Patent ID: 11922493
Assignee: CONFIRMU PTE. LTD.
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 10:
11. One or more non-transitory computer-readable storage mediums storing one or more sequences of instructions, which when executed by one or more processors, causes a method for deriving a machine learning generated score that indicates a likelihood of loan repayment based on visual choices related to a plurality of simulated scenarios within a game from a user device, using a machine learning model performing steps of:
displaying, for the plurality of simulated scenarios within the game that is linked to attainment of a goal under a time constraint, a plurality of images related to the plurality of simulated scenarios, wherein the plurality of images and the plurality of simulated scenarios are shuffled randomly each time the game is played;
obtaining at least one visual choice related to an image from the plurality of images that are displayed at the user device of the user, wherein the at least one visual choice is tagged with at least one behavioral trait that is derived from a hybrid psychological framework that is selected from at least one of (i) OCEAN (Openness, Conscientiousness, Extraversion, Agreeableness, and Neuroticism), (ii) Myers Briggs Type Indicator (MBTI), (iii) Hogan Personality Inventory, or (iv) Robert Hare Psychopathy checklist (PCL-R);
training the machine learning model by correlating historical behavioral trait scores for a subset of behavioral traits of historical users who played the game with previous credit histories or loan repayment records of the historical users who have played the game to obtain a trained machine learning model;
assigning, using the trained machine learning model, points to at least one behavioural trait of the user based on the at least one visual choice to obtain at least one behavioral trait score, wherein the at least one behavioral trait score comprises (i) an introversion score, (ii) an extraversion score, (iii) a grandiosity score, (iv) a neuroticism score, (v) a perceiving score, (vi) an intuition score, (vii) an integrity score, (viii) a communication score, (ix) a judging score, (x) a sensing score, and (xi) a feeling score; and
determining a machine learning generated score that indicates the likelihood of the loan repayment of the user based on (i) the points assigned to the at least one behavioural trait using the trained machine learning model, and (ii) a total time taken for obtaining all visual choices within the game to determine creditworthiness and a risk assessment of the user, wherein the trained machine learning model correlates the machine learning generated score of the user that is determined with at least one of an existing alternative credit score or a progressive repayment record and is retrained when there is a misalignment between the at least one of the existing alternative credit score or the progressive repayment record and the machine learning generated score of the user for predicting the machine learning generated score of the user with improved accuracy.