received by the International Bureau on 21 January 2019 (21.01.2019)

A process of a default probability prediction and credit scoring framework, comprising the steps of: a) fetching the user data by fetching means via video source (10); b) securing and calculating the user data via central computing unit (11) and accesses the learnt parameters via common shared memory (12); c) interfacing the said system with plurality of computation accelerators (13) for parallel computation and for faster rate of data updation; d) sending the loan application for auto filing and reviewal by deep neural network; e) creating an internal storage database (14b) by processing the user data by advanced processing system, after customer or applicant applies for a loan; f) generating inputs to smart model by intelligent retrieval system; g) making a profile after reviewing additional or outsource demographics of a person seeking loan via smart model; h) providing best feasible probability prediction of a person seeking loan or vice versa, on the basis of said credit score; i) approaching of bank, organization and institute and signing a smart or digital contract for loan disbursement; and j) monitoring and tracking the loan timely after loan disbursement.

The process of a default probability prediction and credit scoring framework as claimed in claim 1, wherein said user data includes real-time data by means of face recognition (4a), an emotion detection (4b), pulse detection (4c), speech analysis (4d) and galvanic skin response (4e).

The process of a default probability prediction and credit scoring framework as claimed in claim 1, wherein said outsource or additional demographics database of a person

25 seeking loan includes social media presence (6a) and financial data (6b).

The process of a default probability prediction and credit scoring framework as claimed in claim 1, wherein said internal storage database is being generated from the past data as well as deep neural network model approach.

The process of a default probability prediction and credit scoring framework as claimed in claim 1, wherein said deep neural network model is based upon heuristic formulation or calculations which takes out the various parameters as a set of inputs and executes some distinctively computative process. 6. The process of a default probability prediction and credit scoring framework as claimed in claim 1, wherein said synergic score is generated for rating and identifying the applicant's profile.

7. The process of a default probability prediction and credit scoring framework as claimed in claim 1, wherein said monitoring, and tracking of a person seeking loan is done by various sources and input to the smart neural network model.

A default probability prediction and credit scoring framework system, comprising:

video source (10) in raw format for fetching user data;

26 a host or central computing unit (11), to store the user data, wherein said host or central computing unit (11) has

computation accelerator (13), shared memory (12), and application software ;

a deep neural network model (14a), to transfer processed data to internal storage database (14b) which stores and keeps the updated data for future perspective.

A default probability prediction and credit scoring framework system configured to operate the steps as claimed in claims 1 - 8.

27