Search result for: "Banking, Insurance, other financial services"

Benchmarks features matrix

The attached document represents the DataBench Framework for technical benchmarks. It is extended from the dimensions in the BDVA Reference Model – with additional aspects of industry sectors and application areas. The Benchmarks are listed chronologically according to when they have been introduce...

Earth Observation and Geospatial Pipeline pattern from DataBio R&D Project

Example of a Geospatial EO Pipeline pattern. This information can be found in the DataBench deliverable D5.4.The pipeline for Earth Observation and Geospatial data processing, developed in the DataBio project, depicts the common data flow among six project pilots, four of which are from the agricul...

Financial services - Fraud detection Architectural Blueprints

A sample IT architecture for dealing with Fraud detection.This information can be found in DataBench deliverable D5.4.A major problem faced by banks and insurance services is the risk of frauds: frauds have a major impact in costs for companies, because in most of the cases refunding is the only av...

Qualitative Benchmarks by Industry

Qualitative KPIs (time efficiency, product/service quality, customer satisfaction, number of new products/services launched, and business model innovation) are measured on a rating scale of 1–5, corresponding to a range of improvements (from less than 5% to 50% or more). We used the average rating ...

Qualitative Benchmarks for the Financial Services Industry

Qualitative KPI benchmarks confirm the high level of benefits achieved in the finance industry and the focus on customer satisfaction. The history of applying statistical and machine learning techniques to predicting market and individual asset values is long. But, with the increasing availability ...

Quantitative Benchmarks by Industry

This table shows the benchmark improvements of profit, revenues and cost reduction achieved by industry (median value). The financial, business/IT services, telecom/media industries and utilities have the highest benchmark improvements. Manufacturing is aligned with the overall sample benc...

Quantitative Benchmarks for the Financial Services Industry

The finance industry (banks and insurance companies) is a traditionally data abundant sector, which has always tried to play this ace to be a leader in technical innovation. On top of this, customer satisfaction is a real competitive differentiation between the variety of service providers operat...

Quantitative Benchmarks for the Top 3 Use Cases by Industry

Agriculture and Healthcare sectors are the ones that can benefit the most in the near future from BDTs deployment, as the current level of adoption is still very low. Agriculture’s most-cited BDA use case is leading edge in terms of technology application (precision agriculture), indicating an appr...

Quantitative Benchmarks for the Top 3 Use cases in Financial Services

The financial services industry has always been a leader in technical innovation, and customer satisfaction is the competitive differentiator between service providers, be they high street banks, insurance companies, or investment management providers. An important application is in the effective c...

R&D Project Blueprint - Mapping I-BiDaaS architecture to the DataBench Pipeline

The I-BiDaaS H2020 project, described in this nugget, supports Big Data as a self-service and provides a safe experimentation environment for the methodical development of new Big Data products, services, and tools. The I-BiDaaS system has been applied in a number of industrial use cases in the tel...

Securities Technology Analysis Center (STAC)

The STAC Benchmark Council consists of over 300 financial institutions and more than 50 vendor organizations whose purpose is to explore technical challenges and solutions in financial services and to develop technology benchmark standards that are useful to financial organizations. Since 2007, the...

Track & Know R&D Project

The Track & Know[1] (2018-2022) H2020 Big Data PPP project researches, develops and exploits modern software frameworks that aim to increase the efficiency of Big Data. A variety of toolboxes (that contain specific methods/functions/algorithms for various types of data aggregation, manipula...