CIO Business Intelligence

JUNE 21, 2022

CIOs frequently launch strategic initiatives without fully considering all the risks involved. By that time, governance structures are rushed and risk mitigation measures lose their effectiveness.”. If you can’t see the data, then you can’t properly govern it,” Lahiri says.

IT 116

IT Risk Measurement Risk Management 116

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 88

Insurance Data Architecture Risk Management Risk 88

Cloudera

JUNE 12, 2024

Traditional machine learning (ML) models enhance risk management, credit scoring, anti-money laundering efforts and process automation. Some of the biggest and well-known financial institutions are already realizing value from AI and GenAI: JPMorgan Chase uses AI for personalized virtual assistants and ML models for risk management.

Finance 77

Finance Risk Management Risk Data-driven 77

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

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

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 89

Data Architecture Data Strategy Risk Management Strategy 89

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 106

Big Data Recreation/Entertainment Cost-Benefit Finance 106

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 97

Insurance Risk Unstructured Data Data-driven 97

CIO Business Intelligence

SEPTEMBER 30, 2024

CIO.com / Foundry They also cited AI/ML capabilities in specific areas — such as risk management, fraud detection, smart manufacturing, predictive maintenance, quality control, and personalized employee engagement — as fueling transformation. Everyone is looking at AI to optimize and gain efficiencies, for sure.

Machine Learning 141

Machine Learning ROI Data Processing Optimization 141

CDW Research Hub

MAY 24, 2021

It encompasses other components, including data security that focuses primarily on protecting unstructured data in storage from unauthorized access, use, loss or modification. Develop a security risk management program. Apply defense-in-depth measures and assess the security controls to identify and manage risk.

Risk Management 52

Risk Management Unstructured Data Risk Business Objectives 52

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

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 88

Big Data Data Analytics Analytics Unstructured Data 88

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 Deep Learning Risk Management 64

AWS Big Data

SEPTEMBER 26, 2024

Organizations are collecting and storing vast amounts of structured and unstructured data like reports, whitepapers, and research documents. By consolidating this information, analysts can discover and integrate data from across the organization, creating valuable data products based on a unified dataset.

Data Lake 108

Data Lake Cost-Benefit Unstructured Data Modeling 108

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 Unstructured Data Risk Management Cost-Benefit 52

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

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 77

Data Lake Cost-Benefit Digital Transformation Risk 77

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 53

Insurance Digital Transformation Risk Management Interactive 53

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

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

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

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 53

Data mining Machine Learning Deep Learning Marketing 53

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 Dashboards 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

CIO Business Intelligence

MARCH 27, 2023

Thirty-four percent of IT leaders responding to the 2023 State of the CIO survey called out data/business analytics as a major tech initiative driving IT investments, second only to security and risk management (38%). Foundry / CIO.com With data and analytics a critical engine for driving business strategy, Dow Inc.

Strategy 109

Strategy Digital Transformation Insurance Technology 109

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 40

Management Risk Risk Management Data Processing 40

CIO Business Intelligence

FEBRUARY 14, 2025

Since data is the fuel for AI, unlocking its full potential is only possible when organizations have mastered data management. However, according to Foundry research conducted for GEP, weak internal data management capabilities were the most common challenge organizations face when preparing data for AI initiatives (45%).

Management 59

Management Unstructured Data Risk Management Risk 59

CIO Business Intelligence

FEBRUARY 4, 2025

founder Paul Chada said his company was actively testing a private instance in Azure and it noticed that the R1 model is easily able to get the same results for complex unstructured data extraction as OpenAIs o1 or Claude-Sonnet for instance at a fraction of the cost. Other experts, such as agentic AI-providing Doozer.AI

Enterprise 101

Enterprise Data Processing Consulting Risk 101

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