Sharing financial crime knowledge

A computerized-method for scaling automatic deployment of a machine learning detection model in a cloud-based managed analytics service by knowledge sharing, to overcome an imbalanced dataset learning problem, said computerized-method comprising:

• a computerized system comprising a processor, a database, and a memory;
• a plurality of client computers each comprising an on-prem cloud-based managed analytics service;
• wherein the on-prem cloud-based managed analytics services continuously monitor criminal typologies and operating fraud and Anti-Money Laundering (AML) model performance;
• a storage comprising a consortium shared synthetic data lake,
• wherein the storage is remote from the computerized system and the plurality of client computers,
• wherein the consortium shared synthetic data lake comprises differential privacy data received from the computerized system,
• wherein the differential privacy data excludes any individual personal details,
• the computerized system:
• receiving financial transaction data from an associated on-prem cloud-based managed analytics service,
• determining fraudulent financial transaction data in the received financial transaction data;
• generating, by machine learning models at the computerized system, new differential privacy data by synthesizing patterns of the fraudulent financial transaction data;
• wherein the new differential privacy data excludes any individual personal details,
• wherein the synthesized patterns are at least one of:
• fraud patterns or AML patterns;
• storing the new differential privacy data in the database of the computerized system;
• updating the consortium shared synthetic data lake by adding the new differential privacy data to the consortium shared synthetic data lake;
• creating a packaged machine learning detection model, by the processor of the computerized system, based on (i) the received financial transaction data and (ii) data obtained from the updated consortium shared synthetic data lake,
• wherein the data obtained from the updated consortium shared synthetic data lake comprises differential privacy data associated with financial transaction data from at least one different client;
• deploying the packaged machine learning detection model on-prem of the client of the associated on-prem cloud-based managed analytics service.