Smart Data Collective

SEPTEMBER 17, 2020

Financial institutions such as banks have to adhere to such a practice, especially when laying the foundation for back-test trading strategies. Some prominent banking institutions have gone the extra mile and introduced software to analyze every document while recording any crucial information that these documents may carry.

Big Data 142

Big Data Finance Risk Management Risk 142

DataRobot Blog

APRIL 29, 2022

With the big data revolution of recent years, predictive models are being rapidly integrated into more and more business processes. This provides a great amount of benefit, but it also exposes institutions to greater risk and consequent exposure to operational losses. Model Validation – Prior to the use of a model (i.e.,

Risk 111

Risk Modeling IT Risk Management 111

BizAcuity

APRIL 1, 2023

Anti-Money Laundering (AML) is increasingly becoming a crucial branch of risk management and fraud prevention. In this case, once a customer’s documents are scanned and uploaded, the necessary data is extracted from the key documents and then converted to machine-readable form.

Machine Learning 52

Machine Learning Predictive Analytics Predictive Modeling Risk 52

BizAcuity

JANUARY 18, 2023

Anti-Money Laundering (AML) is increasingly becoming a crucial branch of risk management and fraud prevention. In this case, once a customer’s documents are scanned and uploaded, the necessary data is extracted from the key documents and then converted to machine-readable form. Predictive Analytics. These include-.

Machine Learning 52

Machine Learning Predictive Analytics Predictive Modeling Risk 52

O'Reilly on Data

MAY 18, 2020

All predictive models are wrong at times?—just As the renowned statistician George Box once quipped , “All models are wrong, but some are useful.” Broadly speaking, materiality is the product of the impact of a model error times the probability of that error occuring. just hopefully less so than humans.

Risk 359

Risk Modeling Data Processing Software 359

O'Reilly on Data

DECEMBER 12, 2019

Model debugging is an emergent discipline focused on finding and fixing problems in ML systems. In addition to newer innovations, the practice borrows from model risk management, traditional model diagnostics, and software testing. 6] Debugging may focus on a variety of failure modes (i.e.,

Machine Learning 191

Machine Learning Modeling Testing Risk Management 191

CIO Business Intelligence

OCTOBER 13, 2023

Responsibilities include building predictive modeling solutions that address both client and business needs, implementing analytical models alongside other relevant teams, and helping the organization make the transition from traditional software to AI infused software.

Deep Learning 143

Deep Learning Machine Learning Modeling Software 143