CIO Business Intelligence

OCTOBER 3, 2023

They enable greater efficiency and accuracy and error reduction, better decision making, better compliance and risk management, process optimisation and greater agility. Intelligent document processing: uses artificial intelligence and machine learning techniques to automate the processing of documents and unstructured data.

Cost-Benefit 122

Cost-Benefit Machine Learning Interactive Software 122

bridgei2i

MAY 17, 2021

The AI-backed interface enables the lender to ensure if the applicants are at high default risks. AI And Risk Management. AI can conduct in-depth analysis, generate reports, and simulate investment scenarios to identify the risks in the system. It allows traders to establish specific rules for trade entries and exits.

Finance 52

Finance Risk Management Unstructured Data Cost-Benefit 52

IBM Big Data Hub

DECEMBER 4, 2023

As part of our generative AI initiatives, we can demonstrate the ability to use a foundation model with prompt tuning to review the structured and unstructured data within the insurance documents (data associated with the customer query) and provide tailored recommendations concerning the product, contract or general insurance inquiry.

Insurance 56

Insurance Digital Transformation Risk Management Risk 56

CIO Business Intelligence

SEPTEMBER 2, 2022

Skills for financial data engineers include coding skills, data analytics, data visualization, data optimization, data integration, data modeling, cloud computing services, knowledge of relational and nonrelational database systems, and an ability to work with high volumes of structured and unstructured data.

Finance 98

Finance IT Software Reporting 98

CIO Business Intelligence

AUGUST 31, 2022

Skills for financial data engineers include coding skills, data analytics, data visualization, data optimization, data integration, data modeling, cloud computing services, knowledge of relational and nonrelational database systems, and an ability to work with high volumes of structured and unstructured data.

Finance 98

Finance IT Software Reporting 98

Cloudera

OCTOBER 28, 2021

The tools and technology to analyze this data have advanced also of course. The capabilities exist to collect real-time data and act on it in real-time to be relevant and affect business decisions. Another example is fleet management. Another example is fleet management.

Insurance 94

Insurance Risk Unstructured Data Data-driven 94

Smart Data Collective

FEBRUARY 2, 2021

Also, thanks to Big Data, recruitment is now more accurate. Keep in mind that recruitment agencies have to deal with huge volumes of unstructured data, and analyzing all this data by traditional means is not only slow, but also ineffective. Public services.

Big Data 84

Big Data Recreation/Entertainment Cost-Benefit Finance 84

Smart Data Collective

OCTOBER 14, 2019

Understanding Big Data Analytics. Big data analytics is the process of evaluating large chunks of information at once. Said information can be a combination of semi-structured and unstructured data sets — coming from web server logs, social media, network traffic logs, etc.

Big Data 69

Big Data Data Analytics Analytics Unstructured Data 69

IBM Big Data Hub

AUGUST 28, 2023

Named entity recognition (NER): NER extracts relevant information from unstructured data by identifying and classifying named entities (like person names, organizations, locations and dates) within the text. Popular algorithms for topic modeling include Latent Dirichlet Allocation (LDA) and non-negative matrix factorization (NMF).

Data mining 57

Data mining Machine Learning Deep Learning Marketing 57

bridgei2i

APRIL 23, 2020

One of the specific AI use case in FS has been Fraud Management, which triggers alerts when seemingly contradictory spending patterns are observed. AI can assess quantitative data, as well as unstructured data systems, for better risk management of financial and reputational losses. Learn MORE. “We

Risk 52

Risk Management Scorecard Forecasting 52

Cloudera

FEBRUARY 15, 2024

From stringent data protection measures to complex risk management protocols, institutions must not only adapt to regulatory shifts but also proactively anticipate emerging requirements, as well as predict negative outcomes. This results in enhanced efficiency in compliance processes.

Insurance 84

Insurance Data Architecture Risk Management Risk 84

Cloudera

JULY 29, 2022

When it comes to FSI, one of the key findings from the report is the importance of risk management and regulatory compliance when it comes to data management. In an industry that is subject to stringent regulatory requirements, it is critical to use data to accurately scale up risk management.

Data Architecture 84

Data Architecture Data Strategy Risk Management Strategy 84

Domino Data Lab

AUGUST 10, 2021

You expose the algorithm to training data, let the model analyze the output and adjust parameters until it achieves the desired goal. Unsupervised learning: the model is free to explore data and to identify patterns between variables. This is useful for grouping unstructured data based on statistical properties.

Modeling 64

Modeling IT Risk Management Deep Learning 64

Ontotext

FEBRUARY 26, 2024

Risk management : Understanding the correlation between events and stock price fluctuations helps manage risk. This semantic model serves as a blueprint or framework against which raw data is analyzed and organized. Then it presents customizable insights through an interactive dashboard for thorough analysis.

Data-driven 59

Data-driven Risk Modeling Risk Management 59

bridgei2i

FEBRUARY 28, 2020

Today, with AI, more sophisticated rules can be developed which address the sparse data problems by factoring in alternate and behavioural data such as smart phone usage and payment behaviour. With AI, apart from the quantitative data, unstructured data systems can be assessed for risk management.

Risk Management 78

Risk Management Unstructured Data Machine Learning Digital Transformation 78

Cloudera

NOVEMBER 18, 2021

This included using NiFi to automatically collect and centralize documents consisting of unstructured data and then leveraging advanced natural language processing to extract tacit knowledge and perform sentiment analysis on unstructured text and images from more than 20 million documents.

Data Lake 74

Data Lake Cost-Benefit Digital Transformation Risk 74

bridgei2i

APRIL 9, 2019

Wealth Management for Clients. Most enterprises and heavyweight financial companies are acquiring start-ups with the motive to analyze the massive amounts of unstructured data automatically. The banking sector that makes the most use of AI is wealth management. This is where AI companies come into the picture.

Unstructured Data 69

Unstructured Data Sales Data-driven Finance 69

bridgei2i

MAY 21, 2019

Wealth Management for Clients. Most enterprises and heavyweight financial companies are acquiring start-ups with the motive to analyze the massive amounts of unstructured data automatically. The banking sector that makes the most use of AI is wealth management. This is where AI companies come into the picture.

Unstructured Data 40

Unstructured Data Sales Data-driven Finance 40

datapine

FEBRUARY 22, 2022

Improved risk management: Another great benefit from implementing a strategy for BI is risk management. However, it is possible to identify some potential drawbacks and apply risk management practices in advance. Pursue a phased approach. Rome wasn’t built in a day: neither will your BI.

Business Intelligence 218

Business Intelligence Strategy Cost-Benefit Key Performance Indicator 218

CIO Business Intelligence

JULY 6, 2023

For example, IDP uses native AI to quickly and accurately extract data from business documents of all types, for both structured and unstructured data,” Reis says. Another benefit is greater risk management.

Cost-Benefit 119

Cost-Benefit Technology Unstructured Data Testing 119

Laminar Security

MAY 3, 2023

They define DSPM technologies this way: “DSPM technologies can discover unknown data and categorize structured and unstructured data across cloud service platforms. Start by using DSG to establish the data security policies and posture, and then take the final three steps to assess the DSPM deployment.”

Management 52

Management Risk Risk Management Data Processing 52

Jet Global

MAY 9, 2024

The architecture may vary depending on the specific use case and requirements, but it typically includes stages of data ingestion, transformation, and storage. Data ingestion methods can include batch ingestion (collecting data at scheduled intervals) or real-time streaming data ingestion (collecting data continuously as it is generated).

Data Lake 52

Data Lake Data Warehouse Business Intelligence Machine Learning 52