FIELD

The present disclosure relates to custom ranking systems and methods for machine learning models applicable to fraud assessments and credit risk assessments.

BACKGROUND

Credit risks can arise when credit is extended to account holders that are likely to default. Financial institutions are particularly concerned with offering credit based on the perceived risk of default associated with the credit. Credit accounts that go into default are often losses.

Once credit is extended to card members, their accounts may occasionally be compromised. Financial institutions offering transaction accounts may protect users against fraud by reimbursing account holders for fraudulent charges. As such, financial institutions seek to minimize fraud in order to minimize their losses as a result of reimbursement expenses. Financial institutions may implement risk assessment systems to assess risk, but those systems have traditionally been only as effective as the expert-written rules guiding the systems.

Models may be used in some scenarios to predict a likely outcome. For example, models may be built and used in assessing credit risk and fraud risk. However, the models are often sub-optimal for use in a particular scenario, as there may be a gap between the loss function that machine learning uses and the metric used to evaluate the accuracy and/or precision of the model. Inaccurate models result in lower fraud capture rates (i.e., more fraud goes undetected) or inefficient characterization of credit risk of customers (i.e., more defaults occur than expected). Thus, inaccurate models result in greater losses for financial institutions.

SUMMARY

A system, method, and computer readable medium (collectively, the “system”) is disclosed for analyzing risk using machine learning models. The system may be trained using a data set to generate a risk assessment model that is optimized for at least two metrics. The metrics may include Gini and CaptureRate, for example. The system may receive a request for a financial service, and generate a risk assessment by applying the risk assessment model to factors associated with the request. The system may also decide on the request in response to the risk assessment.

In various embodiments, the system may swap a first value and a second value from a metric of the at least two metrics in response to the first value being lower than a predetermined threshold. A pair may be assigned a numeric weight (e.g. 1 or 2) indicating the importance of ordering them accurately with respect to their known risk. In various embodiments, a pair of the metric may be assigned a value selected from a set including −1, 0, and 1. The pair of the metric may also be assigned a value selected from a set including 1 and 2. The pair of the metric may be assigned the value of 2 in response to a first member for the pair being greater than the predetermined threshold and a second member of the pair being less than the predetermined threshold.

The forgoing features and elements may be combined in various combinations without exclusivity, unless expressly indicated herein otherwise. These features and elements as well as the operation of the disclosed embodiments will become more apparent in light of the following description and accompanying drawings.

