set of user profiles and an associated set of risk profiles to determine an initial credit limit assignment. Based on this initial credit limit assignment, an updated set of user profiles and an associated set of updated risk profiles are generated. The reinforcement learning module can use these updated sets of user profiles and associated risk profile as inputs to determine an optimized credit limit assignment that maximizes the reward function for the given bank.

A method and corresponding system to determine an optimized credit limit assignment using reinforcement learning techniques in order to maximize a reward function for a given bank. A reinforcement learning module is configured to use a set of user profiles and an associated set of risk profiles to determine an initial credit limit assignment. Based on this initial credit limit assignment, an updated set of user profiles and an associated set of updated risk profiles are generated. The reinforcement learning module can use these updated sets of user profiles and associated risk profile as inputs to determine an optimized credit limit assignment that maximizes the reward function for the given bank.